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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Khatkar, Monika Vigenesh, M. Kumar Verma, Sanjeev Premananthan, G. Sharma, Sachin mathurkar, Piyush K. R. Mangalam University Department of Computer Science & Engineering Haryana Gurugram122103 India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India School of Engineering and Computer Science Dev Bhoomi Uttarakhand University Uttarakhand 248007 India E&TC Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350304480

Huge networking of items (IoT) with 5G is an exciting future innovation with many benefits. It poses some difficulties because many devices require a strong connection. In this situation, picture-input photo-output (MIMO) devices provide great ability to communicate and volume. With MIMO, there are a problem with antenna coherence. In order to lessen association, this article suggests a photo-sector MIMO home location antenna using 5G-New Media (5G-NR) spectrum N77 using a dual polarization structure. The suggested antenna exhibits an association of less than 0.01 with an aggregate connection of -16.90 dB. Its BER, erroneous bit picture (BER) rate, efficiency attained is remarkably similar to that of picture-correlated antennae having a 1.87 GHz range. It indicates how the recommended antenna had been thoughtfully created. © 2023 IEEE.

关键词： 5G Antenna picture illustration

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Prognostic Kalman Filter Based Bayesian Learning Model for Data Accuracy Prediction

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computers, Materials & Continua 2022年第7期72卷 243-259页

作者： S.Karthik Robin Singh Bhadoria Jeong Gon Lee Arun Kumar Sivaraman Sovan Samanta A.Balasundaram Brijesh Kumar Chaurasia S.Ashokkumar Department of ECE College of Engineering and TechnologySRM Institute of Science and TechnologyVadapalaniChennai600026India Department of CSE Birla Institute of Applied Sciences(BIAS)BhimtalUttarakhand263136India Division of Applied Mathematics Wonkwang University460Iksan-DaeroIksan-SiJeonbuk54538Korea School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India Department of Mathematics Tamralipta MahavidyalayaWest Bengal721636India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India Indian Institute of Information Technology(IIIT) LucknowUttar Pradesh226002India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India

Data is always a crucial issue of concern especially during its prediction and computation in digital *** paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data *** also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the *** paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and *** method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from *** proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown *** scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic *** also demonstrates the generative function forKalman-filer based prediction model and its *** paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.

关键词： Bayesian learning model kalman filter machine learning data accuracy prediction

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A Critical Analysis on Cybercrimes 2

A Critical Analysis on Cybercrimes

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Hussain, Sadaf Ashraf, Saher Seep Al Hamadi, Hussam Ul Abideen, Zain Institute of Management Science Department of Computer Science Lahore Pakistan IT University of Dubai College of Engineering 14143 United Arab Emirates NCBA&E Lahore School of Computer Secince Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

Network of computers and associated equipment that spans the globe is commonly known as cyberspace. Countries have grown more dependent on this interconnected system, which has permeated almost every aspect of daily life. Social media has emerged as a key factor in cybercrime, alongside other activities such as online shopping and banking. In today's world, cyber-attacks can have significant strategic consequences by giving attackers control of networks and access to sensitive information. The recent incidents involving the Iranian and Indian nuclear infrastructure serve as prominent examples of such dangers. Various sectors such as banks, hospitals, and government websites have all been targets of cyber-attacks, resulting in identity theft and hacking. To combat this growing problem, it is essential to make dedicated efforts to address the cyber threat in an appropriate manner. It is crucial to identify the most recent cyber threats and explore the best possible means of dealing with them, which is the need of the hour. This paper provides a comprehensive discussion of cybercrime and its various forms in Pakistan, along with its outcomes. Additionally, it covers the Prevention of Electronic Act - 2016, a much- needed legislation in a society where information technology has become an integral aspect of daily life. The study also highlights the exploitation of this act and the loopholes in the existing infrastructure. Furthermore, the paper includes an analysis of the effectiveness of measures required to mitigate the latest cyber threats and explains the measures implemented, as well as additional countermeasures needed to combat this threat. © 2023 IEEE.

