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SA-MSVM:Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

computer Systems Science & engineering 2023年第3期44卷 2439-2456页

作者： C.P.Thamil Selvi R.PushpaLaksmi Department of Computer science and Engineering Sri Ranganathar Institute of Engineering and TechnologyCoimbatoreTamilnadu641009India Department of Information and Technology PSNA College of Engineering and TechnologyDindigulTamilnadu624622India

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata *** is created from social websites like Facebook,WhatsApp,Twitter,*** about products,persons,initiatives,political issues,research achievements,and entertainment are discussed on social *** unique data analytics method cannot be applied to various social websites since the data formats are *** approaches,techniques,and tools have been used for big data analytics,opinion mining,or sentiment analysis,but the accuracy is yet to be *** proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine(SA-MSVM)***-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing(NLP)process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers ***-MSVM is implemented,experimented with MATLAB,and the results are *** results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine(SVM)***-MSVM has obtained 96.34%accuracy in classifying the product review compared with the existing systems.

关键词： Bigdata analytics Twitter dataset for cloth product heuristic approaches sentiment analysis feature selection classification

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Machine Learning for Forecasting Depression and Anxiety in University Students 1

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8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Biswas, Tamal Bhattacharya, Diptendu Rudrapal, Dwijen Roy, Srijan National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India M Tech Student National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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LED-Based Adaptive Algorithm: A Novel Integration of Logit and Exponential Decay for Self-Organizing Sequential Search Problem 5

LED-Based Adaptive Algorithm: A Novel Integration of Logit a...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Baisakh Swain, Bishnu Prasad Patel, Nileshkumar Kalia, Sarthak Gmr Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Rajam India Sambalpur University Institute of Information Technology Department of Computer Science and Engineering Odisha Sambalpur India Nirma University School Of Technology Computer Science and Engineering Department Gujarat Ahmedabad India

ISBN: (纸本)9798331522100

This research presents an innovative adaptive list reorganization strategy aimed at minimizing the total access cost for dynamic sequences. The proposed algorithm leverages a dual-parameter approach combining Logit and Exponential Decay (LED) model for the self-organizing linear list problem, also known as the list update problem. The proposed method presented in this article, is one of the few existing approaches that offers an alternative to deterministic strategies, specifically designed to handle random input sequences for this *** strategy significantly improves performance compared to deterministic algorithms like Move-to-Front (MTF) and Move-to-Middle (MTM), which fail to account for varying patterns of locality and frequency in request sequences. The comparative analysis is performed with the well-known deterministic list update algorithms such as MTF and MFM by considering the Calgary Corpus standard dataset. The experimental results show that the adaptive reorganization strategy outperformed both algorithms. © 2025 IEEE.

关键词： Adaptive algorithms

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Breast-NET: a lightweight DCNN model for breast cancer detection and grading using histological samples

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Neural Computing and Applications 2024年第32期36卷 20067-20087页

作者： Saha, Mousumi Chakraborty, Mainak Maiti, Suchismita Das, Deepanwita Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur713209 India School of Innovation Design and Engineering Division of Intelligent Future Technologies Malardalen University Västerås Sweden Department of Information Technology Narula Institute of Technology West Bengal Kolkata700109 India Department of Computer Science and Engineering Narula Institute of Technology West Bengal Kolkata700109 India

