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Sentimental Analysis-Based Recommended System for Products Using Machine Learning 8th

Sentimental Analysis-Based Recommended System for Products U...

8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Mamatha Bai, B.G. Likhith, S.R. Itagi, Salma Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Karnataka Bengaluru India Department of Computer Science Engineering Sai Vidya Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819976218

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on *** and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Polarity Sentimental analysis Web scraping

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Environmental water quality prediction based on COOT-CSO-LSTM deep learning

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Environmental science and Pollution Research 2024年第42期31卷 54525-54533页

作者： Rajagopal, Sankarasubbu Ganesh, Sundaram Sankar Karthick, Alagar Sampradeepraj, Thangavel Department of Information Technology National Engineering College Tamilnadu Kovilpatti628503 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Puduvoyal Tamilnadu Thiruvallur601206 India Renewable Energy Lab Department of Electrical and Electronics Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu Virudhunagar626126 India

Water resource management relies heavily on reliable water quality predictions. Predicting water quality metrics in the watershed system, including dissolved oxygen (DO), is the main emphasis of this work. The enhanced long short-term memory (LSTM) model was suggested to improve the model’s performance. Additionally, a hybrid model was employed to calculate the ideal parameter values for the LSTM model, which helped overcome the nonstationarity, unpredictability, and nonlinearity of the data about the water quality parameters. This model recruited the COOT method. The original weekly water quality values at the Vaigai River, Madurai, Tamil Nadu, India, were tested using the suggested hybrid model. An independent LSTM, the hybrid optimisation method takes its cues from the cuckoo bird’s reproductive strategy and a novel meta-heuristic optimisation technique dubbed COOT, which is based on the behaviour of a flock of coot birds. If implemented, the suggested hybrid model might serve as an alternate framework for water quality prediction, laying the groundwork for basin-wide efforts to manage water quality and control pollutants. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Macroinvertebrates

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Intelligent Fraud Detection Techniques in Credit Card and Internet Banking

Intelligent Fraud Detection Techniques in Credit Card and In...

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Communication, Control and Intelligent Systems (CCIS)

作者： Santosh Iranagouda Goudar Biswajit Bhowmik BRICS Lab Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

In an era where financial services are easily accessible, financial fraud has emerged as a widespread threat, aimed at unlawfully seizing assets from individuals, businesses, and institutions. Crimes like credit card and internet banking fraud present serious risks to the financial ecosystem. Criminals exploit card information using techniques such as identity theft, skimming, and phishing, causing substantial economic losses each year. Similarly, the rise of internet banking fraud, where unauthorized access to accounts often results from weaknesses in authentication systems, has further disrupted the financial sector. Detecting such fraudulent activities is especially difficult due to the ever-changing tactics of perpetrators, the large volume of transaction data, and technical challenges like data imbalance and concept drift. This paper examines the landscape of financial fraud, with a specific emphasis on credit card and internet banking fraud. It begins by providing an overview of the issue and its impact on the modern economic environment, followed by an exploration of various fraud detection methods. Additionally, the paper addresses key challenges in fraud detection and highlights the need for innovative approaches to effectively combat the growing threat of financial crime.

关键词： Economics Online banking Computational modeling Biological system modeling Scalability Concept drift Finance Credit cards Fraud Systematic literature review

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Performance Analysis and Predictive Modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study

Performance Analysis and Predictive Modeling of MPI Collecti...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Manideep Raya V S R S Reddy Sanjay Ramapogu Raju Girish K K Biswajit Bhowmik Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms—Flat Tree, Chain, and Binary Tree—by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical.

关键词： Program processors Network topology Scalability Message passing Clustering algorithms Parallel processing Predictive models Prediction algorithms Topology Performance analysis

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Money Laundering Detection in Imbalanced E-wallet Transactions with Threshold Optimization

Money Laundering Detection in Imbalanced E-wallet Transactio...

