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8th International Conference on Intelligent Technologies, ICIT 2023

作者： Jaiswal, Hrishikesh Anjali Arya, K.V. Department of Information Technology ABV-Indian Institute of Information Technology and Management Gwalior474015 India Department of Computer Science and Engineering ABV-Indian Institute of Information Technology and Management Gwalior474015 India

ISBN: (纸本)9789819738588

Stock prediction and portfolio management are vital in the financial industry, aiding investors in informed decisions. This study introduces a novel approach termed the Stock Prediction and Portfolio Management Model, which amalgamates advanced machine learning algorithms with portfolio optimization techniques. The Stock Prediction and Portfolio Management Model framework substantially enhances the precision of stock price predictions, leading to an elevated performance of investment portfolios while concurrently mitigating inherent risks. The initial phase of the model focuses on predicting stock prices by harnessing historical data and employing technical indicators like the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD). In the subsequent phase, the SPPMM system meticulously constructs optimal portfolios utilizing genetic algorithms, further solidifying its efficacy in aiding investors in making informed decisions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Evaluating the Efficacy of Machine Learning Methods in Phishing Detection: A Comparative Analysis 3

Evaluating the Efficacy of Machine Learning Methods in Phish...

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

作者： Saraswathi, P. Keerthi, S. Reshmika, K.S. Gourav Pritham, G.R. Prabha, M. Dhiyanesh, B. Velammal College of Engineering and Technology Department of Information Technology Madurai India Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350354348

ISBN: (纸本)9798350354348

In the field of cybersecurity, the ongoing issue of phishing attacks is still of utmost concern because it puts both individuals and companies at serious risk. In order to prevent these fraudulent efforts and protect sensitive data, it is essential to implement effective detection and mitigation mechanisms. This research undertakes a thorough comparative evaluation of various machine learning models designed for phishing detection. Through a review of the literature on phishing detection techniques, we highlight the need for a methodical comparison analysis to identify the most effective strategies. In order to close this gap, we carefully assess the effectiveness of well-known machine learning methods, including Neural Networks, Gradient Boosting, XGBoost, Decision Trees, Random Forests, Support Vector Machines (SVM), and Logistic Regression. Using datasets from reliable sources, such as the PhishTank open-source service and the University of New Brunswick's dataset, we create a balanced dataset with 5000 authentic URLs and the same amount of phishing URLs. Our approach includes thorough feature extraction and preprocessing steps to guarantee the accuracy and applicability of the dataset. To evaluate each model's effectiveness, key performance indicators such F1-score, precision and recall are used. Our findings highlight subtle variations in the models' functionality and provide insight into their advantages and disadvantages for phishing detection. Additionally, we use visualisation approaches to explain each model's outputs, which offer deeper insights into the operational dynamics of each model. Our results reveal subtle differences in the performance of the models, explaining their various advantages and disadvantages in the field of phishing detection. The resulting comparative study provides useful information about the effectiveness of machine learning techniques, which helps decision-makers in practical deployment settings. © 2024 IEEE.

关键词： Phishing

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Improved Multi-Path Routing for QoS on MANET

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computer Systems Science & engineering 2023年第6期45卷 2521-2536页

作者： M.Vargheese Surbhi Bhatia Shakila Basheer Pankaj Dadheech Department of Computer Science and Engineering PSN College of Engineering and TechnologyTirunelveli627152Tamil NaduIndia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl HasaSaudi Arabia Department of Information Systems College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.BOX 84428Riyadh11671Saudi Arabia Department of Computer Science&Engineering Swami Keshvanand Institute of TechnologyManagement&Gramothan(SKIT)Jaipur302017RajasthanIndia

A Mobile Ad hoc NETwork(MANET)is a self-configuring network that is not reliant on *** paper introduces a new multipath routing method based on the Multi-Hop Routing(MHR)*** is the consecutive selection of suitable relay nodes to send information across nodes that are not within direct range of each *** to ensure good MHR leads to several negative consequences,ultimately causing unsuccessful data transmission in a *** research work consists of three *** first to attempt to propose an efficient MHR protocol is the design of Priority Based Dynamic Routing(PBDR)to adapt to the dynamic MANET environment by reducing Node Link Failures(NLF)in the *** is achieved by dynamically considering a node’s mobility parameters like relative velocity and link duration,which enable the next-hop *** method works more efficiently than the traditional *** the second stage is the Improved Multi-Path Dynamic Routing(IMPDR).The enhancement is mainly focused on further improving the Quality of Service(QoS)in MANETs by introducing a QoS timer at every node to help in the QoS routing of *** QoS is the most vital metric that assesses a protocol,its dynamic estimation has improved network performance *** method uses distance,linkability,trust,and QoS as the four parameters for the next-hop *** is compared against traditional routing *** Network Simulator-2(NS2)is used to conduct a simulation analysis of the protocols under *** proposed tests are assessed for the Packet Delivery Ratio(PDR),Packet Loss Rate(PLR),End-to-End Delay(EED),and Network Throughput(NT).

