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International Conference on Innovative sustainable Computational Technologies (CIsCT)

作者： Nirosha veeramachaneni v. vaishnavee Nikhil Ranjan Rashi saxena K vandana sarvesh Kumar Department of CSE B V Raju Institute of Technology Narsapur Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Department of Computing Science and Engineering Galgotias University Greater Noida Computer Science & Engineering Koneru Lakshmaiah Education Foundation (KLEF) Hyderabad Department of CSE G L Bajaj Institute of Technology and Management Greater Noida Department of CSE BBD University Lucknow

ISBN: (纸本)9781665474177

Healthcare sector is having lots of data about various disease and newly discovered viruses and bacteria’s and so on. The data has been resided for a long at various data warehouses of each country’s healthcare department but due to lack of collaboration and employment of advanced machine learning algorithms the meaningful insights which may conclude from data research and association, remain hidden from world. Hence this paper proposes the application of various machine algorithms like linear regression and multi-layer perceptron on a target dataset which is having the attributes of related to healthcare industry. With implementation of algorithm the detection of diabetes in various areas covered in 80 attributes of selected dataset.

关键词： Industries Machine learning algorithms computer viruses Error analysis Linear regression Medical services Diabetes

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A Novel Advanced Algorithm in Automation of Food and Health Monitoring system using IOT

A Novel Advanced Algorithm in Automation of Food and Health ...

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International Conference for Convergence of Technology (I2CT)

作者： Pattem. sampath Kumar J. vijayasree K saikumar Mohammed Ayad Alkhafaji Ahmed J. Obaid sajad Ali Zearah Malla Reddy Institute of Engineering & Technology Hyderabad India Department of Electronic and Communication Vignan’s Institute of Information Technology (A) Dept of ECE Koneru Lakshmaiah Education Foundation HyderabadR.V.S Nagar Moinabad-Chilkur Rd near AP Police Academy Aziznagar Telangana India Department of Medical Device Industry Engineering College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Faculty of Computer Science and Mathematics University of Kufa Iraq Scientific Research Center Al-ayen University Thi-Qar Iraq

Food is one of the essential factors in human growth and development. The recent results of the internet of things and robots have already shown the beginnings of a new food monitorization. Automation in the restaurants and food sector did not originate technically, so humanoid-related IoT platforms are necessary. In this research, an advanced monitoring algorithm for the automation of food & health system performs using the (IoT). The suggested system develop using food quality sensing devices. Each detector was equipped with the above qualities and connected using breadboards and relay boards. There is tremendous fear and motivation in the thought that there may not be enough food to go around as the human population expands exponentially. To construct a monitoring system that can identify the presence of people, gases, and temperature differences, we employed website creation in combination with the (IoT). The designed application has attained a sensitivity of 97.53%, an accuracy of 98.83%, a recall of 97.98%, and a detection rate of 98.91%. The implemented technology is beneficial to researchers and society. The proposed IoT-based automation of food & health monitoring system compete with present technology and outperforms the methodology.

关键词： Temperature sensors Automation sensitivity software algorithms sociology sensor phenomena and characterization sensor systems

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Explainable AI for Fraud Detection: Enhancing Transparency and Trust in Financial Decision-Making 2nd

Explainable AI for Fraud Detection: Enhancing Transparency a...

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2nd International Conference on Emerging Wireless Technologies and sciences, ICEWTs 2024

作者： Kapale, Rahul Kishor Deshpande, Prasanna shukla, shyam b. Kediya, shailesh Pethe, Yoginee s. Metre, sujit Gajananrao Shri Ramdeobaba College of Engineering and Management Nagpur India School of Ports Terminal Management and Logistics Symbiosis Skills and Professional University Kiwale Pune India BVDU IMED Pune India School of Logistics and Supply Chain Management Symbiosis Skills and Professional University Pune India Department of Computer Science and Applications Ramdeobaba University Nagpur India S. B. City College Nagpur India