关键词： Crime

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Grassroots Systems: Concept, Examples, Implementation and Applications

arXiv

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arXiv 2023年

作者： Shapiro, Ehud Department of Computer Science and Applied Math Weizmann Institute of Science Israel

Informally, a grassroots system is a distributed system that can have multiple instances, independent of each other and of any global resources, that can interoperate once interconnected. More formally, in a grassroots system the set of all correct behaviors of a set of agents P is strictly included in the set of the correct behaviors of P when embedded within a larger set of agents P′ ⊃ P: Included, meaning that members of P are correct to ignore outside agents indefinitely;strictly included, meaning that the interaction of members of P with outside agents is also a possible and correct behavior. Client-server/cloud computing systems are not grassroots, and neither are systems designed to have a single global instance (Bitcoin/Ethereum with hardwired seed miners/bootnodes), and systems that rely on a single global data structure (IPFS, DHTs). An example grassroots system would be a serverless smartphone-based social network supporting multiple independently-budding communities that can merge when a member of one community becomes also a member of another. Grassroots applications are potentially important as they may allow people to conduct their social, economic, civic, and political lives in the digital realm solely using the networked computing devices they own and operate (e.g., smartphones), free of third-party control, surveillance, manipulation, coercion, or rent seeking (e.g., by global digital platforms such as Facebook or Bitcoin). Here, we formalize the notion of grassroots systems and grassroots implementations;specify an abstract grassroots dissemination protocol;describe and prove an implementation of grassroots dissemination for the model of asynchrony;extend the implementation to mobile (address-changing) devices that communicate via an unreliable network (e.g. smartphones using UDP);and discuss how grassroots dissemination can realize applications that support digital sovereignty – grassroots social networking and grassroots currencies. The mathemat

关键词： Smartphones

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Towards a Supporting Framework for Neuro-Developmental Disorder: Considering Artificial Intelligence, Serious Games and Eye Tracking

Towards a Supporting Framework for Neuro-Developmental Disor...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Rehman, Abdul Heldal, Ilona Stilwell, Diana Lin, Jerry Chun-Wei Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway Alameda da Universidade Faculty of Psychology Lisbon1649-013 Portugal

ISBN: (纸本)9798350362480

This paper focuses on developing a framework for uncovering insights about NDD children's performance (e.g., raw gaze cluster analysis, duration analysis & area of interest for sustained attention, stimuli expectancy, loss of focus/motivation, inhibitory control) and informing their teachers. The hypothesis behind this work is that self-adaptation of games can contribute to improving students' well-being and performance by suggesting personalized activities (e.g., highlighting stimuli to increase attention or choosing a difficulty level that matches students' abilities). The aim is to examine how AI can be used to help solve this problem. The results would not only contribute to a better understanding of the problems of NDD children and their teachers but also help psychologists to validate the results against their clinical knowledge, improve communication with patients and identify areas for further investigation, e.g., by explaining the decision made and preserving the children's private data in the learning process. © 2024 IEEE.

关键词： Students

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Heart Disease Risk Prediction Expending of Classification Algorithms

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computers, Materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha Mary Bilal Khan Abdullah A.Asiri Fazal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irfan Khalaf A.Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science Najran University Kingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early *** failure is on the rise as a result of today’s *** healthcare business generates a vast volume of patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful *** methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning *** a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from *** SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

关键词： Heart disease heart disease datasets evaluation measures classification techniques

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Real-Time Lateral Sitting Posture Detection using YOLOv5

Real-Time Lateral Sitting Posture Detection using YOLOv5

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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Niklas Hoefflin Tim Spulak André Jeworutzki Jan Schwarzer Department of Computer Science Faculty of Engineering and Computer Science Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Prolonged periods of sitting at work and in everyday life highlight the importance of a correct sitting posture to avoid long-term health issues. Technologies such as smart chairs offer real-time assistance for adjusting poor postures through direct feedback. This study introduces a method for the real-time detection and evaluation of sitting postures from a lateral perspective, utilizing the real-time object recognition algorithm You Only Look Once version 5 (YOLOv5). So far, YOLOv5 has not been used for that purpose. Our work is a response to current research that emphasizes, for example, the need to combine existing devices such as smartphones with sitting posture detection to promote health awareness and better sitting habits. Correspondingly, we chose to train a series of small-sized YOLOv5 models (YOLOv5s) due to their efficiency to run on, for instance, mobile devices to make our approach accessible for as many people as possible. The trained models categorize sitting postures into two distinct classes: sitting good and sitting bad. Each model was trained on a diverse dataset of seated people, ensuring rich nuances in the underlying data. Results are demonstrated using the Mean Average Precision (mAP) metric, enabling the determination of the best-performing YOLOv5s model. This model shows high accuracy in identifying correct and incorrect sitting postures with mAP scores at both the 50% and the 50–95% Intersection over Union (IoU) thresholds of 93.1% and 73.4%, respectively.