关键词： Multi-path routing quality of service node-link failure packet delivery ratio

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Identification of Phishing Attacks using Machine Learning 7

Identification of Phishing Attacks using Machine Learning

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7th International Conference on Image information Processing, ICIIP 2023

作者： Jindal, Nikhil Rastogi, Dhruv Joshi, Kartik Gupta, Deepak Jaypee University of Information Technology Department of Computer Science & Engineering And Information Technology Solan India

ISBN: (纸本)9798350371406

The Internet universal connectivity is both a boon and a breeding ground for phishing attacks, manipulating unsuspecting users into interacting with harmful links that redirect them to deceptive websites aiming to pilfer personal information. Phishing, a prevalent cyber threat, often uses cleverly disguised links in emails, texts, or social media that appear authentic, leading individuals to divulge sensitive details. Moreover, the unprecedented rise of artificial intelligence in recent years has also fueled a fresh wave of phishing attacks. These have become even more sophisticated, dangerous and prevalent. Therefore, there is a need to deploy advanced solutions that can automatically detect and prevent such phishing attacks. In this paper, the proposed solution analyzes URLs, considering factors like domain name, URL length, and the presence of suspicious keywords, seeking to differentiate between legitimate and phishing attempts. Further, various machine learning models are deployed to detect and classify phishing attacks to achieve an accuracy of 95.2%. © 2023 IEEE.

关键词： Adaptive boosting

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Privacy BERT-LSTM: a novel NLP algorithm for sensitive information detection in textual documents

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Neural Computing and Applications 2024年第25期36卷 15439-15454页

作者： Muralitharan, Janani Arumugam, Chandrasekar Department of Information Technology St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India

In this modern digital era, the increasing volume of textual data and the widespread adoption of natural language processing (NLP) techniques have presented a critical challenge in safeguarding sensitive privacy information. As a result, there is a pressing demand to design robust and accurate NLP-based techniques to perform efficient sensitive information detection in textual data. This research paper focuses on the detection and classification of sensitive privacy information in textual documents using NLP by proposing a novel algorithm named Privacy BERT-LSTM. The proposed Privacy BERT-LSTM algorithm employs BERT for obtaining contextual embeddings and LSTM for sequential information processing, facilitating efficient sensitive information detection in textual documents. The BERT with its bidirectional characteristics captures the nuances and meaning of the textual documents, while the LSTM derives the long-range dependencies in the textual data. Moreover, the proposed Privacy BERT-LSTM algorithm with its attention mechanism highlights the important regions of the textual documents, contributing to efficient sensitive information detection. The comprehensive performance evaluation is conducted by employing the SMS Spam Collection dataset in terms of standard performance metrics and comparing it with different state-of-the-art techniques, namely, CASSED, PRIVAFRAME, CNN-LSTM, Conv-FFD, GCSA, TSIIP, and, C-PIIM. The experimental outcomes clearly illustrate that the Privacy BERT-LSTM algorithm demonstrates superior performance in identifying various types of sensitive information by achieving an accuracy of 92.50%, F1-score of 85.02%, and Precision of 89.36%. The proposed algorithm outperforms existing baseline models, providing valuable advancements in sensitive information detection using NLP. Therefore, this research contributes to the advancement of privacy protection in NLP applications and opens avenues for future investigations in the domain of sensitive info

关键词： Classification (of information)

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Indian Sign Language Recognition in Real Time using YOLO NAS 3

Indian Sign Language Recognition in Real Time using YOLO NAS

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Raj, Rohit Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Anish, Mohammad Pune Institute of Computer Technology Department of E&TC Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350354171

Normal people typically communicate using voice, while deaf individuals use sign language to communicate with each other. However, a challenge arises when deaf people need to interact with those who do not understand sign language. To address this issue, we propose developing a model that can recognize Indian Sign Language and translate it into text. Deep learning approaches have been used into computer vision in recent years, greatly advancing the area of sign language identification and improving accessibility for the deaf. To achieve real-time recognition of Indian Sign Language, we have used YOLO NAS (You Only Look Once Neural Architecture Search) object detection *** YOLO NAS architecture achieves 0.9568 mean Average Precision (mAP) with intersection over union (IoU) threshold of 50% across 100 classes, using our selfcreated Sign Language dataset. © 2024 IEEE.

