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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Patidar, Mukesh Vekariya, Daxa Pradhan, Preeti Gupta, Kartik Patidar, Praveen Kumar Khan, Pirmohammad Parul University Parul Institute of Engineering and Technology Department of Computer Science & Engineering Gujarat Vadodara India Madhya Pradesh Indore India Acropolis Institute of Technology and Research Department of Computer Science and Engineering Madhya Pradesh Indore India Parul University Parul Institute of Technology Department of Computer Science & Engineering Gujarat Vadodara India

ISBN: (纸本)9798331518981

Price comparison websites (PCWs) have become integral to the online shopping experience, enabling consumers to make informed decisions by comparing prices, features, and reviews from multiple retailers. These platforms not only benefit consumers by saving time and money but also influence market competition by increasing pricing transparency and driving businesses to optimize their strategies. This research explores the evolution of PCWs, their technological infrastructure, and their business models. Moreover, it examines their impact on consumer behavior, the challenges they pose for retailers, and the future trends shaping the industry. Through an analysis of current practices, technological innovations, and economic effects, this work highlights the growing importance of PCWs in the global e-commerce ecosystem. © 2024 IEEE.

关键词： Marketplaces

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Sentiment Analysis of Movie Reviews Using Machine Learning

Sentiment Analysis of Movie Reviews Using Machine Learning

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Yadav, Avantika Garg, Payal Jain, Tarun Manipal University Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology and Management Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

This paper delves into the critical role that machine learning plays in the accurate classification of sentiments expressed in movie reviews. Throughout the study we research and apply NLP using deep learning and machine learning algorithms to train and test our IMDb 50K movie reviews dataset on different models and feature embedding to compare their accuracies and choose the most optimal model for sentimental analysis. Ultimately, using deep neural network model BERT neural gives us the best accuracy of 94.2% compared to lexicon-based approaches due to the limitations in feature extractions like Bag of Words and TF-IDF. © 2025 IEEE.

关键词： Bag of Words BERT Deep Learning IMDb Dataset Machine Learning Natural Language Processin Sentiment Analysis TF-IDF Training and Testing

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Deep Belief Network Optimization Using PSOGAA Algorithm for Efficient Crop Recommendation 5th

Deep Belief Network Optimization Using PSOGAA Algorithm for ...

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5th International Conference on Cognitive Computing and Information Processing, CCIP 2023

作者： Madhuri, J. Indiramma, M. Nagarathna, N. Department of Computer Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9783031607240

The agricultural sector plays a crucial role in the development of an emerging economy, providing employment to a significant portion of the population. Selecting the most suitable crops based on specific weather and soil conditions is vital to maximize yield and ensure profitability for farmers, contributing to global food security. In this context, this paper introduces a crop recommendation model utilizing Deep Belief Networks (DBN), fine-tuned by a hybrid optimization approach combining Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Adam optimization. To enhance the efficiency of DBN-based crop recommendation model, this paper proposes the PSOGAA algorithm, a novel hybrid optimization algorithm designed to optimize the DBN’s network structure. Subsequently, genetic operators featuring self-adjusting crossover and mutation probabilities are applied to further refine the PSO and explore a global optimization solution. Ultimately, this global optimization solution is employed to construct the network structure of the crop recommendation model. Experimental results demonstrate the superior performance of the proposed algorithm compared to other DBN optimization techniques, showcasing an average classification accuracy improvement of at least 2.3%. This emphasizes the effectiveness of the PSOGAA algorithm as an efficient DBN optimization technique. Focusing on four major crops in India—rice, sugarcane, maize, and finger millet—the crop recommendation model’s outcomes are compared with those of the DBN model optimized using alternative algorithms. The experimental findings highlight that the proposed hybrid optimization algorithm surpasses other optimizers, exhibiting enhanced accuracy in crop recommendations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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Empowering Consumers and Sustainability: A Global Review of Right to Repair Policies

Empowering Consumers and Sustainability: A Global Review of ...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Preethish Kumar, M. Usha, B.A. Basavaraj, G.N. Mohan, B.A. Prithvi, G. Rajkumar Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Nagarjuna College of Engineering and Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375442

The 'Right to Repair' (R2R) movement has gained significant attention in recent years with the aim empowering consumers to fix their products rather than replacing them when they become faulty. This paper offers a comprehensive analysis of the impact of R2R policies on consumer rights, environmental sustainability, economic outcomes, social implications, and technological advancement in the digital era. Further, this paper discusses the role of open repair ecosystems in fostering innovation, facilitating aftermarket services, and empowering informed consumer choices. It examines the legal and regulatory frameworks surrounding R2R, including recent legislative initiatives at multiple levels. The paper concludes by advocating for R2R as a fundamental aspect of consumer rights and environmental stewardship, highlighting its potential to promote sustainability, foster innovation, and create a more equitable economy. © 2024 IEEE.

