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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Sasikala, C. Ramya, S. RajeshKumar, S. Ramachandramoorthy, K.B. VijayaKumar, S. Umapathi, K. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

Fake reviews are a growing concern for e-commerce websites and other online platforms. To tackle this issue, researchers have developed an advanced convolutional neural network system that can detect and classify fake reviews. This system has been specifically designed to analyze the Amazon review dataset, and by integrating various text pre-processing methods and an innovative attention mechanism, it has significantly improved its classification accuracy. In fact, the proposed model outperforms existing state-of-the-art techniques in terms of accuracy and F1-score, with an impressive accuracy of 0.97. These results demonstrate the effectiveness of the improved CNN model in accurately detecting and classifying fake reviews, and provide a valuable tool for maintaining the authenticity and trustworthiness of online reviews. © 2023 IEEE.

关键词： Convolutional neural networks

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Blockchain based System for Secure Property Registration

Blockchain based System for Secure Property Registration

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Mehta, Nishu Rani, Rinkle Kalra, Nidhi Thapar Institute of Engineering and Technology Computer Science and Engineering Department Patiala India

ISBN: (纸本)9798350333602

The procedure of registering a property involves paying stamp duty, and registering the sales deed for the property you have purchased. Property registration is done at the office of the sub-registrar who has jurisdiction over the area in which you purchased it. Land/property registration is a long process necessitating numerous paper checks, in-person meetings, and cash exchanges, which results in significant delays and is both time- and money-consuming. In India, currently government is practicing the paper-based land registration system which raises significant problems like high time intricacy, centralized authority, physical visit to property site for authentication, high cost of transactions, proneness to human error, frauds involving property documents, lack of communication, third-party participation, ineffectiveness, timing issues and issue with ownership. These issues are getting worse every day;hence the registry procedure system needs to be improved. In this study, to overcome such challenges we will develop a tamper-proof, fraud-resistant, automated Blockchain - based system to create, issue, securely store, share and verify the Land Registration Records. The proposed system will also provide a digital locker all the stakeholders for keeping land record certificates. All the actions taken by different entities involved in the land record registration will be time-stamped, verified by the different nodes, and stored as a block which has a unique hash value, so that neither the hackers/spammers nor the insiders can issue a counterfeit certificate on a later date or modify/tamper the original certificate. © 2023 IEEE.

关键词： Smart contract

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Artificial Intelligence-Enabled Cooperative Cluster-Based Data Collection for Unmanned Aerial Vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.S.S.Anupama G.Jose Moses E.Laxmi Lydia Seifedine Kadry Sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial Vehicles(UAVs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAV networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAVs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAV)in 6G *** proposed AECCDC-UAV technique purposes for dividing the UAV network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAV technique involves a quasi-oppositional shuffled shepherd optimization(QOSSO)algorithm for selecting the CHs and construct *** QOSSO algorithm derives a fitness function involving three input parameters residual energy of UAVs,distance to neighboring UAVs,and degree of *** performance of the AECCDC-UAV technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Green Cloud Computing: A Sustainable Energy-Efficiency Approach for Business Rapidity and the Environment 6th

Green Cloud Computing: A Sustainable Energy-Efficiency Appro...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Bristy, Sadia Sultana Azam, Tooba Islam, Mukta Marion Rahman, Raisur Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031501500

Cloud computing is significantly assisting in addressing IT issues. To lessen the environmental impact, a thorough investigation of cloud computing and energy-efficient algorithms is required. A green cloud computing strategy aims to build an environment for cloud computing that is more energy-efficient, maximizes resource consumption without overprovisioning, leaves fewer carbon footprints, reduces Greenhouse Gas emissions, and minimizes E-wastages. Some features and techniques that Green Cloud Computing focuses on bringing to the cloud environment include containerization, designing green data center facility infrastructure, server consolidation, virtual machine placement, designing green networks and protocols, green data management, sustainable software development, and safe disposal techniques with the three R’s (Reuse, Reduce and Recycle). This paper offers a thorough examination of green cloud computing and its traits. The main objective of this study is to investigate green cloud computing, including its various components and conversion processes, as well as to compare energy-efficient VM placement techniques for green cloud computing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy efficiency