ISBN: (纸本)9783031878855

Within the past few a long time, utilizing Counterfeit Insights (AI) to discover extortion in managing an account frameworks has gotten a parcel of consideration since it seem make things more secure and more effective. but when AI models are utilized, particularly in critical areas like keeping money, there has to be a adjust between exactness and openness. This exposition looks at how Explainable AI (XAI) can offer assistance unravel these issues by making trick discovery frameworks less demanding to understand. This will lead to more openness and trust within the ways that monetary choices are made. The most reason of this think about is to see into how XAI strategies can offer assistance us get it how complex AI models utilized for trick spotting truly work. by clarifying how these models make choices, XAI not as it were increments openness but too makes a difference individuals who have a stake within the matter, like budgetary controllers, bookkeepers, and conclusion clients, get it why certain choices are made. This information is exceptionally critical for making beyond any doubt that individuals are held dependable and take after the rules set by the basel Committee on Managing an account supervision (bCbs) and the Common Information security Control (GDPR). The think about moreover looks at diverse XAI strategies and how they can be utilized to find fraud. These incorporate model-agnostic approaches such as LIME (Nearby Interpretable Model-agnostic Clarifications) and sHAP (sHapley Added substance Clarifications). These strategies grant you data around how critical highlights are by appearing you which components have the greatest affect on foreseeing extortion. These sorts of findings not as it were offer assistance to test and progress models, but they moreover allow money related educate more data to assist them make superior choices approximately how to handle hazard and halt tricks. This consider moreover talks about the benefits of XAI that go beyond

关键词： budget control

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Electromagnetic Channel Modeling and Capacity Analysis for HMIMO Communications

arXiv

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arXiv 2025年

作者： Wei, Li Yuan, shuai s.A. Huang, Chongwen Zhang, Jianhua bader, Faouzi Zhang, Zhaoyang Muhaidat, sami Debbah, Mérouane Yuen, Chau School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Technology Innovation Institute Masdar City Abu Dhabi United Arab Emirates KU 6G Research Center Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi127788 United Arab Emirates Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada KU 6G Research Center Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates

Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless communication systems. To accurately characterize the achievable performance limits of these systems, it is crucial to develop a universal EM-compliant channel model. This paper addresses this necessity by proposing a comprehensive EM channel model tailored for realistic multi-path environments, accounting for the combined effects of antenna array configurations and propagation conditions in HMIMO communications. both polarization phenomena and spatial correlation are incorporated into this probabilistic channel model. Additionally, physical constraints of antenna configurations, such as mutual coupling effects and energy consumption, are integrated into the channel modeling framework. simulation results validate the effectiveness of the proposed probabilistic channel model, indicating that traditional Rician and Rayleigh fading models cannot accurately depict the channel characteristics and underestimate the channel capacity. More importantly, the proposed channel model outperforms free-space Green’s functions in accurately depicting both near-field gain and multi-path effects in radiative near-field regions. These gains are much more evident in tri-polarized systems, highlighting the necessity of polarization interference elimination techniques. Moreover, the theoretical analysis accurately verifies that capacity decreases with expanding communication regions of two-user communications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna arrays

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Retraction Note: Enhanced secure communication over inter-domain routing in heterogeneous wireless networks based on analysis of bGP anomalies using soft computing techniques

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soft Computing 2024年第2期28卷 879-879页

作者： Elamathi, N. Jayashri, s. Pitchai, R. Department of Information Technology Adhiparasakthi Engineering College Melmaruvathur India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur India Department of Computer Science and Engineering B.V. Raju Institute of Technology Narsapur Methak India

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segmentation of Arecanut bunches: A Comparative study of Different Color Models 1

Segmentation of Arecanut Bunches: A Comparative Study of Dif...