关键词： Sustainable development goals

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Automated Disease Detection and Classification in Jackfruit Leaves: An Efficient Deep Learning-Based Approach 3rd

Automated Disease Detection and Classification in Jackfrui...

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3rd International Conference on Trends in Electronics and Health Informatics, TEHI 2023

作者： Saha, Dip Kumar Mahmud Joy, Md. Ashif Reza, Md. Rokonuzzaman Shovon, Reduanul Bari Department of Computer Science and Engineering American International University-Bangladesh Kuratoli Bangladesh Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering The International University of Scholars Dhaka Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819739363

Jackfruit is the national fruit of Bangladesh, and one of the most consumed fruits in India, Sri Lanka, Philippines, Indonesia, Malaysia, Australia, and many more countries. The every year due to diseases jackfruit production stays lower than the expectation. The early identification of these diseases is crucial for agricultural productivity, and our research showcases the effectiveness of deep learning techniques in addressing this issue. This study introduces an innovative approach to detect and classify two common jackfruit leaf diseases, algal spot and black spot, combining the YOLOv8 object detection model and the EfficientNetB7 pre-trained deep convolutional neural network model. The models trained on 6,360 images of those two diseases and our methodology achieved an accuracy of 99.9% on training and 99.5% on testing. This work represents a valuable contribution to agriculture, emphasizing the synergy between cutting-edge technology and age-old challenges, with the potential to positively impact the agricultural industry and global food security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Citrus fruits

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Enhanced Cuckoo Search Optimization Technique for Skin Cancer Diagnosis Application

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Intelligent Automation & Soft Computing 2023年第3期35卷 3403-3413页

作者： S.Ayshwarya Lakshmi K.Anandavelu Department of Computer Science and Engineering University College of Engineering Panruti607106TamilnaduIndia Department of Mechanical Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Skin cancer segmentation is a critical task in a clinical decision support system for skin cancer *** suggested enhanced cuckoo search based optimization model will be used to evaluate several metrics in the skin cancer pic-ture segmentation *** time and resources are always limited,the proposed enhanced cuckoo search optimization algorithm is one of the most effec-tive strategies for dealing with global optimization difﬁ*** of the most signiﬁcant requirements is to design optimal solutions to optimize their *** is no particular technique that can answer all optimization *** proposed enhanced cuckoo search optimization method indicates a constructive precision for skin cancer over with all image segmentation in computerized *** accuracy of the proposed enhanced cuckoo search based optimization for melanoma has increased with a 23%to 29%improvement than other optimization *** total sensitivity and speciﬁcity attained in the proposed system are 99.56%and 99.73%*** proposed method outperforms by offering accuracy of 99.26%in comparisons to other conventional *** proposed enhanced optimization technique achieved 98.75%,98.96%for Dice and Jaccard coefﬁ*** model trained using the suggested measure outperforms those trained using the conventional method in the segmentation of skin cancer picture data.

关键词： Cukoo search optimization technique ﬁtness function cancer

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Resource Profiling for Virtualized Radio Access Networks 18

Resource Profiling for Virtualized Radio Access Networks

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Tella, Rajashekhar Reddy Gupta, Utkarsh Venkateswarlu, Gudepu Chintapalli, Venkatarami Reddy Tamma, Bheemarjuna Reddy Kondepu, Koteswararao IIT Dharwad Department of Computer Science and Engineering Dharwad India National Institute of Technology Department of Computer Science and Engineering Kerala Calicut India IIT Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350391725

The mobile networks are rapidly evolving, transitioning from specialized hardware to fully virtualized platforms that run on commercial off-the-shelf (COTS) hardware powered by general-purpose processors. Mobile operators are increasingly adopting Network Functions Virtualization (NFV) to take advantage of virtualization benefits, such as ease of deployment, flexibility, and cost savings. A crucial component of this transformation is the implementation of virtualized Radio Access Network (vRAN) solutions, which provide a cost-effective way to deploy the 5G and beyond RAN as a containerized network function (CNF) on COTS hardware. vRAN is more efficient than traditional RAN, as it allows multiple base station workloads to be multiplexed on the same hardware, potentially co-located with other workloads. However, ensuring consistent performance of vRAN workloads in this consolidated environment is challenging due to the high variability and unpredictability arising from contentions for shared system resources such as CPU cores, Last Level Cache (LLC), and Memory Bandwidth. Thus, there is a strong need to understand the vRAN components if they are affected by any system resource. This paper presents an understanding of how the next-generation scalable processors can be used to run the vRAN components and presents a detailed analysis of the controllable knobs such as CPU, LLC, Memory Bandwidth, and energy consumption. The proposed vRAN profiling shows that the observed throughput varies significantly based on the number of pinned CPU cores (e.g., up to 250 {Mbits} / {sec}), and also similar performance observed with other system resources - LLC allocation and Memory Bandwidth. © 2024 IEEE.

