Telematique

The Impact of Music on Problem-Solving Abilities and Logical Thinking of Students in Vietnamese Education

Nguyen Thi Ha Chau — Tue, 09 Jan 2024 00:00:00 +0000

This study aims to investigate the effects of music on the problem-solving abilities and logical thinking skills of students within the context of Vietnamese education. Music has long been recognized for its potential to enhance cognitive processes, and its impact on various aspects of human development has been widely studied. However, there is a dearth of research specifically focusing on the relationship between music and cognitive abilities in the Vietnamese education system. The study will employ a mixed-methods approach, incorporating quantitative measures and qualitative analysis. A sample of students from different grade levels in Vietnamese schools will be selected, and they will be divided into experimental and control groups. The experimental group will receive music-based interventions, involving activities such as listening to music, engaging in rhythmic exercises, and learning to play musical instruments, while the control group will not receive any specific musical interventions. The findings of this study will contribute to the existing body of knowledge on the relationship between music and cognitive abilities, particularly within the Vietnamese education system. It is anticipated that the results will provide empirical evidence supporting the integration of music-based activities in educational settings to promote problem-solving skills and logical thinking among students. The study's outcomes may also inform educational policymakers and curriculum developers in Vietnam, offering insights into the potential benefits of incorporating music into the educational framework to enhance cognitive development.

Advancing Video Transmission Flexibility: A Comprehensive Performance Analysis in Wireless Networks

H.M. Moyeenudin, Dr. K Rajeshwar Rao, Shaik Jasmine, Anup Ingle — Thu, 11 Jan 2024 00:00:00 +0000

Multimedia transmission in wireless networks poses substantial challenges due to the
inherent limitations of wireless links, marked by lower bandwidths and increased
transmission error rates, often fluctuating and sporadic. This is in sharp contrast to the more
stable conditions observed in wired networks. In response to these challenges, a
comprehensive framework is proposed to enhance the resilience of video transmission over
mobile wireless networks. This architecture integrates the Radio link layer, UDP, and error
control layer, strategically amalgamating their functionalities to alleviate the impact of
elevated error rates in wireless environments. Through the utilization of an adaptive
algorithm for video transmission, the system dynamically adapts to varying error conditions.
The efficacy of the developed algorithm is meticulously assessed through simulation results,
offering valuable insights into its performance in real-world wireless scenarios

Enhancing Data Security: Employing Finite State Machines in a Steganography Encryption Scheme

Mr. Manoj Kumar Vemula, I M V Krishna, B. Sowmya, Mrs. Mannepuli Srujana — Mon, 29 Jan 2024 00:00:00 +0000

Steganography is an intricate technique used for concealing confidential information within diverse types of media. Both Steganography and cryptography aim to protect data, with a key distinction lying in the processed output's appearance. Cryptography transforms the output into a scrambled format, rendering it challenging to decipher without the appropriate key. In contrast, Steganography ensures that the concealed data remains imperceptible, seamlessly blending into the cover medium without any observable changes. Within the domain of computer science, automata theory assumes a crucial role, exploring the study of abstract computing devices or machines. This field holds significance as it furnishes a theoretical framework for comprehending and modeling systems with finite states. Finite state systems are prevalent in computer science, and the theory of finite state systems emerges as a valuable design tool for crafting efficient and resilient systems. By applying concepts from automata theory, engineers and researchers can scrutinize, design, and implement intricate systems with finite states, thereby contributing to advancements in diverse computing applications. In this paper, we present a pioneering technique for concealing data streams through the utilization of finite state machines. Furthermore, the paper systematically analyzes and evaluates the effectiveness of the proposed method

A Systematic Review of Literature on Impact Finance: A Communication Perspective

Bashera Alrehaili — Mon, 29 Jan 2024 00:00:00 +0000

Impact financing has become an important method of financing social projects, especially for small enterprises, as they have limited access to regular financing mechanisms. Business projects with attached social benefits or sustainable elements are normally eligible for impact funding. Government policies and market dynamics also influence whether an investor opts for impact financing or traditional financing. Considering these aspects, a review of the status of research in this area was conducted. A search of Google Scholar and screening and selecting using PRISMA flow led to the final selection of 31 papers. The salient points from these 31 papers were presented and discussed. Most papers (21 out of 31) dealt with applications of impact financing in different countries and contexts. Six papers dealt with types and methods of impact financing. Only one paper dealt with mechanisms of impact financing. Three papers dealt with policy effects. The large potential for the application of impact financing in a wide range of contexts was evident from the fact that most papers (21) dealt with this aspect. These applications ranged from using impact financing to achieve MDG goals to achieving specific social benefits in areas like agriculture, energy, healthcare, conservation of natural resources, and climate change. Although mostly small entrepreneurs, who have limited access to financial resources are most benefitted by impact financing, mega projects like the Tehran Sewage project were also financed by impact funding. Very often, government policies facilitate impact financing by framing rules on tax, green certificates, and subsidies. There are also some challenges in impact financing. These include inadequate methods of outcome measurement, risk assessment, and uncertainties in financial markets. It is important for stakeholders like governments to communicate the benefits of impact financing to the investors, ensuring benefits for the larger community.

Modernizing Cryptography: High-Throughput Reseeding-Mixing PRNG in the Design and Implementation of a Cutting-Edge Algorithm

Dr. Saranya S, M Thukaram Reddy, K.Snehalatha, Kancharakuntla Shirisha — Tue, 30 Jan 2024 00:00:00 +0000

The Pseudorandom Number Generator (PRNG) plays a vital role in number sequence generation and is highly regarded for its swift production of pseudorandom numbers. Within cryptography, the Reseeding-Mixing PRNG (RM-PRNG) algorithm is extensively employed as a key generation mechanism, ensuring the secure transmission of large datasets without vulnerability to interruptions or hacking endeavors. This thesis introduces an innovative reseeding mixing method designed to extend the system period length and improve the statistical properties of the PRNG. The reseeding technique eliminates brief periods within the digitized logistic map, while the mixing method prolongs the system period length to an impressive 2253 through "XOring" with a DX generator. The resulting RM-PRNG attains a remarkable throughput rate of 6.4 Gb/s, surpassing other nonlinear PRNGs. The output sequence of the RM-PRNG serves as a key for encryption and decryption modules. The complete design is meticulously detailed in Verilog Hardware Description Language (HDL) and thoroughly verified for functionality using Xilinx ISE 9.1i software.

