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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Vats, Shailender Kumar Peddi, Prasadu Vats, Prashant Department of Computer Application Institute of Computer Science and Engineering Shri Jagdishprasad Jhabarmal Tibrewala University Jhunjhunu Rajasthan Chudela India Uttar Pradesh Noida India Department of Computer Science Engineering and Information Technology Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan Jhunjhunu India Department of Computer Science and Engineering SCSE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819766772

The potential of blockchain technology to transform several industries, including supply chains, health care, and banking, has drawn much interest in recent years. Blockchain is becoming widely used, but this has raised questions about its security and ability to withstand attacks and threats. An overview of blockchain security issues and risk-reduction strategies is given in this abstract. The decentralized and unchangeable character of blockchain, which is accomplished through the application of cryptographic algorithms and consensus processes, is its primary security feature. However, it remains susceptible to security flaws such as 51% assaults, smart contract glitches, and compromised private keys. This abstract investigates these flaws and how they might affect the security and integrity of blockchain data. Several strategies have been put out and put into practice to increase blockchain security. The abstract looks at how important it is to set best practices and standards to protect blockchain networks from malicious activity and unauthorized access. Security issues will arise as the blockchain ecosystem develops, requiring constant study and cooperation between government, business, and academia. The appeal for a coordinated effort to address blockchain security issues in their entirety and guarantee the technology’s safe and dependable use across a range of applications and sectors is made in the abstract’s conclusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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An Intelligent Diabetes Prediction System Augmenting Feature Selection and Balancing Techniques 7th

An Intelligent Diabetes Prediction System Augmenting Feature...

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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Giri, Sourav Kumar Dash, Sujata Sahoo, Tapaswini Department of Computer Science and Engineering Srinix College of Engineering Odisha Balasore756001 India Department of Computer Application Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada757003 India Skill Development Institute Odisha Bhubaneswar India

ISBN: (纸本)9789819950140

Diabetes seems to be a prevalent disease nowadays across the whole globe and is found to be very common on every doorstep. The consequences of this disease are very severe if it remains unnoticed during its initial stage. It affects populations of every age group. The causes of this disease are attributed to genetic links, lack of physical exercise, unhealthy food habits, overstress, etc. Many recent machine learning models have been proposed to predict this disease by considering the historical data of patients who have diabetes. This work attempts to choose a robust and reliable machine learning model for diagnosing the disease from seven widely used classification algorithms such as;Logistic Regression, Decision Tree, Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, and AdaBoost. The model is validated with the PIMA diabetes dataset. The efficiency of the models is augmented by addressing the imbalanced characteristics of the dataset by applying SMOTE technique and a univariate feature selection technique, ‘chi-square’ for feature selection. The experimental findings confirm that the Random Forest model has outperformed other models in terms of accuracy, precision, recall, F-score, and AUC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Analysis of Energy Quality Issues in the Solar Energy System 3

Analysis of Energy Quality Issues in the Solar Energy System

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3rd IEEE International Conference on Electrical Power and Energy Systems, ICEPES 2024

作者： Uniyal, Ishita Tiwari, Shweta Vishal Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Electrical Engineering Dehradun India University Institute of Engineering Chandigarh University Department of Computer Science & Engineering Punjab Mohali India School of Computer Application Lovely Professional University Punjab Jalandhar India Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350390728

Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is power quality. DSTATCOM is a power quality compensating device that corrects voltage sag and swell are examples of power quality concerns. In high voltage line for power transmission this can be happened. The study was carried out with MATLAB or Simulink software to resolve the voltage sag and swell, and the model was created. The simulation results show the problem of voltage sag and swell is minimize to very large extent. © 2024 IEEE.