关键词： Object detection

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Indian Sign Language Recognition Using Video Vision Transformer 3

Indian Sign Language Recognition Using Video Vision Transfor...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ojaswee Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Anish, Mohammad Pune Institute of Computer Technology Department of E&TC Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350354171

Sign language is a way of communication that uses hand shapes, orientation, movements, and facial expressions to express instead of spoken words like normal language. Different regions have developed their own versions of sign language. In India, Indian sign language (ISL) is the primary mode of communication for more than 5 million speech or hearing impaired people. However, the knowledge of signing and understanding the ISL is only limited to the deaf community, which can make it hard to communicate with the people who don't understand the language. Thus, effective recognition of ISL gestures is vital for seamless interaction between these impaired people and the broader society. The rise of artificial intelligence and computer vision models has provided us with many effective tools to tackle this challenge. While there has been some progress in this field, most research has focused on static signs or American Sign Language, leaving video-based recognition for ISL largely unexplored. To bridge this gap, our paper proposes an approach that utilizes Video Vision Transformer (ViViT) architecture for ISL recognition. We have experimented with different layers and different numbers of attention heads to get a model that works best for the assigned task. Our architecture gives an overall accuracy of 96.69% and a top-5 accuracy of 99.55% on the sign recognition task using our own created VISL-PICT dataset. © 2024 IEEE.

关键词： Audition

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Protecting machine learning systems using blockchain: solutions, challenges and future prospects

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Multimedia Tools and Applications 2024年 1-28页

作者： Hajlaoui, Rejab Dhahri, Salah Mahfoudhi, Sami Moulahi, Tarek Alotibi, Gaseb Department of Computer and Informtion Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Laboratory of Advanced Multifunctional Materials and Technological Applications Faculty of Science and Technology of Sidi Bouzid University of Kairouan Sidi Bouzid Tunisia Department of Information and Technology Management Mediterranean School of Business South Mediterranean University Tunis Tunisia Department of Information Technology College of Computer Qassim University Buraidah Saudi Arabia Department of Computers and Information Technology Alwajah College University of Tabuk Tabuk Saudi Arabia

Machine learning-based systems have emerged as the primary means for achieving the highest levels of productivity and efficiency. They have become the most influential competitive factor for many technologies and business companies such as Cloud AI Companies (Google, Facebook…) and Health Care AI Companies (Tempus, Nanox …). However, privacy and security issues have become the biggest challenge facing all ML applications. These threats are known as Adversarial Machine Learning (AML). Their main goal is to maliciously manipulate training data and model parameters to obtain misleading results. To overcome these challenges, several research studies have proven that Blockchain, which relies on cryptographic technologies, constitutes a promising solution for securing ML applications. It provides high guarantees to make the system scalable, reliable and more secure. In this survey, we provide an overview of ML applications and the most hostile attacks they face. Then, we define the blockchain technique and its distinctive features. After that, we conduct a comprehensive and detailed survey of the latest and best blockchain-based research to address security issues in ML applications (integrity, confidentiality and availability). We analyze, compare and comment on all the proposed solutions. In the end, we discuss in detail the various challenges that hinder the adoption of blockchain technology and we extract the most important scientific trends that will serve as guidance and support for researchers in their future works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Combating Digital Deception: Machine Learning Approaches for Detecting Political Misinformation and Clickbait on Social Media 6

Combating Digital Deception: Machine Learning Approaches for...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Harindi, Dewmini Perera, Nirmal Liyanage, Chamath Dematagoda, Hirushini Fernando, Harinda Rathnayake, Samadhi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents machine learning-based strategies for mitigating the problem of misinformation in social media, with a special focus on political misinformation detection and clickbait detection. The following research work attempts to address the pressing issue of the rapid spread of false or misleading information by making use of deep recurrent neural networks (RNN s) combined with semi-supervised learning methodologies. For political misinformation detection, a hybrid model integrating Long Short-Term Memory (LSTM) networks with fully connected layers was developed, achieving 94.22 % accuracy on the CIC Truth Seeker dataset. Clickbait detection was made using a sequential architecture that combines LSTM and Gated Recurrent Units (GRU) layers with Simple RNN layers to achieve an accuracy of 96% on test data. This study underlines the effectiveness of different machine learning techniques in battling misinformation that would contribute to online dignity in political discourse and ensure better public communication in a digital world. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing Industrial Anomaly Detection with Auto Encoder-Based Temporal Convolutional Networks for Motor Fault Classification

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SN computer Science 2024年第8期5卷 1067页

作者： Varalakshmi, B.D. Lingaraju, G.M. Department of Computer Science and Engineering Acharya Institute of Technology Karnataka Bangalore India Department of Information Science and Engineering MS Ramaiah Institute of Technology Karnataka Bangalore India

The creation of binary and multi-classification models with the goal of accurately detecting and categorizing motor defects is important to study. This work explores how autoencoders can be used to apply self-supervised learning in industrial settings. A generic temporal convolutional network with an autoencoder was built and trained using the milling data. The successful identification and classification of motor faults underscore the industrial anomaly detection using advanced machine learning techniques. Also, paper highlights the importance of data processing and domain expertise in achieving significant model performance improvements. The performance used a fraction of the data that was available, highlighting the need of future research to include a more extensive range of fault states and the intensities of those fault states in order to enhance the diagnostic capabilities of the model. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Autoencoder Deep learning Machine learning TCN (Temporal Convolution network) UC Berkeley milling dataset

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