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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Chauhan, Naman Revathy, S. Murali, P. Basker, N. Saranya, S. Natarajan, B. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Electronics and Communication Engg. Chennai Ramapuram India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engg. Chennai India Sona College of Technology Department of Computer Science and Engg. Salem India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India School of Computer Science and Engg Vellore Institue of Technology Chennai Campus India

ISBN: (纸本)9798350373110

People in the modern era are increasingly concerned with their diet and food choices to prevent developing chronic diseases like high blood pressure and diabetes. With the increasing usage of smartphones and technology, an application to automatically monitor their diet would be a great boon, especially for the younger generation. This research work investigates the process of estimating the calories present in the food images. This research work was carried out in three phases. The first phase involves the categorization of food images, such as boiled, fried in oil, or junk. This research work introduces a novel deep neural model by incorporating the MobileNet-V2 framework. In the second phase, the quantity of the labelled food is estimated. Further, it involves computing the calories present in the food using the Google search API. The details of the ingredients present in each type of food are used for this purpose. Classification accuracy, food amount calculation precision, and calorie estimation accuracy are used to evaluate the workings of the proposed model. This research work has secured plausible results with an overall accuracy of 95.21%, comparatively better than the existing approaches. © 2024 IEEE.

关键词： Calorie Estimation Deep Learning Google Search API Image Classification MobileNet-V2 Neural Network

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Deep Transfer Learning Models for Mobile-Based Ocular Disorder Identification on Retinal Images

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computers, Materials & Continua 2024年第7期80卷 139-161页

作者： Roseline Oluwaseun Ogundokun Joseph Bamidele Awotunde Hakeem Babalola Akande Cheng-Chi Lee Agbotiname Lucky Imoize Department of Multimedia Engineering Kaunas University of TechnologyKaunas44249Lithuania Department of Computer Science Landmark UniversityOmu Aran251103Nigeria Department of Computer Science Faculty of Information and Communication SciencesUniversity of IlorinIlorin240003Nigeria Department of Telecommunication Science University of IlorinIlorin230003Nigeria Department of Library and Information Science Fu Jen Catholic UniversityNew Taipei City24205TaiwanChina Department of Computer Science and Information Engineering Fintech and Blockchain Research CenterAsia UniversityTaichung City41354TaiwanChina Department of Electrical and Electronics Engineering Faculty of EngineeringUniversity of LagosAkokaLagos100213Nigeria Department of Electrical Engineering and Information Technology Institute of Digital CommunicationRuhr UniversityBochum44801Germany

Mobile technology is developing *** phone technologies have been integrated into the healthcare industry to help medical ***,computer vision models focus on image detection and classification ***2 is a computer vision model that performs well on mobile devices,but it requires cloud services to process biometric image information and provide predictions to *** leads to increased *** biometrics image datasets on mobile devices will make the prediction faster,but mobiles are resource-restricted devices in terms of storage,power,and computational ***,a model that is small in size,efficient,and has good prediction quality for biometrics image classification problems is *** pre-trained CNN(PCNN)MobileNetV2 architecture combined with a Support Vector Machine(SVM)compacts the model representation and reduces the computational cost and memory *** proposed novel approach combines quantized pre-trained CNN(PCNN)MobileNetV2 architecture with a Support Vector Machine(SVM)to represent models efficiently with low computational cost and *** contributions include evaluating three CNN models for ocular disease identification in transfer learning and deep feature plus SVM approaches,showing the superiority of deep features from MobileNetV2 and SVM classification models,comparing traditional methods,exploring six ocular diseases and normal classification with 20,111 images postdata augmentation,and reducing the number of trainable *** model is trained on ocular disorder retinal fundus image datasets according to the severity of six age-related macular degeneration(AMD),one of the most common eye illnesses,Cataract,Diabetes,Glaucoma,Hypertension,andMyopia with one class *** the experiment outcomes,it is observed that the suggested MobileNetV2-SVM model size is *** testing accuracy for MobileNetV2-SVM,InceptionV3,and MobileNetV2 is 90.11%,86.88%,a