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International Conference for Convergence of Technology (I2CT)

作者： Sachin Shivaji Doddamani Girish K K Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Lab National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

The swift embrace of technology within the financial industry has driven the extensive utilization of electronic payment systems, providing smooth money transfers and substituting outdated paper-based procedures. Consequently, mobile payment systems centered on electronic wallets (e-wallets) have signifi-cantly transformed contemporary finance, introducing improved security measures and transaction functionalities. However, the prevalence of unlawful activities, including money laundering and associated fraud via e-wallets, presents a substantial threat to the integrity of the financial sector. This research paper delves into the pivotal role of machine learning models in identifying money laundering activities within e-wallet transactions. The study focuses on addressing the imbalance inherent in the PaySim dataset through the oversampling technique. Employing three distinct models—Logistic Regression (LR), Gradient Boosting, and XGBoost—the research systematically evaluates their effectiveness. Notably, XGBoost emerged as the standout performer, showcasing exceptional accuracy at 99.88%, precision at 0.9984, and sensitivity at 0.999. Furthermore, a threshold moving technique is employed to enhance the model’s efficiency, optimizing its performance in detecting potential instances of money laundering within e-wallet transactions.

关键词： Logistic regression Sensitivity Online banking Finance Boosting Fraud Security

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Device Processing Challenges for Miniaturized Sensing Systems Targeting Biological Fluids

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Biomedical Materials and Devices 2023年第1期1卷 82-98页

作者： Stoukatch, S. Dupont, F. Redouté, J.-M. Microsys Lab Department of Electrical Engineering and Computer Science Liege University Seraing Belgium

This article presents a review of device processing technologies used in the fabrication of biomedical systems, and highlights the requirements of advanced manufacturing technology. We focus on biomedical systems that perform diagnostics of fluidic specimens, with analytes that are in the liquid phase. In the introduction, we define biomedical systems as well as their versatile applications and the essential current trends. The paper gives an overview of the most important biomolecules that typically must be detected or analyzed in several applications. The paper is structured as follows. First, the conventional architecture and construction of a biosensing system is introduced. We provide an overview of the most common biosensing methods that are currently used for the detection of biomolecules and its analysis. We present an overview of reported biochips, and explain the technology of biofunctionalization and detection principles, including their corresponding advantages and disadvantages. Next, we introduce microfluidics as a method for delivery of the specimen to the biochip sensing area. A special focus lies on material requirements and on manufacturing technology for fabricating microfluidic systems, both for niche and mass-scale production segments. We formulate requirements and constraints for integrating the biochips and microfluidic systems. The possible impacts of the conventional microassembly techniques and processing methods on the entire biomedical system and its specific parts are also described. On that basis, we explain the need for alternative microassembly technologies to enable the integration of biochips and microfluidic systems into fully functional systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of

关键词： Alternative microassembly techniques Biological analysis Biomedical system Biosensors

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Enhancing the Security of SDN in 5G: A Hybrid Feature Selection Based Ensemble Machine Learning Framework for Classification of Cyber-Attacks

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SN computer science 2025年第3期6卷 1-16页

作者： Singh, Mahendra Pratap Haimashreelakshmi Singh, Virendra Pratap Gupta, Maanak Cyber Security Research Lab (CSRL) Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangaluru 575025 India Department of Computer Science Tennessee Technological University Cookeville TN United States

In recent years, the adoption of 5G has significantly increased due to its numerous benefits, including high availability, lower latency, improved reliability, and high performance. To manage packet flow, 5G relies on Software-Defined Networking (SDN) that employs software controllers and Application Programming Interfaces (APIs) to route packets and communicate with the hardware, providing advantages like high efficiency, low cost, and dependability. However, due to centralized control, SDN controllers are vulnerable to various cyber-attacks, including Distributed Denial of Service (DDoS), Denial of Service (DoS), Password Brute Forcing, Web Attacks, etc. This paper proposes a framework that comprises a hybrid feature selection method and an ensemble machine learning model. The proposed ensemble model combines the strengths of three different machine learning (ML) classifiers to create a voting classifier for classifying traffic in SDN. Additionally, the optimal value for the hyperparameters of each classifier is obtained through hyperparameter tuning. Finally, the experimental analysis of the proposed model using the InSDN dataset shows 99.96% accuracy, highlighting the proposed model’s effectiveness in addressing the limitations of the existing approaches and detecting multiple attacks in the SDN context. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： 5G Hybrid feature selection Machine learning Software defined networking

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Improving Open-Ended Text Generation via Adaptive Decoding 41

Improving Open-Ended Text Generation via Adaptive Decoding

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41st International Conference on Machine Learning, ICML 2024