Maiden Application of a Structural Regression Model for Consumer Goods Demand Sales Forecasting Based on Consumer Behavior

Murari Thejovathi, Dr M.V.P. Chandra Sekhara Rao — Tue, 06 Feb 2024 00:00:00 +0000

Big data analytics has become an important new factor field of study for both researchers and professionals, demonstrating the significant demand for solutions for companies' Problems in an economy based on impulse knowledge. Today, the customer's behavior when buying consumer goods has been expressly transformed. The Big Data associated with the buyer's behavior is volatile, cumbersome, and truthful. Offering customized services to its customers through the consumer goods market is a big challenge. In this paper, we are developing a structural regression model that combines classifiers and predictors to perform sales forecasting for the future demand for an FMCG (Fast Moving Consumer Goods) company’s products or services. The novelty in this paper is designing the structural machine learning model which does not exist. The objective is to predict and forecast the sales which are the fast-moving goods for future decision-making. Here we choose a few FMCG hair care products with huge data to compare services, products, and sales based on consumer behavior of different characteristics. The proposed model considers first, the coincidence of the items purchased by the customer systematically together or one after the other. Secondly, Frequency: customer purchases at certain times of the year. Third, recurrence: sequential purchase during each period. By using the regression algorithm and moving average method, we can predict data analysis on training data sets. Our results show that variables from online reviews of customer behavior supported the demand prediction of marketing strategies. This can help manufacturers enhance ways to progress competitiveness and productivity by reducing prices and improving delivery and responsiveness to customer needs. This proposed model incorporates customer information like past web activities, reviews, comments, and tweets online.

Refining Workflow Modeling: Employing a Hybrid Approach to Boost Efficiency

Ravula Jyothsna, Apurva Khandekar, Asmita Pankaj Ambekar, Jeevitha Ravula — Fri, 09 Feb 2024 00:00:00 +0000

Structured design and formal methods are distinct methodologies within system development. Structured design emphasizes the visual representation of a system, illustrating the arrangement and flow of its components. In contrast, formal methods use mathematical techniques for rigorous analysis and verification of system properties. Both approaches offer unique advantages and applications. To harness the potential benefits of structured design and formal methods, a hybrid approach is suggested to enhance the specification of workflows in business processes. This hybrid methodology seamlessly combines the visual representation of structured design with the precision of mathematical techniques derived from formal methods. Within this hybrid framework, specific primitives are identified, each paired with mathematical rules. These primitives serve as foundational elements for workflow design, providing a structured and visually intuitive representation. The inclusion of mathematical rules establishes a formal foundation, ensuring a clear and unambiguous specification of the workflow. A significant advantage of this hybrid approach is its dual functionality. It not only facilitates the design of workflows by offering a structured and comprehensible representation but also enables the verification of the designed workflows. Leveraging formal methods allows for systematic and mathematical validation of workflow specifications, ensuring consistency, correctness, and adherence to specified rules. By incorporating both structured design and formal methods in this hybrid approach, organizations can benefit from both intuitive visualization and mathematical precision simultaneously. This streamlines the creation of intricate workflows and enhances the overall reliability and verifiability of the designed processes. The synergy between these two approaches contributes to a more robust and effective methodology for specifying and validating workflows in business processes.

An Affective Framework for Multimodal Sentiment Analysis to Navigate Emotional Terrains

Mohd Usman Khan, Faiyaz Ahamad — Tue, 13 Feb 2024 00:00:00 +0000

Affective computing, an interdisciplinary research field, unites experts from beginning of artificial intelligence, NLP, and cognitive sciences. The rise of E-content on platforms like YouTube and social media has spurred a shift in affective computing from traditional unimodal to complex multimodal methodologies. To fill the gap in literature discussions, we present a contemporary analysis focuses on multimodal affect analysis, incorporating audio, visual, and text information. Our study critically examines state-of-the-art methods. The new framework aims to establish a foundation for refined understanding. Specifically, we recommend an Interactive outline of framework which is based on Soft Mapping approach for multimodal emotion and sentiment analysis. Evaluated on MOSEI, MOSI and MELD datasets, our model demonstrates accuracy improvements. This innovative framework offers a fresh perspective on addressing the challenge of data communication in multimodal emotion/sentiment analysis, providing insights into emotional terrains within multimedia content.

Classification Algorithm to Detect the Brain Tumor's Diseases Using on Deep Learning Based Convolutional Neural Networks to Implement a Mri and CT Scan Images

Wed, 14 Feb 2024 00:00:00 +0000

The Brain Tumors is the one of the leading disease affects the humans, thus the early detection of brain tumors prevent millions of deaths. Thus, most of the researches are focusing on detection of brain tumor using machine learning based approaches. But, those approaches are failed to provide the classification accuracy. To overcome these drawbacks, in this work Adative Neuron Fuzzy Inference System (ANFIS) based Deep Learning based convolution neural networks (DLCNN) classification algorithm has been performing with the help of effective use of Grey level Co-occurrence Matrix (GLCM) features. Initially, probabilistic kernel fuzzy c means segmentation (PKFCM) based multi level segmentation operation has been performed to detection of accurate tumor region. The simulations are conducted on various datasets, the results shows that the proposed work shows the better performance compared to various conventional approaches with respect to both quantitative and qualitative evaluation.

Revolutionize Secure Smart Banking: Harnessing Central FPGA For Enhanced Distance Analysis and Security

Dr B Hari Krishna, Prashant Balkrishna Jawade, A Sarala Devi, Aleem Mohammad — Thu, 15 Feb 2024 00:00:00 +0000

Automated teller machines (ATMs) have transformed the landscape of personal and business financial transactions, offering convenient access to banking services across various locations. However, despite their widespread adoption, many ATMs remain limited by proprietary systems and lack networking capabilities. With the increasing demand for smart banking solutions, there arises a necessity for seamless integration and automated management of ATM functionalities. In addressing this challenge, this paper introduces a pioneering approach centered on a central Field Programmable Gate Array (FPGA) controller. Here FPGA controller serves as the interface for ATMs and sensors, facilitating automated monitoring and control. Through integration with a mobile phone's GSM interface, users can remotely access and manage ATM operations. This innovative methodology ensures uninterrupted availability of ATM services, empowering consumers to conduct financial transactions securely and conveniently at any time and from any location.

Motif & Symbolism of Cucuk Sanggul from Perbadanan Adat Melayu dan Warisan Negeri Selangor

Aiqa Afiqah Isnin, Arba’iyah Ab. Aziz, Mohamad Kamal Abd. Aziz — Sat, 17 Feb 2024 00:00:00 +0000

A cucuk sanggul is considered a component of jewellery, material culture, or artefact that embodies the ideas, values, concepts, and identity of a civilization. As a result, it holds significant societal significance and is considered a representation of the country's cultural heritage. Despite their long-standing presence and status as national treasures shown in Malaysian museums, obtaining extensive knowledge on jewels remains challenging due to a scarcity of reading materials. The objective of this study is to categorize motif and symbolic meaning of cucuk sanggul from the collection of Perbadanan Adat Melayu Dan Warisan Negeri Selangor (PADAT). The results of this study will provide comprehensive insights on cucuk sanggul as a reference and guide for future requirements of jewellers, designers, scholars, the general public, researchers, and other related fields, as well as the younger generation. This will be done while preserving traditional values and simultaneously promoting aesthetic appreciation. This study examines the qualities of ring shape in PADAT, specifically focusing on six cucuk sangguls. It analyses numerous factors, including theme and material, and explores their relationship to symbolic meaning. The study utilises the theory of Zakaria Ali.