Breast cancer is a prevalent and highly lethal cancer affecting women globally. While non-invasive techniques like ultrasound and mammogram are used for diagnosis, histological examination after biopsy is considered the gold standard. However, manual examination of tissues for abnormality is labor-intensive, expensive, and requires prior domain knowledge. Early detection, awareness, and access to specialized medical infrastructure in resource-constrained and remote areas are significant challenges but crucial for saving lives. In recent years, deep learning-based approaches have shown promising results in breast cancer detection, facilitated by advancements in GPU memory, computation power, and the availability of digital data. Motivated by these observations, we propose the Breast-NET deep convolutional neural network model for breast cancer detection and grading using histological images. Our model’s performance is evaluated on the BreakHis dataset, and we demonstrate its generalization ability on the Invasive Ductal Carcinoma (IDC) grading and IDC datasets. Extensive experimental and statistical performance analysis, along with an ablation study, validates the efficiency of our proposed model. Furthermore, we demonstrate the effectiveness of transfer learning with seven pre-trained convolutional neural networks for breast cancer detection and grading. Experimental results show that our framework outperforms state-of-the-art approaches in terms of accuracy, space, and computational complexity for the BreakHis, IDC grading, and IDC datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Grading

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Features enhancement of micro-service based cab booking system using block-chain 4th

Features enhancement of micro-service based cab booking syst...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Rohit Singh, Neelaksh Shukla, Riya Singh, Ashutosh Kumar Department of Computer Science and Engineering Institute of Engineering and Rural Technology Prayagraj India Department of Computer Science and Engineering United College of Engineering and Research Prayagraj India

ISBN: (纸本)9781032911571

Cab booking services help people order taxis. Existing cab booking services use client server-based architecture. The paper gives a study of the architecture and workings of the Uber cab booking website (Dissanayake, March 18, 2021). It has a micro-service based architecture (Li et al. 2021). Load balancing (Al-Jaroodi et al. 2012) and stream processing (Besnard et al. 2013) are used to handle a large number of requests. The workload is divided among servers using Dispatch Optimization (Abid et al. 2022). It ensures fast responses, easy connection minimal downtime, and efficient use of resources. However, the use of servers increases the cost of service. Rider and driver depend on the server to connect. They don’t know if the system is charging them fairly. It reduces transparency and trust. The paper proposes a blockchain-based cab booking application to solve some of the problems. Trust, transparency, and decentralization can be ensured using blockchain. Transparent and immutable ledgers of blockchain ensure trust and reduce reliance on intermediaries. Smart contracts govern ride management, optimizing efficiency. Blockchain capabilities are used to ensure fair rides, direct interaction of passengers with drivers, and automated payments. The proposed system aims at combining existing technology with blockchain solution to ensure security and transparency along with scalability and quick response time. © 2025 the Author(s).

关键词： Reservation systems

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Bézier cubics’ agreement with the neural network of the TEC map

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Soft Computing 2024年第19期28卷 10835-10852页

作者： Eroglu, Emre Research Department Armoya Institute of Technology Besiktas Istanbul34337 Turkey Department of Computer Engineering Epoka University Tirana1032 Albania

The Bézier curves submitted by Pierre Bézier in the mid-1950s are splendid differential geometric structures. The paper compares a mechanical–theoretical curve (classical-conventional) with a computer-based artificial neural network (modern). While the reader witnesses hourly TEC (TECU) modeling by the class C0 Bézier structure for the first time, (s)he has the opportunity to evaluate the reliable results of segmented-continuous curves with network. The Bézier structure is established by operating the differential geometrical invariants. The yielding and accuracy of the models are evaluated with the R correlation coefficient and the mean squared error. The discussion demonstrates the harmony of the two different approaches by modeling the 365-day TEC map of 2017. Then, in particular, the outputs of the intense geomagnetic storms of 28 May (Dst = − 125 nT) and 8 September (Dst = − 122 nT) are modeled and compared. Bézier curves are read segmental-continuous every 12-h TEC map in all discussions. The network, on the other hand, employs solar wind parameters to model the TEC atlas. The models obey rigorously the causality principle. The results of the discussion display that the R score reaches around 93% and 98.8% for Bézier curves and the neural network, respectively. Again, it is noticed that the mean squared error of the network model decreases to 1.1308 TECU. The curve model emerges to the reader as an alternative to neural networks in projections of space climate conditions. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Neural networks

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Incorporation of Two-Fold Trust in Group Recommender System to Handle Popularity Bias