作者： Zhu, Wenhong Hao, Hongkun He, Zhiwei Ai, Yiming Wang, Rui MT Lab Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation *** study introduces adaptive decoding, a mechanism that dynamically empowers language models to ascertain a sensible candidate set during ***, we introduce an entropy-based metric called confidence and conceptualize determining the optimal candidate set as a confidence-increasing *** rationality of including a token in the candidate set is assessed by leveraging the increment of *** results reveal that our method balances diversity and coherence *** human evaluation shows that our method can generate human-preferred ***, our method can potentially improve the reasoning ability of language models. Copyright 2024 by the author(s)

关键词： Decoding

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Beyond p-y method:A review of artificial intelligence approaches for predicting lateral capacity of drilled shafts in clayey soils

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Journal of Rock Mechanics and Geotechnical engineering 2024年第9期16卷 3812-3840页

作者： M.E.Al-Atroush A.E.Aboelela Ezz El-Din Hemdan Civil and Environmental Engineering Program Engineering Management DepartmentCollege of EngineeringPrince Sultan UniversityRiyadhSaudi Arabia Department of Structural Engineering Faculty of EngineeringAin Shams UniversityCairoEgypt Structure and Materials Research Lab Prince Sultan UniversityRiyadhSaudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt

In 2023,pivotal advancements in artificial intelligence(AI)have significantly *** that in mind,traditional methodologies,notably the p-y approach,have struggled to accurately model the complex,nonlinear soil-structure interactions of laterally loaded large-diameter drilled *** study undertakes a rigorous evaluation of machine learning(ML)and deep learning(DL)techniques,offering a comprehensive review of their application in addressing this geotechnical challenge.A thorough review and comparative analysis have been carried out to investigate various AI models such as artificial neural networks(ANNs),relevance vector machines(RVMs),and least squares support vector machines(LSSVMs).It was found that despite ML approaches outperforming classic methods in predicting the lateral behavior of piles,their‘black box'nature and reliance only on a data-driven approach made their results showcase statistical robustness rather than clear geotechnical insights,a fact underscored by the mathematical equations derived from these ***,the research identified a gap in the availability of drilled shaft datasets,limiting the extendibility of current findings to large-diameter *** extensive dataset,compiled from a series of lateral loading tests on free-head drilled shaft with varying properties and geometries,was introduced to bridge this *** paper concluded with a direction for future research,proposes the integration of physics-informed neural networks(PINNs),combining data-driven models with fundamental geotechnical principles to improve both the interpretability and predictive accuracy of AI applications in geotechnical engineering,marking a novel contribution to the field.

关键词： Laterally loaded drilled shaft load transfer and failure mechanisms Physics-informed neural networks(PINNs) P-y curves Artificial intelligence(AI) Dataset

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Enhancing Movie Recommendation Systems with MapReduce Genetic Algorithms: Addressing Scalability and Accuracy Challenges

Enhancing Movie Recommendation Systems with MapReduce Geneti...

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Smart Electronics and Communication Systems (ISENSE), International Conference on

作者： Priyansh Patidar Sathwik Venkata Posa Girish K K Shaifali Rao Biswajit Bhowmik Department of Computer Science and Engineering Ishwarchandra Vidyasagar AIT Lab BRICS Lab National Institute of Technology Karnataka Mangalore Bharat

ISBN: (数字)9798331528126

ISBN: (纸本)9798331528133

In the world of big data, the efficacy of movie recommendation systems is crucial for personalizing user experiences in digital entertainment. Traditional methods, including collaborative and content-based filtering, often encounter limitations such as data sparsity, cold start problems, and scalability issues. This paper introduces a novel approach that integrates MapReduce technology with Genetic Algorithms (GAs) to address these chal-lenges. Utilizing the Hadoop framework, our MapReduce Genetic Algorithm (MRGA) efficiently processes extensive datasets by distributing tasks across a cluster of machines. The genetic algorithm component optimizes recommendation accuracy through advanced techniques like selection, crossover, and mutation. Our experimental results, based on the MovieLens 100K dataset, demonstrate that the MRGA approach outperforms traditional collaborative filtering methods in terms of recommendation accuracy and scalability. By leveraging MapReduce's distributed computing power and the GA's optimization capabilities, this research offers a robust solution to improve movie recommendations and handle large-scale data efficiently.

关键词： Accuracy Filtering Scalability Collaborative filtering Entertainment industry Parallel processing Motion pictures Recommender systems Optimization Genetic algorithms

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