Empowering Youth in High-Risk Areas: Assessing the Impact of a Psychospiritual Prevention Program on Drug Abuse Prevention

Sat, 17 Feb 2024 00:00:00 +0000

Substance or drug abuse is one of the biggest threats to the nation. One of the main factors contributing to the failure of staying abstinent is the lack of social support from the community. The models of prevention programs are mostly adopted from the Western model, which seems to be unfit for the local context. Hence, there is a significant need to explore new strategies in which the protective and spiritual elements are practiced in the community in Malaysia against drug abuse. The emerging themes for Malaysians’ protective and spiritual factors are useful for conceptualizing and developing a psychospiritual prevention module for the community in Malaysia using the Sidek Module Development Model (2005). The objective of this research is to evaluate the influence of a psychospiritual prevention module on (1) spiritual awareness, (2) locus of control, (3) knowledge, and (4) self-assertiveness. To achieve this, participants were assigned to either an experimental or control group during both the initial assessment and follow-up evaluation. This study employed a quasi-experimental design involving two distinct youth cohorts, each consisting of 33 individuals in both the experimental and control groups. The experimental group participated in a psychoeducational intervention conducted in seven sessions by trained staff, whereas the control group received no intervention. The comparative analysis between the pre- and post-tests revealed a significant increase in the mean scores for spirituality (from 172.24 to 188.12 in the post-test and 191.79 in the follow-up test), locus of control (from 68.76 to 76.79 in the post-test and 87.52 in the follow-up test), knowledge (from 28.91 to 41.48 in the post-test and 72.09 in the follow-up test), and self-assertiveness (from 99.48 to 106.42 in the post-test and 119.42 in the follow-up test) in the experimental group. In contrast, the three variables examined did not exhibit noteworthy increases within the control group. The new evidence-based module can create synergy between government agencies and the community in drug prevention.

Employee Perceptions of Occupational Health and Safety Standards (OHS) in the Oil and Gas Industry in Malaysia

Sat, 17 Feb 2024 00:00:00 +0000

purpose: The purpose of the study is to explore the relationship between information and training, workplace environment and work safety inspection with the employee perceptions of occupational health and safety standards in the oil and gas industry in Malaysia. Design/methodology/approach: The population in this study are employees in oil and gas companies in Malaysia. The method used in this research was quantitative descriptive. Data was collected using questionnaires and Partial Least Squares Structural Equation Modelling (PLS-SEM) was used as the research tool in this study because it offers a higher degree of predictive power when sample sizes are limited or model structures are complex. Findings: Results suggest that information and training have the strongest relationship with employee perception followed by workplace environment and workplace safety inspection. For the hypothesis testing, two variables which are training and information and workplace environment have a significant relationship while work safety inspection doesn’t have a significant relationship with employee perception. Research limitations/implications: This study has only focused on the employees that are under the oil and gas industry, and therefore could not be generalized to the other contexts. The time constrain also lead to low response rate. The online data collection method may have excluded workers in remote locations. The survey was entirely in English, making it challenging for non-English speakers. Practical implications: This study offers a number of important theoretical, practical and/or managerial implications. It has developed and tested the integrated model that examines how information and training, workplace environment and work safety inspection can influence the employee perceptions of occupational health and safety standards in the oil and gas industry in Malaysia. Originality/value: The research model is valid in explaining the factors that have contributed to employee perceptions of occupational health and safety standards in the oil and gas industry in Malaysia. In this light, understanding these factors will provide valuable insights on ways to enhance the operational and health standards in the Oil and Gas Industry. These would also benefit the related parties such as managers, staff, consumers, government agencies, organized labour teams and researchers.

Investigating The Entrepreneurial Self-Efficacy in the Relationship between Entrepreneurship Education and Student’s Self-Employment Intentions: An Empirical Analysis from Malaysia

Sat, 17 Feb 2024 00:00:00 +0000

The rise of self-employment is influenced by a number of personal and environmental factors among which we may distinguish the factors linked to education in entrepreneurship as well as personal characteristics. Given this aspect, the purpose of this paper is to investigate the main factors influencing students’ self-employment intentions, paying particular attention to their entre- preneurial self-efficacy. Additionally, the paper aims to explore the effect of entrepreneurial self- efficacy on the relationship between entrepreneurship education and selfemployment intention. We conducted a study where results were based on the outcomes of a survey among Malaysian public university students (N = 388). Our four main hypotheses were tested through multiple re- gression analysis. The findings highlighted that the students with an entrepreneurial self-efficacy reported a positive relationship with self-employment intention. The variables that positively influ- enced the self-employment intentions of the students were entrepreneurial self-efficacy and entre- preneurship education. Furthermore, this entrepreneurial self- efficacy positively mediates the rela- tionship between entrepreneurship education and self-employment intention. For this reason, em- phasis should be placed on both formal and informal entrepreneurial education, which will increase the propensity of young people to choose an entrepreneurial career. This is an open access article under the CC BY-SA license.

Unveiling the Transformative Influence of Instant Short Message Prowess on Literacy Skills among UiTM's Students.

Sat, 17 Feb 2024 00:00:00 +0000

This study aims to examine the influence of instant short messages on the literacy skills of students, particularly in the domains of spelling, writing, and reading. The survey encompassed the involvement of 100 final-year students from the Faculty of Education at UiTM, who were chosen using the cluster sampling technique. To gather data for all three-study inquiries, a questionnaire was utilized, and subsequent analysis was carried out utilizing linear regression. The results showed that instant short messages have had a positive impact on students' spelling skills, leading to more accurate word usage, a better grasp of spelling structures and variations, and overall improved spelling proficiency. Additionally, the study concludes that instant short messages contribute positively to students' writing skills by facilitating the daily incorporation of new phrases and encouraging the selection of appropriate words for their written expressions. Finally, the influence extends to students' reading skills, with instant short messages aiding in fluent reading, enhancing punctuation comprehension, facilitating personalized interpretations of academic subjects, and simplifying information retrieval.