关键词： Radio access networks

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Enhancing User Experience and Integrating Passwordless FIDO-Based Authentication through Bluetooth Low Energy (BLE) 3

Enhancing User Experience and Integrating Passwordless FIDO-...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Rathi, Snehal Oswal, Harsh D'Souza, Ansfred Pratham, Shaantanu Khedkar, Vijayshri BRACTs Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Department of Computer Science & Engineering Pune India

ISBN: (纸本)9798350354348

Traditional password-based authentication methods confront several issues such as security weaknesses, user inconvenience, and susceptibility to various attacks, e.g. phishing. These issues are solved with newly created industry standards such as Web Authentication, CTAP (Client to Authenticator Protocol), FIDO (Fast Identity Online), and W3C guidelines that deliver reliable and interoperable authentication solutions. This research explores how the FIDO, Web Authentication, CTAP, and W3C standards can be blended to revolutionize authentication practices. The proposed method should increase the security and usability of the authentication process without requiring passwords. Our technology facilitates this by establishing secure connections over Bluetooth, offering seamless UX - between a mobile device (in your pocket) and single desktop while maintaining the strongest security postures through structured authentication experiences. © 2024 IEEE.

关键词： Authentication Protocol

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An Efficient Method to Deal with Missing Values and Class Imbalance in Healthcare Data 5th

An Efficient Method to Deal with Missing Values and Class Im...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Singh, Harpreet Singh, Birmohan Kaur, Manpreet Rani, Suvita Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India Department of Electrical and Instrumentation Engineering Sant Longowal Institute of Engineering and Technology Sangrur Longowal India

ISBN: (纸本)9789819773701

The present era belongs to the age of digital devices, where everything is going to be digitized. This makes the massive production of data at faster rates and brings Big Data to light. The arrival of big data has influenced and reformed many domains like Healthcare, Bioinformatics, Agriculture, etc. Healthcare big data has gained the attention of various researchers as it aids in the early diagnosis and prediction of diseases. Generally, real-world healthcare data contains missingness and imbalance distribution of classes which makes classification a challenging problem. Therefore, an efficient method is introduced to deal with the problems of data incompleteness and class imbalance in healthcare data. A class mean and standard deviation-based imputation strategy is proposed to impute the missing values within the region of a particular class. Further, an Improved Elephant Herding Optimization (IEHO) algorithm is utilized to apply the optimal weights for improving the imbalanced learning in healthcare data. A new fitness function is proposed which considers the data missingness and class imbalance along with the feature weighting. The optimized k-Nearest Neighbor (k-NN) learning algorithm is used to perform the classification. The performance of the proposed method is evaluated on four healthcare datasets: Pima diabetes, Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival. A comparative analysis with state-of-the-art methods is performed based on four-performance metrics: accuracy, balanced accuracy, g-mean, and f-score. The proposed method obtained higher classification accuracy for Wisconsin Breast Cancer (Original), Hepatitis, and HCC Survival datasets than state-of-the-art methods. Experimental results validate the effectiveness of the proposed method in dealing with the missing values and class imbalance problems in healthcare data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An Optimal Weighted Ensemble of 3D CNNs for Early Diagnosis of Alzheimer’s Disease

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SN computer science 2024年第2期5卷 252页

作者： Dharwada, Sriram Tembhurne, Jitendra Diwan, Tausif Department of Computer Science and Automation Indian Institute of Science Bangalore India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur 441108 India

Alzheimer disease (AD) is a chronic neurological disorder in which the loss of brain cells causes dementia. Early and accurate diagnosis of AD will lead to better treatment of the disease before irreversible brain damage has been occurred. This paper proposes the classification of Alzheimer's disease using 3D structural Magnetic Resonance Imaging (sMRI) images through 3D convolutional neural networks (CNNs). Most existing methods utilizing 3D subject-level CNNs for Alzheimer's disease classification design a single model which relies on a very large training dataset for improved generalization. Herein, we address this issue through 3D transfer learning which makes use of knowledge gained from a pre-trained task. We train 3D versions of five classical 2D image classification architectures—ResNet, ResNeXt, SE-ResNet, SE-ResNeXt, and SE-Net—by initializing each model with pre-trained weights from their 2D counterparts, and combine their predictions through a weighted average method. The weights assigned to each model of the ensemble are optimized to achieve a performance better than any single 3D CNN model. With a relatively smaller training dataset, the proposed model obtains 97.27%, 82.33%, 90.41%, 84.22%, 84.26%, and 77.1% accuracies for the Alzheimer’s disease (AD) versus cognitively normal (CN), early mild cognitive impairment (EMCI) versus CN, late mild cognitive impairment (LMCI) versus CN, EMCI versus AD, LMCI versus AD, and EMCI versus LMCI classification tasks, outperforming current state-of-the-art methods, and indicating the effectiveness of our proposed model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Alzheimer’s disease Convolutional neural networks Ensemble learning Transfer learning

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