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1st IEEE Mysore sub section International Conference, MysuruCon 2021

作者： Dhanesha, R. Umesha, D.K. shrinivasa Naika, C.L. Girish, G.N. Davangere University Dept. of Studies in Computer Science Davanagere Karnataka India Government Women's Polytechnic Shiralakoppa Dept. of CS&E Shivamogga Karnataka India U. B. D. T. College of Engineering Dept. of Studies in CS&E Davanagere Karnataka India Indian Institute of Information Technology Computer Science and Engineering Group Sri City Chittoor India

ISBN: (纸本)9780738146621

Arecanut is one of the important commercial crop of Agriculture sector. Agriculture sector plays important role towards the economic development of India. The market price of arecanut is determined by it's maturity level of the ripeness. Farmers often incur loss in profit due to the lack of expertise in judging the ripeness maturity level of the arecanut bunches before the harvest. In the recent years, image processing and computer vision based precision agriculture techniques has helped the farmers in identifying the ripeness quality of the crops. so, accurate segmentation of the arecanut bunches plays vital role in the automated identification of the arecanut ripeness maturity level. In this proposed work YUv, YCbCr, YCgCr, YPbPr and Hsv color models are used to segment arecanut bunches. Dataset with 1017 images of arecanut bunch are used to conduct experiment and segmentation result of the each color model is evaluated using different segmentation performance metrics. Results of experiment clarifies, segmentation of arecanut bunches were efficient using YCgCr and Hsv color models. © 2021 IEEE.

关键词： computer vision

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scalability and Performance Evaluation of Machine Learning Techniques in High-volume social Media Data Analysis

Scalability and Performance Evaluation of Machine Learning T...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Kottala sri Yogi Dankan Gowda v Mouna K M L.R. sujithra KDv Prasad P Midhun Department of Operations Symbiosis institute of Business Management Hyderabad Symbiosis International University Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka Department of Electronics & Communication Engineering BGS Institute of Technology/ACU B G Nagara Mandya Karnataka India Department of Computer Science and Engineering Dr.N.G.P.Institute of Technology Coimbatore Tamilnadu India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Electronics & Communication Engineering Sree Vidyanikethan Engineering College (Now It's MOHAN BABU UNIVERSITY) Tirupati Andhra Pradesh India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

In the modern era of digital communication, social media platforms are data factories and hence provide a new set of opportunities as well as challenges before the analysts. The objective of this paper is to evaluate the scalability and performance of various ML algorithms for social media data analysis in practice with high-volume datasets. The objective is to design ML algorithms that are scalable and achieve high accuracy when used with big datasets. In a literature survey, observed gaps in the present research advancements toward scalability and performance analysis for various ML techniques used for this intent. To make sure of diversity and size, we collected a big source from different social networks. The approach we did involve assessing a number of ML methods that are widely known like natural language processing (NLP), sentiment analysis (sA) and predictive analytics on scalability factors such as time to process and resource utilization while also considering performance metrics such as accuracy precision recall rate f1 score. The results demonstrate significant differences in the scalability of investigated approaches and their quality, such as some algorithms being more efficient or accurate at a large scale. This research adds value to the existing works in this area, by performing a detailed comparative study of ML techniques for large-scale social media data processing considering scalability vs performance tradeoffs. The findings are informative, for the research and practice community, on selection of suitable ML techniques that will be applicable to social media analytics; scalability is an important factor in this era dominated by big data. In future research, more advanced techniques and hybrids should be taken into account to enhance the scalability and performance of social media data analysis.

关键词： support vector machines surveys sentiment analysis Data analysis Machine learning algorithms social networking (online) scalability

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Early Classification of Lung Cancer based on Cell Morphology Features 1st

Early Classification of Lung Cancer Based on Cell Morphology...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： byeon, Haewon soni, Mukesh Deb, Nabamita Rivera, Richard sharma, Nilesh vijay Kaushal, Chetna Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Department of Information Technology Gauhati University Guwahati India Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Artificial Intelligence and Data Science SNJB′s Late Sau. K. B. Jain College of Engineering Nashik Chandwad India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