A Study on The Preferred Sensory Modalities for Learning among Children with Autism Spectrum Disorder (ASD)

Sat, 17 Feb 2024 00:00:00 +0000

Quality education is one of the Sustainable Development Goals (SDG) declared by the United Nations in September 2015. Criterion number four emphasises ensuring inclusive and equitable quality education and encouraging opportunities for lifelong learning. To ensure that children with exceptional needs, such as Autism Spectrum Disorder (ASD), enjoy equitable educational opportunities in academics and personality, "Parallel Education" was developed. Regardless of their origin or circumstances, all children should have access to high-quality education, according to the Sustainable Development Goals (SDGs). Autism is a complex developmental condition that affects people of all racial, cultural, and social backgrounds, regardless of their socioeconomic standing, way of life, or degree of education. To improve their brain growth and create teaching and learning materials, the criteria must be determined. It is crucially important and demands the specific attention of every designer for all the instructors to approach the needs of autistic children. Besides, conduct, social skills, and communication abilities are three crucial areas on which autism spectrum disorder has a considerable effect. This study primarily serves as a reference for the educational tools designers who seek to improve the emotional state of autistic children through the cognitive and affective domains, while also fulfilling their sensory modalities and helping their psychomotor development. The study also identified unique challenges and needs for autistic children who have sensory issues by focusing on an emotional evaluation.

The Relationship Between Demographic Factors and Learning Approaches with the Academic Achievement of Malaysian Vocational Certificate Students

Lili Sunaini Ramli, Prof. Dr. Zamri Mahamod, Dr. Mohd Azlan Mis — Sat, 17 Feb 2024 00:00:00 +0000

Introduction: Student academic excellence can be linked and influenced by various factors that can be internal and external. Excellent academic achievement not only reflects success in formal education, but also has a positive impact on various aspects of an individual's life and future. Objective: This study aims to compare the level of Malay language achievement of Malaysian Vocational Certificate (MVC) students based on demographic factors (gender and field of study). This study also exameanes the learning approach (surface, deep and motivational) and its relationship with Malay language achievement of MVC students. Methodology: In this study, a total of 297 Year 1 students of the MVC were selected through moderate random sampling to represent the population that is 1334 students as research respondents who are studying at a vocational college in the state of Pahang, Malaysia. Data was analyzed using software Statistical Package for the Social Science (SPSS) version 26.0. Namely descriptive statistics (mean, frequency and percentage) and inferential statistics (Test Mann-Whitney U, Kruskal Wallis and Post Hoc Test and Correlation). The degree of reliability (Cronbach Alpha) for the entire learning approach is .0.81. Findings: The results of the study show that there is a significant difference between demographic factors (gender and field of study) and student achievement. The mean analysis shows that the level of deep approach and motivational approach is at a high level while the surface approach is at a moderate level. Spearman Rho correlation analysis found that there is a significant relationship between the learning approaches studied and student achievement. This study has an impact on the college management in designing an academic excellence program related to academic achievement among MVC students. Summary: The role of demographics and an effective learning approach plays an important role in improving students' academic achievement. Educators and school admeanistrators can take the right actions to support the learning and development of each individual student while ensuring that no one is left behind in the pursuit of academic excellence as well as foster intrinsic motivation.

Aesthetic Value in Underwater Photography

Tue, 20 Feb 2024 00:00:00 +0000

Underwater photography is a unique and challenging artistic expression that requires a deep understanding of aesthetic value. By examining the sources provided, including the perspectives of philosophers, photographers and visual artists, as well as scholarly research in literary and cultural criticism, this study aims to explore the complexities of aesthetic value in underwater photography. This study focuses on how artistic value can improve image quality in underwater photography in a discursive manner. Underwater photography requires photographers to apply the basic principles of art and utilise various elements to overcome the challenges of the unique underwater environment. The optical properties of water, such as its limited transparency and the need for artificial lighting, present a challenge to underwater photography. When photographers understand and apply the values of art, they can capture fascinating images that show the beauty and diversity of the underwater world.

Application of Machine Learning Algorithm for Optimal Model Design for Opinion Extraction

Devendra Kumar, Faiyaz Ahamad — Thu, 22 Feb 2024 00:00:00 +0000

Evolution of technology introduced huge amount of web data of users of internet that not only use the available web resources, but also give feedback, that generates helpful information. The large size of user data in terms of opinion, feedback, view & suggestions is accessible on web resources, it is normal trend to explore, organize & analyze the reviews for opting right decision. Analysis of sentiment or opinion extraction is an Information Extraction and Natural Language Processing task that helps in identification the user opinions and reviews in form of positive, neutral or negative quotes &comments associated in the text. Various data-driven techniques to opinion extraction are available that applies the sentiment classification. This study explores the efficient classification techniques utilizing KNN using text extraction for a sentiment dataset and performance evaluated in terms of accuracy. Using data from the tweeter dataset, the study evaluates the performance KNN models.

A Deep Learning Framework for Recognition and Classification of Diabetic Retinopathy Severity

Fri, 23 Feb 2024 00:00:00 +0000

Diabetes is an infection that improves when the human body cannot properly utilize insulin or the pancreas fails to release enough. When diabetes worsens, the condition increasingly affects the cardiovascular system. If DR is not diagnosed at the earliest possible stage, it can result in partial or complete vision loss. Retinal lesions related to the infection are used to determine the several stages and the severity of the condition. Retinal images taken using a fundus camera with a motorized camera on the rear provide useful details of the nature, outcome, and stage of effects on the eye. These kinds of pictures aid ophthalmologists in evaluating patients to plan various administration strategies and make progress monitoring more effective. If DR is not treated, it could eventually become the primary cause of vision loss and visual impairment. Those with diabetes have a rise in blood glucose levels and retinal damage. Blindness or vision loss results from this. A wide range of conditions, including cataracts, glaucoma, DR, and age-related macular degeneration, are major causes of blindness and visual impairment in people. DR is the colloquial name for the blinding diabetic eye condition. As a result, the blood cells in the retina are injured, which could cause blindness. Retinopathy is a disease that damages the blood vessels that supply the retina. This disorder can cause distorted vision, eye hemorrhage, and total vision loss. This disease impairs the visual nerve by obstructing blood flow.

Unfolding the Epoch of Large Language Models: A Comprehensive Exploration

Faisal Almunimi, Prof. Tareq Ahmad, Dr. Muhammad Binsawad — Wed, 28 Feb 2024 00:00:00 +0000

Natural Language Processing (NLP) has undergone a remarkable transformation since the introduction of Large Language Models (LLMs). The extensive scope and sophisticated architectures of these models have redefined the limits of machine comprehension and generation of human language. This paper provides a comprehensive review of the evolution, architecture, and applications of LLMs, tracing their development from simple statistical models to advanced Transformer-based architectures such as GPT-4. We strive to provide a comprehensive understanding of the current state of LLMs through a thorough examination of historical context and significant milestones. In addition, we conduct a comprehensive analysis of the architectural principles regulating these models and investigate a multidimensional taxonomy encompassing the various aspects of LLMs. To cast light on the broader implications of this technology, the transformative impact of LLMs across a variety of sectors is examined. Finally, we look to the future and discuss potential directions for future advancements in this field, emphasising the need for interdisciplinary approaches and automated classification. This paper aims to promote a comprehensive understanding of LLMs, thereby contributing to the scholarly discourse and assisting practitioners and policymakers in navigating the rapidly evolving landscape of NLP and AI.

