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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Ansh Shankar Dhruv Varshney Alok Katiyar Vikash Kumar Mishra School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The escalating problem of waste clusters in the planet's oceans has raised alarms, with the Indian Ocean not being spared from the growing predicament of aquatic refuse. In this exploration, we harnessed the power of remote sensing advances, utilizing Sentinel-2 spacecraft observations to track and assess the proliferation of waste clusters within the Indian Ocean. The Sentinel-2 spacecraft's Multi Spectral Instrument (MSI) endows us with critical data about the hues of the ocean, which is instrumental in identifying and outlining these zones of concentrated pollution. The scope of this paper encompasses a detailed examination of the Sentinel-2 satellite's capabilities in remotely surveying the trash accumulation in the Indian Ocean, with an emphasis on gathering, preparing, and evaluating MSI data. This research taps into Sentinel-2's distinct features to unlock a better comprehension of the patterns and evolution of waste buildup in the Indian Ocean, shedding light on a significant ecological challenge. The findings affirm the value of Sentinel-2 as a tool for overseeing and tackling marine debris in the Indian Ocean and furnishing crucial insights to decision-makers and environmental bodies dedicated to shaping effective countermeasures.

关键词： Space vehicles Waste materials Pollution Indian Ocean Instruments Plastics Remote sensing

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Beyond Search: A Network-Based Exploration of Buyer Behavior in the OLX Car Market

Beyond Search: A Network-Based Exploration of Buyer Behavior...

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Multimedia Processing, Communication & Information Technology (MPCIT), International Conference on

作者： Y. Moin Ashvath Ramya G R Department of Computer Science and Engineering Amrita School of Computing Coimbatore India

ISBN: (数字)9798350375466

ISBN: (纸本)9798350375473

The information on used cars offers a thorough analysis of the second-hand car industry. This dataset may be utilized to construct predictive models that can estimate the price of a used car based on its characteristics, including the make, model, year, mileage, fuel type, and condition. This dataset enables comprehensive data analysis to extract insights into market trends, pricing dynamics, and buyer-seller behavior. The main goals are to examine price patterns depending on different characteristics, assess the influence of fuel type and geographical factors on pricing and demand, investigate the correlation between mileage and resale value, and determine the most popular automobile models. Furthermore, the dataset enables the development of predictive models that can estimate both selling prices and the amount of time it takes to sell a product. Methodologies encompass the processes of data cleaning and exploratory data analysis (EDA). The results will assist in identifying communities or clusters within the dataset, uncovering groups of cars with similar attributes or market characteristics. The results aim to offer comprehensive market insights, improve comprehension of buyer behavior, and create predictive tools to assist sellers, buyers, and platform operators in decision-making. Ultimately, this will optimize strategies and operations in the used car market.

关键词： Data analysis Social networking (online) Shape Pricing Predictive models Market research Automobiles Fuels Stakeholders Vehicle dynamics

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Physical Layer Covert Communication in B5G Wireless Networks - its Research, Applications, and Challenges

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Proceedings of the IEEE 2024年第1期112卷 47-82页

作者： Jiang, Yu'e Wang, Liangmin Chen, Hsiao-Hwa Shen, Xuemin University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China Southeast University School of Cyber Science and Engineering Nanjing211189 China National Cheng Kung University Department of Engineering Science Taiwan70101 Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooN2L 3G1 Canada

Physical layer covert communication is a crucial secure communication technology that enables a transmitter to convey information covertly to a recipient without being detected by adversaries. Unlike typical cryptography and physical layer security systems that concentrate on protecting the sent signal content, covert communications seek to conceal the existence of legitimate transmission. Thus, with beyond fifth-generation (B5G) wireless communications, covert communications can operate in tandem or as a supplement to conventional security techniques. We provide an extensive overview of the basic theories and several strategies in physical layer covert communications in this article. In particular, we go into great detail about the basic theories of physical layer covert communications, such as channel models, codes, secret keys, and covertness metrics, as well as various covert schemes in progressively more complicated scenarios, such as covert communications in single-antenna and multiantenna three-node systems and covert communications in jammer- and relay-aided systems. In addition, we identify the challenges and future directions for research on covert communications in B5G wireless networks. © 1963-2012 IEEE.