关键词： Retinal images ocular disorder deep transfer learning disease identification mobile device

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Blockchain Fundraising and Charity Platform 2

Blockchain Fundraising and Charity Platform

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Govindarajan, Sreevatsan Rajkumar, Sujeeth Sundarajan Mayan, Albert Posonia, A.Mary Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350347982

Charity is regarded as a moral imperative throughout the world, and enormous sums of money are spent in its name. In most situations, the charity collection systems are not clear, and owing to this, charitable organisations struggle to acquire contributors' confidence and attention. This study introduces a crowdfunding platform built on the blockchain to increase the transparency of the fundraising process and grant makers' expenses and to overcome the drawbacks that exist in the present distribution system. Donations can only be withdrawn by the people or organizations that organized the charity event, enabling registered charities to post their identities for investor verification. Since no single entity is in charge of the blockchain for charities and fundraising, blockchain technology can help prevent data breaches and promote openness. The platform serves as a gateway for fundraising and charitable work. © 2023 IEEE.

关键词： Transparency

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Enhanced memory window and efficient resistive switching in stabilized BaTiO3-based RRAM through incorporation of Al2O3 interlayer

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材料科学技术（英文版） 2025年第10期213卷 125-134页

作者： Akendra Singh Chabungbam Minjae Kim Atul Thakre Dong-eun Kim Hyung-Ho Park Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCAUSA Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Centre of Functional Materials Vellore Institute of TechnologyTamil Nadu 632014India Department of Materials Science and Engineering Yonsei UniversitySeoul 03722Korea Aerogel Materials Research Center Yonsei UniversitySeoul 03722Korea

As artificial intelligence and big data become increasingly prevalent,resistive random-access memory(RRAM)has become one of the most promising alternatives for storing massive amounts of *** this study,we employed high-quality crystalline TiN/Al2O3/BaTiO3/Pt RRAM with an optimized thin Al2O3 in-terlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the inter-layer affects the memory window *** insertion of the Al2O3 interlayer,the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in ***,the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magni-tude,respectively,thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude,*** device also exhibits multilevel resistive switching behavior when varying the applied ***,we also developed a 6 × 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal ***,our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.

关键词： Resistive random-access memory Resistive switching Atomic layer deposition Al2O3 interlayer

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Blend the Separated: Mixture of Synergistic Experts for Data-Scarcity Drug-Target Interaction Prediction 39

Blend the Separated: Mixture of Synergistic Experts for Data...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhai, Xinlong Wang, Chunchen Wang, Ruijia Kang, Jiazheng Li, Shujie Chen, Boyu Ma, Tengfei Zhou, Zikai Yang, Cheng Shi, Chuan Beijing University of Posts and Telecommunications China China Telecom Cloud Computing Research Institute China College of Computer Science and Electronic Engineering Hunan University China

ISBN: (纸本)157735897X

Drug-target interaction prediction (DTI) is essential in various applications including drug discovery and clinical application. There are two perspectives of input data widely used in DTI prediction: Intrinsic data represents how drugs or targets are constructed, and extrinsic data represents how drugs or targets are related to other biological entities. However, any of the two perspectives of input data can be scarce for some drugs or targets, especially for those unpopular or newly discovered. Furthermore, ground-truth labels for specific interaction types can also be scarce. Therefore, we propose the first method to tackle DTI prediction under input data and/or label scarcity. To make our model functional when only one perspective of input data is available, we design two separate experts to process intrinsic and extrinsic data respectively and fuse them adaptively according to different samples. Furthermore, to make the two perspectives complement each other and remedy label scarcity, two experts synergize with each other in a mutually supervised way to exploit the enormous unlabeled data. Extensive experiments on 3 real-world datasets under different extents of input data scarcity and/or label scarcity demonstrate our model outperforms states of the art significantly and steadily, with a maximum improvement of 53.53%. We also test our model without any data scarcity and it still outperforms current methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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High Accurate Gold Prize Rate Prediction Using Random Forest Regression Algorithm 7

High Accurate Gold Prize Rate Prediction Using Random Forest...