关键词： YOLO Training Annotations Biological system modeling Computational modeling Manuals computer architecture Streaming media Real-time systems Smart phones

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KGIF: Optimizing Relation-Aware Recommendations with Knowledge Graph Information Fusion

KGIF: Optimizing Relation-Aware Recommendations with Knowled...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Jeon, Dong Hyun Sun, Wenbo Song, Houbing Herbert Liu, Dongfang Alvaro, Velasquez Xie, Yixin Chloe Niu, Shuteng Bowling Green State University Department of Computer Science United States University of Michigan Transportation Research Institute Ann Arbor United States University of Maryland Baltimore County Department of Information Systems United States Rochester Institute of Technology Department of Computer Engineering United States University of Colorado College of Engineering & Applied Science Boulder United States Kennesaw State University Department of Information Technology United States

ISBN: (纸本)9798350362480

While deep-learning-enabled recommender systems demonstrate strong performance benchmarks, many struggle to adapt effectively in real-world environments due to limited use of user-item relationship data and insufficient transparency in recommendation generation. Traditional collaborative filtering approaches fail to integrate multifaceted item attributes, and although Factorization Machines account for item-specific details, they overlook broader relational patterns. Collaborative knowledge graph-based models have progressed by embedding user-item interactions with item-attribute relationships, offering a holistic perspective on interconnected entities. However, these models frequently aggregate attribute and interaction data in an implicit manner, leaving valuable relational nuances *** study introduces the Knowledge Graph Attention Network with Information Fusion (KGIF), a specialized framework designed to merge entity and relation embeddings explicitly through a tailored self-attention mechanism. The KGIF framework integrates reparameterization via dynamic projection vectors, enabling embeddings to adaptively represent intricate relationships within knowledge graphs. This explicit fusion enhances the interplay between user-item interactions and item-attribute relationships, providing a nuanced balance between user-centric and item-centric representations. An attentive propagation mechanism further optimizes knowledge graph embeddings, capturing multi-layered interaction patterns. The contributions of this work include an innovative method for explicit information fusion, improved robustness for sparse knowledge graphs, and the ability to generate explainable recommendations through interpretable path visualization. The implementation and datasets for this study are publicly available1 © 2024 IEEE.

关键词： Knowledge graph

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The Intersection of computer engineering and Intellectual Property Law 15

The Intersection of Computer Engineering and Intellectual Pr...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Bansal, Shweta Sharma, Sachin Sagar, Sipra Ahmad, Shamim Mishra, Rahul Pandey, Arvind Kumar K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India Invertis University Department of Applied Sciences Uttar Pradesh Bareilly243123 India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India Arka Jain University Department of Computer Science and Information Technology Jharkahnd Jamshedpur India

ISBN: (纸本)9798350370249

laptop engineering and intellectual belongings regulation are beautiful fields, however their intersection has grow to be increasingly more extensive in current years. With technology advancing speedy, computer engineering has end up important in all industries, even as highbrow assets law has turn out to be critical in protecting the rights of creators and innovators. computer engineering involves the layout, improvement, and implementation of pc hardware and software program application systems. The era maintains to conform, computer engineers are constantly growing new merchandise and generation that push the bounds of innovation. however, with the ones improvements come demanding situations related to highbrow assets rights. As such, laptop engineers must have complete understanding of intellectual belongings regulation to shield their creations and navigate criminal problems, patent infringement, and alternate mystery robbery. however, highbrow assets regulation encompasses a set of criminal policies and rules that guard ideas, creations, and improvements from unauthorized use or duplication. © 2024 IEEE.

关键词： Creations Duplication Industries Protecting Unauthorized

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"Fiction to Function"Shaping Renewable Energy Education with MATLAB and ChatGPT-Driven Environments 21

"Fiction to Function"Shaping Renewable Energy Education with...

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21st International Learning and Technology Conference, L and T 2024

作者： Mohamed, Baher Babiker, Safia Aldybous, Alaa Kabbaj, Narjisse Brahimi, Tayeb Effat University Effat College of Engineering Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Natural Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

Addressing the gap in effective and engaging renewable energy education, this study, 'Fiction to Function,' transitions imaginative frameworks into practical educational tools, focusing on solar and wind energy in line with Sustainable Development Goals (SDGs). The goal is to empower users with deep insights into renewable energy through the novel integration of MATLAB and ChatGPT-driven environments, enhancing the analysis and exploration of these energy sources. The study employs a structured approach using MATLAB App Designer to develop a user-friendly GUI, aligning instructional content with the SDGs and integrating ChatGPT via the OpenAI API for dynamic, interactive learning experiences. This integration provides a multifaceted platform that not only educates but also engages users in practical scenarios, bridging the gap between theoretical knowledge and real-world application. The results demonstrate an enhanced understanding of renewable energy concepts among users, showcasing the efficacy of combining MATLAB's analytical capabilities with ChatGPT's AI-driven guidance. Ultimately, the study highlights the transformative potential of innovative technology in sustainable energy education, setting a precedent for future educational tools in this domain. © 2024 IEEE.

关键词： Integration

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