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Comparative Analysis of Transfer Learning Strategies for Automated Identification of Bone Marrow Cell Morphologies 14

Comparative Analysis of Transfer Learning Strategies for Aut...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Ala Walid, Md. Abul Shill, Pintu Chandra Mahmud, Sm Tamim Khulna University of Engineering & Technology Department of Computer Science & Engineering Khulna9203 Bangladesh Northern University of Business and Technology Khulna Department of Computer Science & Engineering Khulna9100 Bangladesh

ISBN: (纸本)9798350335095

The gradual decrease in the average lifespan of mankind is considered to be caused by various diseases, along with hematological disorders are prominent. Diagnosis of this hematological disorder requires an understanding of the morphology of bone marrow cells. This study introduces a computer vision-aided solution in order to perform an automated evaluation of bone marrow smears from the examiner's skill and experience. In this regard, we employ the publicly available dataset of 170,000 expert-annotated cell images from the bone marrow smears with twenty-one separate groups. A comparative study of the models developed from our dataset has been performed using eleven customized deep-transfer learning algorithms. The EfficientNetB3 outperforms other existing models. We have achieved the maximum accuracy of 87.2% by EfficientNetB3 compared with other deep learning models. Similarly, the recall in EfficientNetB3 was 87.22%, the precision was 87.11%, and the F1 score was 87.15%. Conversely, DenseNet201 offers the second-most effectiveness, with an accuracy of 86.32%. © 2023 IEEE.

关键词： Diagnosis

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Deep Retinal Image Analysis and Classification Using Deer Hunting Optimization-Based Tandem Pulse Coupled Neural Network

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Journal of The Institution of Engineers (India): Series B 2022年第6期103卷 1909-1916页

作者： Vinayaki, V. Desika Kalaiselvi, R. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Gummidipoondi India

Retinal eye diseases lead to vision loss and visual deficiency. Various kinds of human eye diseases are arteriosclerosis, diabetic retinopathy, hypertension, and glaucoma. Diabetic retinopathy is the form of the injured retina which is occurred by diabetes. The improper treatment without proper observations leads to permanent blindness. Therefore, it is necessary to detect the disease at an earlier stage to protect their vision. The main aim of this research is to detect diabetic retinopathy early from the fundus images. The proposed approach detects diabetic retinopathy through four major stages, namely pre-processing, segmentation, feature extraction, and classification. The proposed classifier uses the deep neural network for classifying the diabetic retinopathy (DR) infected images and normal images. Here, the proposed approach utilizes features such as coherence, edge features, shape features, local binary pattern, and Gray Level Co-occurrence Matrix (GLCM) features from the segmented output. The performance measures like sensitivity, specificity, accuracy, precision, F1-measure and processing time are utilized for the estimation of the proposed classifier and compared to other approaches. From the performance evaluation, it is noted that the proposed diabetic retinopathy detection has obtained an accuracy of 99.42%, a sensitivity of 98.84%, and a specificity of 96.49% that outperforming other state-of-art methods. © 2022, The Institution of Engineers (India).

关键词： Local binary pattern

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LIME-RAY: What Does a Neural Network See from X-Rays? 2

LIME-RAY: What Does a Neural Network See from X-Rays?

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Datta, Ayon Al Wazed Wasi, Wasif Muntasir, Fahim Nafis, Mahdee Department of Electrical and Electronic Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Department of Electrical and Electronic Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9798331508227

In the field of medical imaging, neural networks have significantly enhanced the analysis and interpretation of X-ray images, providing advanced capabilities in detecting patterns and anomalies. Despite their potential, challenges remain, particularly concerning the interpretability and reliability of these models. The LIME-RAY approach addresses these challenges by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. This hybrid technique leverages CNNs for spatial feature extraction and LSTMs for temporal pattern recognition, improving the accuracy and interpretability of the model’s predictions. Furthermore, Local Interpretable Model-agnostic Explanations (LIME) are used to provide transparent, interpretable results, crucial for gaining the trust of healthcare professionals. Our approach is validated using a diverse dataset of chest X-rays, including cases of Covid-19, Pneumonia, and Tuberculosis. The proposed method demonstrates superior performance in multi-class disease classification and offers valuable insights into the decision-making process, bridging the gap between complex neural network models and practical healthcare applications. ©2024 IEEE.