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7th IET Smart Cities Symposium, SCS 2023

作者： Sivasangari, A. Deepa, R. Geetha Rani, K. Surendran, R. Tamilvizhi, T. School of Computing Sathyabama Institute of Science and Technology Chennai India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Bangalore India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai600123 India

ISBN: (纸本)9781839539831

In the past, one of the precious resources utilized to fund trading purchases was gold. The nations that have gold resources are regarded as rich countries. The nation's gold reserves show how strong its economy is. Not only have government departments participated in gold reserves, but also a number of private individuals and businesses. The routine of the world's top financial prudence affects gold rates. An increase in investors has been drawn to gold due to the recent fluctuations in price. Transactions involving gold are risky, and price fluctuations in gold are unpredictable due to the unpredictability of the gold market. The suggested uses machine learning to forecast the price of gold. Generally, machine learning is used to predict economic variables. To train a prediction model, we use supervised learning algorithms such as multiple linear regression and random forest regression. © The Institution of engineering & Technology 2023.

关键词： Multiple linear regression

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Information Centric Networking Based Cooperative Caching Framework for 5G Communication Systems

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computers, Materials & Continua 2024年第9期80卷 3945-3966页

作者： R.Mahaveerakannan Thanarajan Tamilvizhi Sonia Jenifer Rayen Osamah Ibrahim Khalaf Habib Hamam Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Solar Energy Al-Nahrain Research Center for Renewable EnergyAl-Nahrain UniversityJadriyaBaghdad64074Iraq Faculty of Engineering Universitéde MonctonNew BrunswickE1A3E9Canada Hodmas University College Mogadishu521376Somalia Bridges for Academic Excellence Tunis1002Tunisia School of Electrical Engineering University of JohannesburgAuckland ParkJohannesburg2006South Africa

The demands on conventional communication networks are increasing rapidly because of the exponential expansion of connected multimedia *** light of the data-centric aspect of contemporary communication,the information-centric network(ICN)paradigm offers hope for a solution by emphasizing content retrieval by name instead of *** 5G networks are to meet the expected data demand surge from expanded connectivity and Internet of Things(IoT)devices,then effective caching solutions will be required tomaximize network throughput andminimize the use of ***,an ICN-based Cooperative Caching(ICN-CoC)technique has been used to select a cache by considering cache position,content attractiveness,and rate *** findings show that utilizing our suggested approach improves caching regarding the Cache Hit Ratio(CHR)of 84.3%,Average Hop Minimization Ratio(AHMR)of 89.5%,and Mean Access Latency(MAL)of 0.4 *** a framework,it suggests improved caching strategies to handle the difficulty of effectively controlling data consumption in 5G *** improvements aim to make the network run more smoothly by enhancing content delivery,decreasing latency,and relieving *** improving 5G communication systems’capacity tomanage the demands faced by modern data-centric applications,the research ultimately aids in advancement.

关键词： Information-centric networking caching schemes 5G communication non-negative matrix factorization(NMF) weighted clustering algorithm

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Highest Accuracy Based Automated Depression Prediction Using Natural Language Processing 1

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9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Tharun, S.V. Saranya, G. Tamilvizhi, T. Surendran, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

In today's fast-paced society, psychological health issues such as anxiety, depression, and stress have become prevalent among the general population. researchers have explored the use of machine learning algorithms to predict the likelihood of depression in individuals. As datasets related to depression become more abundant and machine learning technology advances, there is an opportunity to develop intelligent systems capable of identifying symptoms of depression in written material. By applying natural language processing and machine learning algorithms to analyze written text, such as social media posts, emails, and chat messages, researchers can potentially identify patterns and linguistic cues associated with depression. These patterns may include changes in word usage, tone, and sentiment. The dataset consists of text-based questions on this information channel. At present, machine learning techniques are highly effective for analyzing data and identifying problems. researchers have conducted comparisons of the accuracy achieved by different machine learning algorithms using the complete set of attributes as well as a subset of selected attributes. In summary, while the potential for AI to aid in mental health diagnosis and treatment is exciting, it's important to proceed with care and consideration for the complexities of the field and the needs of patients. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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