Usability of Online Government Services In Saudi Arabia

Alia Mohammed AlSulaimi — Wed, 28 Feb 2024 00:00:00 +0000

Over the past year, every aspect of our lives has been impacted by Covid-19. This has increased people's reliance on technology in accessing vital services such as healthcare. Another sphere wherein an increased reliance on technology is found is that of government services and programmes. There has been a push to enhance the technological capabilities of government platforms to address issues of usability for people. The aim of this study is to better understand the usability of the Saudi government's online services and programmes. Attention will be paid to the perception of patients towards such telehealth services. Data was collected from patients who had used telehealth and through a major hospital in Mecca, Saudi Arabia. The data was analysed using suitable statistical techniques. Low levels of perception about telehealth tools and their usefulness led to a poor perception of the future scope of using telehealth beyond the current COVID period. High educational levels and low levels of awareness and usefulness perceived on telehealth by the patients also led to predictive models of low levels of future scope. The results were explained based on traditional Islamic culture standing as a barrier to improvement of education for critical thinking and too much emphasis on religious and Arabic lessons preventing adoption or adaptation of technology from the Western world. The Saudi government needs to take steps to improve awareness of telehealth and its education system towards acceptance of modern technology for continued use of telehealth tools in future. There are lessons to be learnt from this study for the government’s other services and programmes as well. This is especially important as the government is trying to implement self-management systems for chronic patients and the need to achieve the goals of its Vision 2030 currently under implementation.

Implementation of Design and Development Research for Pantun Studies in Malay Language Education

Ahmad Fikri Ab Rahman, Zamri Mahamod — Tue, 05 Mar 2024 00:00:00 +0000

Long before the fifteenth century, an oral form of poetry known as pantun was in existence. Previous researchers have conducted several studies on pantun, exploring various aspects such as semantics, morphology, syntax, and other related areas. However, the extent of pantun studies within education remains constrained. Researchers frequently employ qualitative methods for studying pantun, as evidenced by the existing literature. This concept paper aims to discuss the implementation of design and development research approach in the context of researching pantun in education. The objective of this approach is to meticulously design and develop novel products through the use of diverse data collection methodologies, while also incorporating the participation of experts as study participants. Consequently, the production of novel products, such as the development of models, modules, guidelines, frameworks, tools, and innovative products, can serve as a valuable contribution towards enhancing the quality of pantun in education.

Creativity in Teaching and Learning Using Talentlms Platform in Gamification and Blended Learning Environment to Increase Students’ Engagement Level

Azlinda Shafie — Tue, 05 Mar 2024 00:00:00 +0000

The purpose of this paper is to investigate how much Station Rotation gamification is being used. To bridge the gap of unstructured gamification observed in many prior gamification approaches by numerous researchers, an inventive strategy to teaching and learning strategies has been proposed by researchers. To complete the work, the observation was given to the 36 students quantitatively using an experimental design approach on the gamification website Talentlms.com. The course consists of ten weeks of instruction, with networking and databases being the two most challenging subjects. Ten students took part in the study's semi-structured interview to provide qualitative data as well. The results of the interview are divided into themes and subthemes. Depending on the topic matter and the student, there were differences in the level of involvement while employing the Station Rotation Gamification(SRG) for learning. This was especially evident in the first three weeks of the intervention. Students' involvement increased thanks to the gamified platform, albeit it varied depending on the subject. In summary, SRG provides a workable way to reduce engagement issues, even though it might not always solve the problem.

Power Quality Management Using Heuristic Algorithms for Non-Linear Loads in Hybrid Power Systems

Lavanya Sanagari, Dr. Rajendran Arunmozhi — Sun, 10 Mar 2024 00:00:00 +0000

Optimal power flow (OPF) analyses with many competing objectives are becoming more difficult as power networks integrate renewable energy sources and devices for storage. In a remarkably short amount of time, this study proposes optimising the use of various power system entities through the use of heuristic algorithms when combined with power quality correction. When a power quality problem develops in the grid, new algorithms are suggested because of how fast they respond. In order to get the most out of the system, the MPPT method has been proposed. Furthermore, the stability of the hybrid system was improved in this study.Our current work includes the addition of a fact device referred to as DSTATCOM, which is optimized with the grass hopper algorithm (GOA). MC-UPQC is a new power quality conditioning system for multibus/multifeeder systems capable of simultaneous voltage and current compensation. Several series voltage source converters are used in this configuration, as well as one shunt voltage source converter (shunt VSC). DC-link capacitors are shared among all converters in the proposed configuration. In this way, sag/swell and interruptions can be compensated by transferring power between feeders. Simulations are used to illustrate the performance of the MC-UPQC and the control algorithm named advanced wolf pack algorithm (AWPA). With multiple optimal techniques and fact devices a comparison made for the enhancement of power quality management without disturbance in hybrid Power systems.

Blockchain Application in Certificate and Qualification Management to Make Effective Student Data Storage

Huynh Kim Cuong — Thu, 14 Mar 2024 00:00:00 +0000

In the technology sector, Blockchain is being widely applied in many areas, from finance to healthcare and supply chain management.Its main features are decentralization and high data security, which makes the storage and transmission of information more reliable.In the blockchain environment, each block of data is linked to the previous block through a hash string, forming a block string that cannot be easily changed. This ensures the immutability of the data: once the information has been written into the blockchain, it cannot be modified or deleted without changing the entire chain. By using consensus algorithms such as Proof of Work or Proof Of Stake, the blockchain network can consensuses between nodes automatically and reliably. This eliminates the need to rely on a third party to validate transactions, minimizes risks and costs, and the blockchain also opens the door to more sophisticated applications like smart contracts, acting as programs that automatically enforce contract terms when conditions are met. This can be applied in many areas such as insurance, real estate and also in the area of personal data management. Blockchain is not only a data storage technology, it also opens the door to innovation and utility in many fields, helping to create a reliable and secure digital infrastructure.

Exploring the Impact of the Japanese Language and its Influence on Higher Education Development

Mai Huynh Thanh Nhi — Thu, 14 Mar 2024 00:00:00 +0000

The Japanese language holds a significant place in the landscape of global education, particularly in the realm of higher education. This abstract aims to provide a comprehensive overview of the research study investigating the influence of the Japanese language on the development of higher education institutions. The study delves into various dimensions of this influence, examining both historical contexts and contemporary trends. Firstly, it explores the historical roots of Japanese language education, tracing its evolution from traditional practices to modern pedagogical approaches. This historical analysis elucidates the cultural significance attached to the Japanese language and its role in shaping educational paradigms. The research investigates the integration of Japanese language programs within university curricula worldwide. By examining case studies and survey data, the study assesses the motivations behind the inclusion of Japanese language courses and the impact on student enrollment and academic outcomes. Additionally, it explores the pedagogical methodologies employed in teaching Japanese at the university level, highlighting innovative approaches and best practices. The study examines the broader implications of Japanese language proficiency for students' academic and professional trajectories. It investigates the correlation between proficiency levels, cultural competency, and career opportunities, drawing insights from interviews with educators and employers.