In response to the challenges of limited annotated lung cancer pathology images with complex cell morphology, we propose a lung cancer pathology image classification method based on contrastive learning of cell morphology features. This approach integrates highly reliable unlabelled data into the training data through contrastive learning to address the issue of insufficient labelled data. building upon the foundation of k-nearest neighbour contrastive learning, we introduce farthest and nearest neighbour contrastive learning, simultaneously using the farthest and nearest neighbour images for contrastive learning. This enhances the performance of contrastive learning by increasing the difficulty of positive sample learning and diversifying the dataset. We employ a ResNet50 encoder with deformable convolutions and dynamic convolutions to improve the extraction of cell morphology features. Experimental results demonstrate that, especially when labelled data is scarce, our classification method effectively leverages cell feature information from both labelled and unlabelled cancer pathology images, resulting in superior classification performance compared to existing methods. When applied to 80%, 60%, and 40% labeled data, respectively, CCCM outperforms other approaches with superior performance metrics, reaching F1 score of 0.9584, 0.9488, and 0.9386. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： Lung cancer

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Optimized bit Matrix based Power Aware Load Distribution Policy for Federated Cloud

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Procedia computer science 2020年 167卷 1771-1790页

作者： b s Rajeshwari M Dakshayini Assistant Professor Department of Computer Science and Engineering B M S College of Engineering Bangalore 560019 India Professor Department of Information Science and Engineering B M S College of Engineering Bangalore 560019 India

Federated Cloud is a next generation cloud computing model which works towards a new dynamic scenario in smart ecosystems. It consolidates individual clouds together to form single large cloud to satisfy all on-demand requirements of the users. Federated cloud is currently facing many challenges including: monitoring of individual cloud services, maintaining service level agreement, fair load distribution, power usage etc. Hence, to address these challenges this paper presents a brokerage model “ Optimized bit Matrix based Power Aware Load Distribution Policy for Federated Cloud (ObMPLP) ”. Presented model includes two novel concepts: bit Matrix and Load Distribution Factor. bit Matrix constructed by individual cloud members representing resource availability status and environment in executing the user’s request. Load Distribution Factor representing load distribution level at individual cloud. ObMPLP policy dispenses the incoming requests among multiple clouds by analyzing bits pattern and load distribution factor that not only performs fair load distribution among multiple clouds, but also improves response time, alleviate power consumption at each cloud and achieve better quality service. The performance of the proposed policy is evaluated and results demonstrate reduction in response time, optimized power consumption and fair distribution of load compared to existing approaches.

关键词： Cloud Computing Load Distribution Quality service Power Consumption Federated Cloud

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Reinforcement Learning Infused MAC for Adaptive Connectivity

Reinforcement Learning Infused MAC for Adaptive Connectivity

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IEEE Conference on Wireless Communications and Networking

作者： Dinesh Kumar sah Ali Nauman Muhammad Ali Jamshed Korhan Cengiz Nikola Ivković vedran Uroš Division of Networked and Embedded Systems Research Group Data Communication Mälardalens University Västerås Sweden Department of Information and Communication Engineering Yeungnam University Gyeongbuk Republic of Korea College of Science and Engineering University of Glasgow United Kingdom Department of Computer Engineering Istinye University Istanbul Turkey Department of Information Technologies Faculty of Informatics and Management University of Hradec Kralove Kralove Czech Republic Faculty of Organization and Informatics University of Zagreb Varaždin Croatia Polytechnic “Marko Marulić” in Knin Knin Croatia

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The beginning of cellular communication (next-gen, such as 5G and 6G) promises an extreme leap in connectivity, introducing intelligent, adaptive solutions that integrate communication, artificial intelligence, and emerging technologies. Our approach combines reinforcement learning with Medium Access Control (MAC) protocols to dynamically optimize resource allocation and enhance network performance. In this work, we explore the integration of the adaptive frame size adjusting approach similar to the IEEE 802.1Cb to ensure the efficient handling of seamless redundancy. The proposed solutions are validated through simulation, ensuring robustness and real-world applicability. Results indicate significant improvements in redundancy rate detection and delay in the network. This work contributes to achieving intelligent, adaptive, and seamless connectivity in the next generation of communication systems.

关键词： 6G mobile communication Adaptive systems Redundancy Reinforcement learning Media Access Protocol Throughput Resource management

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