关键词： Power distribution lines

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Leveraging AI to Unlock the Potential of Data Science 15

Leveraging AI to Unlock the Potential of Data Science

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

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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A Survey of Current Progress in Depression Detection Using Deep Learning and Machine Learning

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Biomedical Materials and Devices 2025年 1-25页

作者： Kumari, Meena Singh, Gurpreet Pande, Sagar Dhanraj Computer Application Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Department of Computer Science and Engineering Lovely Professional University Jalandhar - Delhi Grand Trunk Rd Punjab Phagwara 144411 India Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune 412106 India

Generally, depression is the most prevalent mental disorder, which has affected hundreds of people around the world and causes a high risk of committing suicide. Automatic diagnosis of depression detection plays a vital role in examining people’s mental illness through various Artificial Intelligence (AI) techniques and has a high probability of reducing patient’s burden by early treatments. In this research, the crucial area of depression detection is examined, with an emphasis on the crucial function of hybridized algorithms. More studies have been developed worldwide to provide a review of depression detection techniques. Still, obtaining significant information about the input data and the different mechanisms utilized in each research for depression detection is challenging. Consequently, this review presents a systematic review of various Deep Learning (DL) and Machine Learning (MI) techniques based on the input data utilized in the models for detecting depression, heralding a seismic shift in our understanding of depression and the diagnostic and support systems connected to it. More specifically, this review examines the literature from 50 recent research articles based on depression detection topics incorporated with its significant morphology that helps findings in the creation of novel models in the future. Further, this review discusses the research gaps, challenges, dataset collection, interdisciplinary cooperation, other ideal strategies, and future scope for progressing and catalyzing the research in this domain. In addition, this review presents an overview to identify the objectives and restrictions of different literature works presented in the domain of depression detection. Moreover, the developed review assists in advancing the knowledge about the technologies adopted for depression detection and serves as a suitable reference for upcoming researchers working in this domain. © The Author(s), under exclusive licence to Springer Science+Business M

关键词： Deep learning Depression detection Machine learning Multimodal data Natural language processing

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Developing Adaptive Deep Learning Strategies for 5G Network Management 15

Developing Adaptive Deep Learning Strategies for 5G Network ...

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

作者： Patil, Shrinivas Kumar, Viranshu Goyal, Manish Kumar Karnataka Bangalore India Department of Mechnical Engineering ARKA JAIN University Jharkhand Jamshedpur India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9798350370249

This paper affords a unique method for growing deep mastering strategies for 5G community control. It leverages state-of-the-art deep neural community architectures and superior technologies inclusive of hierarchical learning, reminiscence networks, and differentiable programming to allow the machine to operate more efficaciously in an ever-changing surrounding. The gadget includes two principal components: an offline schooling community and a web auxiliary network. Each additive is related, permitting the net network to refine the educated parameters to adapt to changing occasions. The gadget is designed to deal with some of the maximum hard issues in 5G community management, along with excessive degree choice-making, resource optimization, and exceptional service enforcement. Experiments and simulations are used to evaluate the performance enhancements finished via applying the proposed technique. The consequences display that the proposed method should improve the general overall performance of 5G networks drastically. The proposed method may be used in a selection of network management packages, which include network virtualization, visitor engineering, and self-organizing networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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IBB Traffic Graph Data: Benchmarking and Road Traffic Prediction Model 29

IBB Traffic Graph Data: Benchmarking and Road Traffic Predic...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Olug, Eren Kaya, Kiymet Tugay, Resul Oguducu, Sule Gunduz Istanbul Technical University Department of Artificial Intelligence and Data Engineering Istanbul Turkey Itu Ai Research and Application Center Istanbul Turkey Bts Group Istanbul Turkey Istanbul Technical University Department of Computer Engineering Istanbul Turkey Gazi University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350377644

Road traffic congestion prediction is a crucial component of intelligent transportation systems, since it enables proactive traffic management, enhances suburban experience, reduces environmental impact, and improves overall safety and efficiency. Although there are several public datasets, especially for metropolitan areas, these datasets may not be applicable to practical scenarios due to insufficiency in the scale of data (i.e. number of sensors and road links) and several external factors like different characteristics of the target area such as urban, highways and the data collection location. To address this, this paper introduces a novel IBB Traffic graph dataset as an alternative benchmark dataset to mitigate these limitations and enrich the literature with new geographical characteristics. IBB Traffic graph dataset covers the sensor data collected at 2451 distinct locations. Moreover, we propose a novel Road Traffic Prediction Model that strengthens temporal links through feature engineering, node embedding with GLEE to represent interrelated relationships within the traffic network, and traffic prediction with ExtraTrees. The results indicate that the proposed model consistently outperforms the baseline models, demonstrating an average accuracy improvement of 4%. © 2024 IEEE.