Comprehensive Evaluation of Clustering-based Defensive Technique against Label Modification Attack

Dr. Jyoti Yadav, Nilambari S. Mate, Dr. A. D. Shaligram — Wed, 20 Mar 2024 00:00:00 +0000

As Machine Learning (ML) models are widely used to make security decisions, attackers are highly motivated to tamper with the models and outputs produced by machine learning algorithms. An attack where training data is manipulated to compromise the performance of ML model is known as data poisoning attack. As a result of data poisoning attack, false positive rate (FPR) of the model increases and accuracy of the model decreases. The effect of data poisoning attacks can be detected and prevented using various methods. This paper evaluates the performance of ML model by modifying the labels of the dataset used to build Support Vector Machine (SVM) classifier. Clustering based data filtering method is used to detect poisonous samples from training data. Clustering method is evaluated by using Euclidean distance measure and Jaccard distance measure on MNIST and WINE datasets. Results show the Jaccard distance measure is more accurate and consistent than the Euclidean distance measure on MNIST dataset using the clustering based filtering technique. However, same defensive technique is not useful for WINE dataset

Predictive Analysis and Performance Evaluation Using in Deep Learningbased Air Quality Monitoring System

Thu, 21 Mar 2024 00:00:00 +0000

Air quality monitoring is vital for safeguarding the environment and public health. It plays a critical role in environmental conservation and public health management. This research explores the potential of deep learning models to enhance air quality prediction accuracy and offer valuable insights into pollution trends. We explore various deep learning methods, including Convolution Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Auto-encoders, Generative Adversarial Networks (GANs), and Transformer-based models on Delhi Air Quality Data Set. To evaluate the models' performance based on standard metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy. We find that Convolution Neural Networks excel in predictive accuracy and image-based air quality assessment.

Detection of Android Malware by Using Classification Algorithms

Tue, 26 Mar 2024 00:00:00 +0000

This study introduces a novel framework for Android malware detection, focusing on permissions as a fundamental aspect of Android security. Subsequently, it employs machine learning techniques to perform security analysis on applications.Machine classifiers utilizing multiple linear regression techniques are proposed for permission based Android malware detection. These classifiers are subjected to comparison against fundamental machine learning algorithms, including MLPClassifier, LinearDiscriminantAnalysis, RandomForestClassifier and LinearRegression are used for detecting android malware files. Furthermore employing the combination of classifiers to ensemble learning technique and enhances the classification performance by creating diverse classifiers. The study demonstrates remarkable performance using classification algorithms grounded using MLPClassifier, LinearDiscriminantAnalysis, RandomForestClassifier and LinearRegression models for obviating the necessity for overly existing techniques to show moure accuracy.

Estimation of Electric Vehicle Parameters Using Web Portal

Dr. Kishor Kinage — Sat, 30 Mar 2024 00:00:00 +0000

The transition towards electric vehicles (EVs) necessitates accurate estimation of various parameters crucial for understanding EV performance and optimizing user experience. This abstract presents the design and implementation of a web portal aimed at estimating key parameters of electric vehicles. The portal serves as a user-friendly platform for individuals and organizations to gain insights into EV characteristics without the need for specialized software or expertise. The portal facilitates the estimation of essential EV parameters such as battery capacity, range, charging time, and efficiency. Leveraging mathematical models, empirical data, and possibly machine learning algorithms, the portal offers users the ability to input relevant variables such as vehicle specifications, driving conditions, and charging infrastructure details. The backend of the portal is developed to handle complex calculations and data processing, ensuring accurate estimation results. Integration with databases may enable the storage of user inputs, historical data, and model updates for continuous improvement. On the frontend, the web portal features an intuitive and interactive user interface designed with HTML, CSS, and JavaScript. The interface guides users through the input process and presents estimation results in a clear and comprehensible manner. Visualization tools may be incorporated to enhance user understanding of the estimated parameters.

The Influence of Economic Literacy and Student Attitude on the Self-Concept Achievement of Business Students

Tue, 02 Apr 2024 00:00:00 +0000

this study aims to identify the factors contributing to the self-concept achievement of business students. A survey questionnaire was employed as a research instrument to obtain quantitative data, involving a total of 402 respondents selected through random sampling. The data were analyzed using SPSS version 22.0 and Analysis of Moment Structures (AMOS). Descriptive and inferential analyses were conducted through parametric statistical tests, including t-tests and AMOS analysis. The t-test results showed no significant differences based on gender and location. AMOS analysis revealed that the level of economic literacy has a positive relationship with the self-concept of business students. The results also indicate that student attitude (SA) is at a high to moderate level and has a positive association with the self-concept of business students. Additionally, the analysis of the self-concept of business students (SCBS) also shows a high to moderate level. There is a significant correlation between economic literacy (EL) and student attitude (SA) toward achieving the self-concept of business students. The results also show a significant influence of the predictive factors, economic literacy (EL), and student attitude (SA), on the achievement of the self-concept of business students (SCBS). In conclusion, the researcher suggests emphasizing the self-concept of business students (SCBS), especially at the level of business subjects, to enhance achievement and academic performance, and facilitate students' transition to tertiary institutions at the university or college level. The factors EL and SA can provide advantages for students ready to pursue jobs requiring business-mindedness, which relates to their self-concept. By implementing these strategies, educators can support the personal growth and development of business students, equipping them with the skills, knowledge, and mindset needed to thrive in their academic and professional pursuits.

Smart Flying Bird Integrated with Iot & GPS

Priti P Somwanshi, Anjali C Pise, Ganesh B. Birajdar — Sat, 06 Apr 2024 00:00:00 +0000

Over the past few years, research into aircraft with flapping mechanisms (also known as ornithopters or robot birds) has increased interest in their use in defense surveillance. These little spies gives realistic view to birds, making them difficult to distinguish between artificial and natural birds, and have never been seen flying before. This new interest in flying robotic birds has led to new challenges in IoT-based communication and control that need to be investigated in the field. In order to get a better surveillance integrated with IoT and GPS, this project aims to design a prototype ornithopter called Smart Flying Bird Integrated with IoT and GPS. This smart bird will be capable of carrying a heavy as much as 800 to 1000 gm of computer and electronic controller circuitry. This will be designed specially, for the spy project and live location detection in defense. The camera will be mounted on the front of the smart bird and can be rotated to view a wide area. The camera will also have a zoom lens, which allows the smart bird to zoom in on objects of interest. The smart bird will also be equipped with a GPS receiver, which allows it to determine its location and send it to ground station with the help of IoT. The GPS receiver will send the bird's location back to a ground station, which can be used to track the bird's movements. The smart bird is a promising new technology for Wild life & Forest crime control, Search & rescue operations during natural calamities, Security services, etc. It will be so designed to surveillance be lightweight, aerodynamic, easy to fly and difficult to detect by Radar