关键词： Transmitters

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Generative AI for Community Empowerment: Transforming Livelihood Opportunities in a Rural Indian Village 4

Generative AI for Community Empowerment: Transforming Liveli...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bharath Suhas, K.B. Sri Krishna, A.P. Karishram, B. Roopesh, Maddina Sai Jothi S, Sachithanantha Teja, Kondepati Kumar, Anu G. Nandanan, Krishna Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electronics and Communication Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Civil Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mechanical Engineering Engineering India Coimbatore Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering India Amritapuri Amrita Vishwa Vidyapeetham Amrita School for Sustainable Futures India

ISBN: (纸本)9798350343670

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly emerging technologies that augur revolutionary changes in developing nations. AI and ML can help address challenges in critical areas such as agriculture, healthcare, education, and employment. The challenges and barriers to the widespread adoption of AI and ML technologies in Indian villages were analyzed through participatory approaches from a livelihood perspective. AI implementations were proposed for a real-world case study conducted as part of the Live-in-Labs® program in Malkhanpur, a rural village in the Indian state of Uttar Pradesh, addressing the community challenge of low income. The challenges were evaluated at different dimensions - community level, household level, and individual level, using Participatory Rural Appraisal (PRA) and Human-centered Design (HCD) approach. This paper explored the application of AI to drive employment to achieve income generation and overall well-being. A generative AI-based platform, 'JobConnect: AI-based Rural Job Seeker-Provider App with ChatGPT Assistance' is proposed with AI-based virtual assist with NLP, location-based Job listing, and matching algorithms. The platform is designed to be user-friendly and accessible to rural community with varying levels of digital literacy and connectivity. © 2024 IEEE.

关键词： Generative AI Human-Centered Design Participatory Rural Appraisal Rural Empowerment SDG 8 Technology

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Cooperative Self-Scheduling Routing Approach Based on Energy Efficient Optimal Link Stability Routing Allocation for Improving QoS-WSN

Cooperative Self-Scheduling Routing Approach Based on Energy...

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2024 Advances in science and engineering Technology International Conferences, ASET 2024

作者： Radhika, G. Radhika, N. Paul, Sujni Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India Computer Information Science Higher Colleges of Technology Dubai Men's Campus Academic City Dubai United Arab Emirates

ISBN: (纸本)9798350344134

The development of Wireless Sensor Networks (WSN) is broad research in recent communication approaches. Due to the increasing demand for service, the quality is insufficient to meet the requirements to take energy efficient network. This leads to high energy losses, traffic, improper scheduling, and poor network performance. Selecting a higher traffic route introduces higher packet drop and reduces the throughput performance. Analyzing the traffic in the existing process is complex and doesn't provide Quality of Service (QoS). So data analysis is essential based on reinforcement learning with an intelligent machine learning approach. To resolve this problem, we propose a Cooperative self-scheduling routing approach (CS2RA) based on energy Efficient Optimal Link stability routing allocation (EOLSRA) designed to improve the quality of service in Wireless sensor networks. The dynamic network topology medium is constructed with Neighbor Stable Link Connectivity Based Routing. With the Probability support, the traffic density approximation rate (PTDAR) is estimated by improving energy stabilization. By intending an optimistic Q-learning network model based on the energy-consuming parameters using an adaptive Petal spider ant colony cluster (PSACC) is constructed for link stability for route optimization. Then Cooperative stabilized proactive Energy-aware cluster routing protocol (CSPECR) was intended to make self-balanced congestion routing with the support of the Lookup energy constraint duty cycle (LECDC) to improve the lifetime of the network. © 2024 IEEE.

关键词： Cooperative communication

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An Efficient Resource Allocation Model in IIoT Using Federated Reinforcement Learning

An Efficient Resource Allocation Model in IIoT Using Federat...

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作者： Mahdi Safaei Yaraziz Richard Hill University of Huddersfield Department of Computer Science School of Computing and EngineeringHuddersfieldUK

In the Industrial Internet of Things (IIoT), resource allocation is important for reducing downtime and improving the system's operational performance. This study introduces a novel federated reinforcement learning approach that addresses resource management difficulties by enabling several agents to learn optimum maintenance policies jointly while maintaining data privacy. According to the analysis and evaluation performed, the proposed technique has the potential for implementation in complex industrial contexts, with future work concentrating on integrating advanced predictive models and expanding the algorithm to include multi-objective optimization cases.

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Evaluating the Effectiveness of Google Cloud Platform Against a Web Server

Evaluating the Effectiveness of Google Cloud Platform Agains...

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： K. Abirami N. Harini Department of Computer Science and Engineering Amrita School of Computing Vidyapeetham India

Containers are serverless and wrap an application as a solitary executable programming bundle that groups application code along with all the connected arrangement records, libraries, and conditions expected to run. The popularity of containers is growing over traditional infrastructure because they are inherently efficient. Lacking a guest operating system and being agnostic to the underlying platform, containers promote modern development practices and architecture, enhancing scalability by allowing individual components to be scaled independently. Peak load on servers makes services inaccessible when an unforeseen responsibility hits the server. To improve performance, peak loads on the servers need to balance the user-generated requests. In this work, a comparison between the web servers and serverless containers is carried out. The results show clearly that the serverless containers perform better than the webservers. In our work, the Firefox GUI is successfully hosted in the web server and Google Container. The requests from the clients are monitored, and the metric Response Time is measured.