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SN computer Science 2024年第2期5卷 259页

作者： Richa Bedi, Punam Gupta, Shivani Kumar, Ranjeet Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science University of Delhi Delhi New Delhi India School of Computer Science Engineering and Technology Vellore Institute of Technology Tamilnadu Chennai India School of Electronics Engineering Vellore Institute of Technology Tamilnadu Chennai India

In recent years, there has been a rise in work which focuses on various issues of recommender systems other than accuracy. Popularity bias is one factor that causes the list of recommendations to deviate from the user’s expectation and causes biased list to generate. This paper proposed an approach that handles the popularity bias in trust aware group recommender system using trust propagation. The proposed work considers two factors, novelty and serendipity, to evaluate the presence of bias. By incorporating trust, along with novelty, diversity, and unexpectedness, the diverse list of recommendation that maintains accuracy should be generated. Trust propagation combines trustworthy user’s opinion indirectly involves enhancing the accuracy. The trust propagation and randomness in trust propagation ensures the trade-off between the accuracy and bias of the recommendations. A prototype is developed using the tourism dataset and compared using traditional approach of recommendation using various accuracy measures and coverage. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Diversity Novelty Popularity bias Recommender system Relevancy

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Towards robust and efficient intrusion detection in IoMT: a deep learning approach addressing data leakage and enhancing model generalizability

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Multimedia Tools and Applications 2024年 1-20页

作者： Bouke, Mohamed Aly El Atigh, Hayate Abdullah, Azizol Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Departement of Computer Engineering Faculty of Computer Engineering Bandirma Onyedi Eylul University Balikesir10200 Turkey

The Internet of Medical Things (IoMT) brings advanced patient monitoring and predictive analytics to healthcare but also raises cybersecurity and data privacy issues. This paper introduces a deep-learning model for IoMT intrusion detection, utilizing the WUSTL EHMS 2020 dataset, which focuses on cyber threats like man-in-the-middle attacks and data injection. Our sequential model, trained with TensorFlow, surpasses traditional predictive accuracy and efficiency methods. It achieves 99% accuracy with reduced loss and excels in precision, recall, F1 Score, and ROC AUC. The model uses 25 key features from 45, preventing overfitting and addressing data leakage in preprocessing. These steps ensure the model's reliability for IoMT cybersecurity, demonstrating its effectiveness in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cybersecurity

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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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Ultra-Wideband technology for Healthcare Monitoring: A Machine Learning Approach

Ultra-Wideband Technology for Healthcare Monitoring: A Machi...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Raj, Raunak Awasthi, Avinash Arora, Komal Singh, Shreshth Miranka, Hemant Rakhra, Manik School of Computer Science Engineering Lovely Professional Univeristy Punjab India Malviya National Institute of Technology Department of Computer Science and Engineering Rajasthan India Visvesaraya National Institute of Technology Department of Computer Science and Engineering Maharashtra India Lakshmi Niwas Mittal Institute of Information Technology Department of Computer Science and Engineering Rajasthan India

ISBN: (纸本)9798331542344

Ultra-Wideband (UWB) technology has emerged as an emerging solution for a variety of applications in clinical establishments., offering significant advantages such as precise localization capabilities., high data rates., and low power consumption. The paper systematically examines the utilization of UWB technology within clinical settings., focusing on its applications in health care monitoring., medical imaging., indoor positioning., and wireless communication. By leveraging a comprehensive dataset from the UCI Machine Learning Repository., the authors provide detailed empirical insights into the performance and effectiveness of VWB-based systems in enhancing healthcare outcomes. The authors also identify and discuss specific challenges., such as integration complexities and regulatory hurdles., as well as opportunities., including potential advancements in patient care and health care delivery through the adoption of UWB technology. Additionally., the authors have built a model using a Random Forest classifier and achieved an accuracy of 91.8%. © 2024 IEEE.

关键词： Electronic health record

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