Hardware Synchronization for Embedded Multi-Core Processors

Tejasvini Bansode, Anjali Pise — Sat, 06 Apr 2024 00:00:00 +0000

it is not the question of whether they are coming but how the architectures of the microcontrollers should look with respect to the strict requirements in the field. We present the step from one to multiple cores in this paper, establishing coherence and consistency for different types of shared memory by hardware means. Also support for point-to-point synchronization between the processor cores is realized implementing different hardware barriers. The practical examinations focus on the logical first step from single- to dual-core systems, using an FPGA-development board with two hard PowerPC processor cores. Bestand worst-case results, together with intensive benchmarking of all synchronization primitives implemented, show the expected superiority of the hardware solutions. It is also shown that dual-ported memory outperforms single-ported memory if the multiple cores use inherent parallelism by locking shared memory more intelligently using an address-sensitive method

An Examination of AI-Powered Medical Tools from the Perspective of the Patient

Suman Mehta, Dr. Amar Nath Chatterjee — Mon, 08 Apr 2024 00:00:00 +0000

Artificial intelligence is a popular buzzword that has two sides while some view it negatively or as destructive, others believe it to be extremely beneficial. Furthermore, because people don't trust a machine to operate on their body, the use of AI in the healthcare sector has long been a contentious issue. Since the healthcare business is so particular, we construct a model generally founded on esteem insights. The motivation behind this study is to examine client impression of the benefits and inconveniences of AI-powered medical gadgets that incorporate clinical choice help (Compact discs) abilities. We get data from 200 individuals in India utilizing a web-based review. The proposed approach pinpoints the wellsprings of patient tension and inspiration for AI-based gadget improvement. The discoveries show that the apparent dangers related with conveying AI frameworks in healthcare are generally impacted by mechanical, moral (trust factors), and administrative contemplations. The performance and communication features of technology are found to be the most important determinants of risk beliefs among the three groups.

The Role of Intellectual Property Rights in Facilitating Global Trade in Developing Countries

Mon, 08 Apr 2024 00:00:00 +0000

This article provides an overview of the role of intellectual property rights in facilitating global trade in developing countries. The findings indicate that intellectual property ownership and its role in international trade have become crucial and vital in the current era of the legal-economic structure of countries. Intellectual property laws are dynamic and constantly evolving in line with global societal conditions, which enhances the dynamism of international trade. Nowadays, the competitive environment compels organizations to change their management approaches. Organizations are encouraged to utilize organizational resources more efficiently while focusing on innovation to gain a competitive advantage. On the other hand, organizations and companies with a competitive advantage require the management of intellectual property to preserve and protect their innovative achievements, as this facilitates their connection with international trade.

Research the Benefits of Applying Software to Evaluate Student Satisfaction with Lecturers' Teaching Activities

Nguyen Tan Phat — Mon, 08 Apr 2024 00:00:00 +0000

In the context of higher education, ensuring effective teaching practices and maintaining high levels of student satisfaction are key objectives. This study synthesizes findings from two distinct research approaches: one focusing on the benefits of using a software-based assessment system and the other investigating student satisfaction with different aspects of lecturers' teaching activities. The synthesis of these methods provides a comprehensive understanding of student perspectives on teaching effectiveness. Research shows that students express satisfaction with lecturers' teaching activities, with the highest level of satisfaction observed with the lecturer's quality and competence. The use of software-based assessment systems has emerged as a promising approach to assessing student satisfaction, offering many benefits including improved efficiency, scalability, and accessibility. Consistency in data collection and analysis. Software-based assessment allows for timely and comprehensive assessment, facilitating adjustment and improvement of teaching strategies. The anonymity provided by digital platforms encourages candid feedback, leading to more accurate assessments. Furthermore, the software empowers educators and administrators with analytical tools to identify trends, patterns, and areas for improvement, thereby facilitating targeted interventions. Aims to improve the quality of teaching and optimize the learning experience for students. This study highlights the transformative potential of software-based assessment in measuring student satisfaction with faculty members' teaching practices, contributing significantly to the continuous improvement of quality and effectiveness. Educational outcomes in higher education environments.

Solutions to Improve the Efficiency of Using Online Learning Tools when Working with a Partner Compared to Working Independently

Huynh Kim Cuong — Mon, 08 Apr 2024 00:00:00 +0000

Online learning gives students more time to study on their own at home. Therefore, the students' need in each lesson will be to absorb the foundational knowledge contained in textbooks, workbooks and part of the expanded content contained in the exam questions. Indirect interaction between teachers and students through smart devices will limit the lecturer's ability to analyze and expand on the problem. Sometimes, there are students in class who cannot keep up with certain content. Therefore, teachers should also spend more time conducting surveys on lesson content and supplementing basic knowledge if students do not understand in order to improve the quality of online teaching. And interaction between students also plays a big role, which will help students have the best possible experience.

3D-CLSTM-GAN: Three-Dimensional Conventional Long Short-Term Memory Generative Adversarial Network for Anomaly Detection in Video Surveillance

Ms. C. Swapna, Dr.B.Padmaja Rani — Sat, 13 Apr 2024 00:00:00 +0000

Anomaly detection in videos is a challenging task due to unbounded nature of anomalies in different contexts. Supervised learning is not ideal for such computer vision applications due uncertainty and abnormality definition. Generative Adversarial Network (GAN) emerged as a class of neural networks with unsupervised learning. In this paper, we proposed a GAN architecture named 3D-CLSTM-GAN for real time detection of anomalies in surveillance videos. We used an 3D convolutions and Conventional Long Short Term Memory (ConvLSTM) in the generator for efficient learning of spatio-temporal features. We used an encoder based discriminator which discriminates generated video frames. We used a loss function that promotes robustness in anomaly detection due to its ability for quick convergence of the underlying model. After learning from normal data distribution, the proposed architecture detects anomalies based reconstruction error which reflects deviation of frames representation. The proposed 3D-CLSTM-GAN is lightweight due to the 3D ConLSTM which exploits 3D convolutions efficiently. The 3D-CLSTM-GAN architecture is evaluated with empirical study using three benchmark datasets such as ShanghaiTech, CHUK Avenue and UCSD. Experimental results revealed that the proposed 3D-CLSTM-GAN outperforms the state of the art models such as PPC+SFA, Conv-AE, ConvLSTM-AE and TSC.