关键词： Convolutional neural networks

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Market Segmentation Using Machine Learning and Data Analysis: A Comprehensive Study 15

Market Segmentation Using Machine Learning and Data Analysis...

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

作者： Singh, Ajay Dhanda, Namrata Tiwari, Sharda Verma, Rajat Amity University Uttar Pradesh Amity School of Engineering and Technology Lucknow India Babu Banarasi Das Northern India Institute of Technology Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350370249

This research explores the integration of machine learning into market segmentation, a strategy traditionally reliant on demographic, geographical, and psychographic traits. With the rise of big data, machine learning offers enhanced precision by uncovering intricate consumer behavior patterns often missed by conventional methods. The study involves reviewing relevant literature, preprocessing datasets, and developing machine learning models to evaluate their effectiveness in segmenting markets. Findings indicate that machine learning significantly improves segmentation accuracy, enabling businesses to predict behaviors and tailor marketing strategies more effectively. This research contributes by highlighting machine learning's potential to transform market segmentation and drive business growth. Future work should focus on real-world implementation of these models to validate their practicality and impact. © 2024 IEEE.

关键词： consumer behavior customer insights machine learning market segmentation predictive analytics targeted marketing

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Prediction of material property using optimized augmented graph-attention layer in GNN

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Materials Today: Proceedings 2022年 69卷 1419-1424页

作者： Sathana, V. Mathumathi, M. Makanyadevi, K. Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy India Department of Computer Science and Engineering K.Ramakrishnan College of Technology Trichy India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur India

Research in materials science continues to focus on the development of ML models capable of accurately predicting material properties (MPP). Graph neural networks (GNNs) have also proven to be quite actual in extracting physicochemical properties from MPP, despite the fact that many other techniques have been offered in the past. Despite this, existing GNN models fail to distinguish between the contributions of various atoms adequately. To forecast the properties of inorganic materials, we have developed an upgraded graph neural network model dubbed IGAT-GNN. OAGAT and a global attention layer, where a meta-heuristic method is used to achieve optimal weight in OAGAT, are the distinguishing features of IGAT-GNN. Each of the two methods, OAGAT layers and global attention layers, is used to better understand the local relationships among nearby atoms and their overall influence to the material's stuff. This results in much improved prediction performance for various qualities that have been examined. As we have demonstrated through numerous experiments, the IGAT-GNN outperforms other currently available GNN models while also revealing new information about the relationship between atoms and their respective material properties. The proposed model alewives the accuracy of 99.95%, which is the high quality accuracy than other algorithm. © 2022

关键词： Machine learning

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Sentirec - A Sentiment Analysis with Recommendation System 8

Sentirec - A Sentiment Analysis with Recommendation System

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Oswalt Manoj, S. Catherine Chandralekha, T. Dharshini, R.M. Hemadharsini, P. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350396638

The standard language is assessed, and the feelings transmitted by the individual are brought up. The purpose of sentiment analysis is to determine the polarity of a person's textual opinion. Most of the people use social media to market ourselves and share all knowledge, thoughts, ideas, and experiences with the rest of the world. Sentiment analysis has been studied by several internet companies that offer materials based on human emotions expressed through social media. Many people use social media to post and share their thoughts and feelings. The sentiment analysis standard assorts a sentence with all forms of emotions, each of which has an intensity. This study includes a sentiment analysis of diverse perspectives on social topics that are trending on social media, along with a recommendation system. This method recommends related content to viewers based on their current statement or issue. The algorithm also suggests customized video sets to users based on their social media activity, such as diverse perspectives on a particular social problem about which they actively express their thoughts on social media. NLP algorithms are utilized for sentiment analysis and recommendation systems. © 2023 IEEE.

关键词： Recommender systems

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