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XRL-SHAP-Cache:an explainable reinforcement learning approach for intelligent edge service caching in content delivery networks

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science China(Information sciences) 2024年第7期67卷 46-71页

作者： Xiaolong XU Fan WU Muhammad BILAL Xiaoyu XIA Wanchun DOU Lina YAO Weiyi ZHONG School of Software Nanjing University of Information Science and Technology School of Computing and Communications Lancaster University School of Computing Technologies Royal Melbourne Institute of Technology State Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Engineering University of New South Wales Data 61 Commonwealth Scientific and Industrial Research Organization School of Computer Science Qufu Normal University

Content delivery networks(CDNs) play a pivotal role in the modern internet infrastructure by enabling efficient content delivery across diverse geographical regions. As an essential component of CDNs, the edge caching scheme directly influences the user experience by determining the caching and eviction of content on edge servers. With the emergence of 5G technology, traditional caching schemes have faced challenges in adapting to increasingly complex and dynamic network environments. Consequently, deep reinforcement learning(DRL) offers a promising solution for intelligent zero-touch network governance. However, the blackbox nature of DRL models poses challenges in understanding and making trusting decisions. In this paper,we propose an explainable reinforcement learning(XRL)-based intelligent edge service caching approach,namely XRL-SHAP-Cache, which combines DRL with an explainable artificial intelligence(XAI) technique for cache management in CDNs. Instead of focusing solely on achieving performance gains, this study introduces a novel paradigm for providing interpretable caching strategies, thereby establishing a foundation for future transparent and trustworthy edge caching solutions. Specifically, a multi-level cache scheduling framework for CDNs was formulated theoretically, with the D3QN-based caching scheme serving as the targeted interpretable model. Subsequently, by integrating Deep-SHAP into our framework, the contribution of each state input feature to the agent's Q-value output was calculated, thereby providing valuable insights into the decision-making process. The proposed XRL-SHAP-Cache approach was evaluated through extensive experiments to demonstrate the behavior of the scheduling agent in the face of different environmental *** results demonstrate its strong explainability under various real-life scenarios while maintaining superior performance compared to traditional caching schemes in terms of cache hit ratio, quality of service(QoS),a

关键词： deep reinforcement learning (DRL) explainable artificial intelligence (XAI) multi-level cache content delivery network (CDN) D3QN algorithm Deep-SHAP

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Improving Long Short-Term Memory (LSTM)-Based Stock Market Price Predictions in the Machine Learning Era

Improving Long Short-Term Memory (LSTM)-Based Stock Market P...

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computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Sarvesh Kumar Nitin Pal Aanjey Mani Tripathi School of Computing Science and Engineering Galgotias University Greater Noida India

Post covid, world especially India has seen enormous rise in stock market investor. Profits in stock market solely depends upon prediction – right prediction can help gain money. And predicting stock price can be very tedious task if done manually. Traditional methods are using stock price charts, exponential moving average (EMA), oscillators, indicators etc. While predicting stock price manually, human greed nature can lead to wrong decisions while choosing right stock. Latest technologies like machine learning and artificial intelligence can be used to predict future of stocks. Several research and works with different techniques have been already carried out to do the same. In this research explores a method, for predicting stock prices using machine learning, Long Short-Term Memory (LSTM) networks. These networks, which fall under the umbrella of learning provide a solution, by successfully capturing complex patterns from past stock data. As we explore the existing body of research and real-world implementations our goal is to participate in the discussion surrounding the utilization of machine learning, specifically LSTM networks to improve the precision and dependability of stock price forecasts. Looking ahead we anticipate a future where financial decision making becomes more informed and accurate.

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

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Advances in computing, Communication and Networking (ICAC2N), International Conference on

作者： Ajay Shankar Ankush Singh Kuldeep Goswami Dhruv Gupta School of Computing Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

The analysis and analysis of cardiovascular sickness are vital clinical obligations to make sure Best to classification, this is helps provide right remedy to the affected character. Gadget analyzing programs within the medical area of interest have improved as they are able to recognize styles from statistics. Using gadget getting to know to Justify the cardiovascular sickness prevalence help misdiagnosis. This study makes a model that can predict heart disease to reduce deaths from heart disease. This paper presents a good integration method that can improve the accuracy of the group starting from Huang. Models used include random forest, decision trees classification, multilayer perceptron, and XG gain (XGB). Grid Search CV starts using the limitations of the Super Music Complete version to improve results. The proposed model was based on real-world data of 73,000 cases from Kaggle. The model was trained on 85.68% cutoff data and achieved the following new accuracy: decision tree: 86.34% (with crossover) and 87.58% (without matching), XG gain: 85.87% (with pass validation) and 87, 58% (without cross-validation), random forest: 87.58% (with pass-validation) and 86.34% (without cross-validation), multilayer perceptron: 87.18% (with pass-validation) and 87.92% (without pass verification).

关键词： Heart Accuracy Multilayer perceptrons Predictive models Prediction algorithms Data models Decision trees Random forests Standards Diseases

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