Automatic Tune Designer System Using Deep Learning

Mrs. Prasanna Pabba, S Krishna Kiran, S Sarthan, S V Sai Srikar, I Sai Prakash — Thu, 18 Apr 2024 00:00:00 +0000

The development of methods for automated and audio generation has been a critical area of research in audio signal processing. Existing techniques often struggle to fully capture the main theme of music, highlighting the need for more sophisticated approaches. In response to these limitations, this study introduces a Conditional Variational Autoencoder (CVAE) model, utilizing the advanced capabilities of the Python libraries This CVAE model, developed within a TensorFlow and Keras framework, incorporates convolutional layers and ResNet1D blocks to effectively encode and decode audio signals. It aims to understand and recreate the complex patterns found in music accurately. By proposing this model, we address previous shortcomings in automated music generation, offering a novel solution that significantly enhances the quality and coherence of generated audio. The model's effectiveness is quantified using the Evidence Lower Bound (ELBO) metric, ensuring high-quality audio output and marking a substantial progress in the quest for automated music generation.

A SVM Based System Model for Personalized Medication Management

M .Lakshmi Prasad, M. Jashwanth Reddy, V.Snehitha, M.Shreya — Sat, 20 Apr 2024 00:00:00 +0000

The proposed the design of an innovative personalized medication management system named Medicatex was modelled, with a primary focus on delivering tailored information to consumers, who are the central users of this proposed solution. This paper highlights the system's distinctive features and its capacity to provide personalized insights into various facets of the medications individuals take and their overall health conditions. The intention is to enhance consumers' awareness regarding the positive benefits of adhering to their medication regimens, while also elucidating the potential consequences associated with specific medications. By equipping consumers with this personalized information, they gain insight into the characteristics of various medications and discover diverse approaches to enhance their overall health. This paper presents a systematic approach to consolidating essential medication information aimed at enhancing patient understanding and safety. The proposed framework centralizes on five fundamental modules. The "Uses" module enumerates the indications for the drug, providing comprehensive insights into its therapeutic benefits and potential off-label uses. In the "Dosage" module, precise dosing regimens are detailed, including variations for different populations and adjustments needed for specific clinical scenarios. "Precautions" are meticulously outlined to inform healthcare providers and patients of necessary measures to mitigate risks, including drug interactions and use in special populations such as pregnant or breastfeeding women. The "Side Effects" module catalogs the spectrum of possible adverse effects, stratified by severity and frequency, and offers guidance on management strategies. Collectively, these modules aim to empower patients with knowledge, promote medication adherence, and minimize the incidence of medication errors, ultimately contributing to improved healthcare outcomes.The "News" module tells the recent news on the disease .The "Educational Tips" provides the information about the diet.

Optimizing Heart Disease Risk Assessment Through Innovative Model Ensemble Techniques

M. Lakshmi Prasad, Pasupula Satwik, P Sai Gowtham Reddy, E Rama Siva Sai Chary — Mon, 22 Apr 2024 00:00:00 +0000

Heart disease remains an increasing world health problem, contributing to a significant share of morbidity and worldwide. Early detection and correct prediction of heart disease are needed for good treatment and preventative plans. In recent time, machine learning approaches, particularly ensemble learning, have demonstrated promise for increasing the accuracy of cardiac disease prediction models. This study investigates the usefulness of ensemble learning algorithms, such as Random Forest, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM), in predicting heart disease using clinical data. The study used a large dataset of 70000 patients that included demographic information, clinical assessments, and cardiovascular risk factors. Data pretreatment techniques are used to deal with missing values, outliers, and feature engineering. Ensemble learning models are trained.The findings show that ensemble learning is more successful in improving the prediction performance of heart disease models than individual algorithms. The Random Forest, MLP, and SVM models all perform well, and ensemble approaches improve accuracy and resilience even more. These findings demonstrate the potential of ensemble learning approaches to assist healthcare practitioners in early diagnosis and risk classification of heart disease patients. This discovery has implications for clinical practice, as precise prediction models may help with personalized treatment planning and resource allocation. Future research paths may include investigating different ensemble techniques, including domain knowledge, and integrating real-time data sources to enable continuous model development.

Maxillo Facial Fracture Detection System (MFDS) in Accident Victims with Deep Learning Techniques using Artificial Intelligence

Wed, 24 Apr 2024 00:00:00 +0000

It is proposed that a novel Maxillo Facial Fracture Detection System (MFDS) be developed to identify traumatic fractures in patients, using the use of deep learning and transfer techniques. To classify future Computed Tomography (CT) scans as "fracture" or "no Fracture," we re-trained a Conventional Neural Network on CT scans after it had been initially trained on non-medical images. A total of 358 CT scans were used for training the model (150) patients were annotated as having a fracture, and 78 were annotated as having no fracture). Five patients were classified as having "no Fracture," and the remaining 38 were classified as having a fracture; these 50 patients made up the statistical analysis validation data set. The whole dateset used for validation included 60 CT images, 38 of which had "fracture" labels and 10 had no such labels, and both sets were used. Both a focus on individual slices and on grouped slices for patients was used in the tests. Patients were considered to have fractures if two successive slices had a fracture probability of more than 0.99. In terms of classifying Maxillo Facial fractures, the model has an 99% accuracy rate, as evidenced by the patients' outcomes. The MFDS model may not be able to fully take over the Radiologist's duties, but it can certainly help by lowering the likelihood of mistakes, protecting patients from harm by shortening the time it takes to make a diagnosis, and alleviating the disproportionate load of staying in the hospital.

Empowering Women Entrepreneurs: Catalysts for Sustainable Business Growth

Thu, 25 Apr 2024 00:00:00 +0000

This comprehensive article explores the dynamic relationship between women entrepreneurs and sustainable growth within the global business landscape. It delves into the challenges, opportunities, and the pivotal role of women-led enterprises in driving socio-economic progress while embracing sustainable practices. Drawing upon research, case studies, and expert insights, the article navigates through the journey of women entrepreneurs, identifies key findings, offers actionable recommendations, and concludes by emphasizing the transformative potential of empowering women in entrepreneurship for fostering sustainable development

Hand gesture identification system for hearing and speech impairment

Ms. Asma Akil Hakim, Prof. A. D. Harale, Dr.K.J.Karande — Sat, 27 Apr 2024 00:00:00 +0000

Lack of speech is taken into account to be a true incapacity. Individuals with these disabilities use a range of ways to speak with others, there are several forms accessible for his or her communication, one in every of the foremost common varieties of communication further as language. Language is employed by the deaf and also the non-listening share data with their community etc. Electronic recognition of language deals from sign language to the touch and continues till text / speaking. Bit is classified as permanent and versatile. But finding an immediate bit is less complicated than seeing a dynamic bit however each awareness systems are vital in human society. Steps in recognizing language are delineating during this study. Knowledge acquisition, pre-processing and modification of knowledge, feature extraction, segmentation and obtained results are assessed. Alternative pointers for future analysis within the space also are advised.