关键词： Traffic congestion

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Dynamic Load Balancing with Task Migration: A Genetic Algorithm Approach for Optimizing Cloud Computing Infrastructure 2

Dynamic Load Balancing with Task Migration: A Genetic Algori...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Priyadarshini, Aliva Pradhan, Sateesh Kumar Laha, Suprava Ranjan Nayak, Soumen Pattanaik, Bikash Chandra Utkal University Department of Computer Science & Application Odisha Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Science & Engineering Iter Odisha Bhubaneswar India Gandhi Institute for Education and Technology Affiliated to Biju Patnaik University of Technology Department of Computer Science & Engineering Odisha Rourkela India

ISBN: (纸本)9798350370188

The advancement of cloud computing is contingent upon the effective establishment of infrastructure and the flexible use of accessible resources. An essential aspect of this evolutionary process is load balancing, a critical task involving fair allocation of dynamic workloads among different nodes. The primary aim is to mitigate the issues of resource overload or underutilization issues, which presents a complex challenge in optimization. A proficient load balancer must be able to adapt its method to the dynamic environment and work demands. This research presents a novel load-balancing technique incorporating task migration using a Genetic Algorithm (GA). The objective is to achieve optimal load distribution within the cloud infrastructure while minimizing the makespan for a given set of tasks. By utilizing the inherent optimization capabilities of genetic algorithms and the flexibility of task migration, the suggested approach demonstrates dynamic adaptation to the constantly evolving demands of cloud computing. In order to evaluate the effectiveness of the plan, it is implemented and assessed using simulations carried out on the Cloud Analyst simulator. The findings from the simulation outcomes, as demonstrated in a representative application scenario, provide evidence for the effectiveness of the task migration-integrated genetic algorithm-based approach compared to existing methods such as First Come, First Serve (FCFS), Round Robin (RR), and the local search algorithm Stochastic Hill Climbing (SHC). This comprehensive method not only improves resource utilization efficiency but also showcases the capacity to mitigate performance limitations through intelligent task reallocation. The proposed technique, which incorporates task migration capabilities, shows potential in facilitating the development of a more efficient and adaptable cloud infrastructure as the cloud landscape undergoes ongoing changes. © 2024 IEEE.

关键词： Genetic algorithms

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Examining the application of Machine Learning to Improve Organizational Performance in the Medical Setting 15

Examining the Application of Machine Learning to Improve Org...

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

作者： Shelke, Ganesh C. Dutta, Abhishek Katariya, Jitendra Kumar Sakhare, Nitin N. Anandhasilambarasan, D. Mahalakshmi, V. Vishwakarma Institute of Technology Department of Information Technology Pune India Department of Computer Science and Information Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Chennai India

ISBN: (纸本)9798350370249

This paper examines the capacity utility of machine gaining knowledge of within the clinical putting to enhance organizational overall performance and affected person effects. The paper begins by means of offering a top level view of ways gadget gaining knowledge of is being utilized in other industries and the way it is able to be applied inside the scientific placing. It then goes on to speak about the important thing challenges related to growing and deploying gadget learning inside the clinical putting which include statistics privateness and security, records availability, and they want for comprehensive expertise of clinical contexts. It examines the modern-day literature on machine studying inside the scientific placing to perceive satisfactory practices and proposes regions of destiny studies. In the end, the paper summarizes the implications from the studies, highlighting the capacity advantages and demanding situations that want to be addressed earlier than machine gaining knowledge of may be efficiently implemented in the medical field. Universal, this paper offers an informative evaluation of the capability programs of machine gaining knowledge of inside the clinical placing and emphasizes the need for greater studies in this place. © 2024 IEEE.

关键词： clinical gaining greater satisfactory situations

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Mask Wearing Detection System for Epidemic Control Based on STM32 6th

Mask Wearing Detection System for Epidemic Control Based on ...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Luoli Yadav, Amit Khan, Asif Varish, Naushad Singh, Priyanka Thakkar, Hiren Kumar Department of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University DarwinNT Australia Department of Computer Application Integral University Lucknow India Department of Computer Science and Engineering GITAM University Hyderabad502329 India Department of Computer Science and Engineering SRM University Andhra Pradesh Amravati522502 India Department of Computer Science and Engineering Pandit Deendayal Energy Univrsity Gujarat Gandhinagar382007 India

ISBN: (纸本)9789819940707

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image recognition

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