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2024 IEEE Conference on engineering informatics, ICEI 2024

作者： Tanan, Cebajel Kulkarni, Sameer G. Das, Tamal Hanawal, Manjesh K. Indian Institute of Technology Computer Science and Engineering Department Dharwad India

ISBN: (纸本)9798331505776

In recent years, the advances in digitalisation have also adversely contributed to the significant rise in cybercrimes. Hence, building the threat intelligence to shield against rising cybercrimes has become a fundamental requisite. Internet Protocol (IP) addresses play a crucial role in the threat intelligence and prevention of cyber crimes. However, we have noticed the lack of one-stop, free, and open-source tools that can analyse IP addresses. Hence, this work introduces a comprehensive web tool for advanced IP address characterisation. Our tool offers a wide range of features, including geolocation, blocklist check, VPN detection, proxy detection, bot detection, Tor detection, port scan, and accurate domain statistics that include the details about the name servers and registrar information. In addition, our tool calculates a confidence score based on a weighted sum of publicly accessible online results from different reliable sources to give users a dependable measure of accuracy. Further, to improve performance, our tool also incorporates a local database for caching the results, to enable fast content retrieval with minimal external Web API calls. Our tool supports domain names and IPv4 addresses, making it a multi-functional and powerful IP analyser tool for threat intelligence. © 2024 IEEE.

关键词： Internet protocols

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Proposed IoT-Based Smart Garbage Management System 4

Proposed IoT-Based Smart Garbage Management System

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Pujar, A.M. Pardeshi, K.R. Shah, Tanishka A. Shah, Yash N. Lokhande, Mrudula N. Pandhare, Yash S. Walchand Institute of Technology Computer Science & Engineering Department Solapur India

ISBN: (纸本)9798331540364

Considering the rapid growth of the urban population and rapid industrialization, the issue of efficient waste disposal has become very important. In response, authors here propose an idea in the form of an IOT -based waste management system designed to address the challenges at hand autonomously. The paper aims to propose solution for garbage management system. The system leverages IoT technology with sensor-based circuits and machine automation and models to streamline waste management processes. Traditional trash cans require manual operation, you need to press the lever to manually open the lid, monitor the trash can capacity, and adjust the garbage distance to prevent the trash can from overflowing. In contrast, this intelligent waste management model will work autonomously. The sensor will detect the presence of a person and automatically will open and close the lid. Additionally, location tracking will alert the authorities when waste processing capacity approaches its limits, facilitating timely disposal of waste and will prevent overflow. Additionally, there will be features such as voice detection to edit specific commands for executable access. By eliminating manual intervention and implementing intelligent monitoring capabilities, this system will provide a smarter and more efficient approach to waste treatment, ultimately leading to cleaner and more sustainable urban environments. © 2024 IEEE.

关键词： Waste treatment

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Effortless Meeting Analysis Automation 15

Effortless Meeting Analysis Automation

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Mohit, Harsh Chaitanya, K. Kumar, Maneesh Kushal, B. Shanthakumara, A.H. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (纸本)9798331300579

The "Automated DSM Insight Analyzer" is a groundbreaking initiative that revolutionizes the analysis of Daily Stand-Up Meetings (DSMs). Leveraging a sophisticated combination of cutting-edge technologies, including a Flask-based API for audio processing, Azure Speech-to-Text for precise transcription, and the GPT API for advanced Natural Language Processing (NLP), this solution provides comprehensive insights. It addresses challenges in resource management and bug tracking, ensuring the prompt generation of actionable insights. With multilingual support and seamless integration with Google Calendars, it enhances productivity, offering a user-friendly platform that fosters improved collaboration and project success through the extraction of valuable insights from daily meetings. © Grenze Scientific Society, 2024.

关键词： Application programming interfaces (API)

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Multi-Modal Sentiment Analysis of Product Reviews

Multi-Modal Sentiment Analysis of Product Reviews

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Gorai, Joy Shaw, Dilip Kumar National Institute of Technology Department of Computer Science and Engineering Jamshedpur India

ISBN: (纸本)9798350388534

With the exponential growth of e-commerce, understanding consumer sentiments from online product reviews has become crucial for businesses. This study presents a comprehensive approach to online product review sentiment analysis, encompassing text and image data. To facilitate our research, we compiled a novel dataset from the Amazon e-commerce platform, comprising paired textual reviews, corresponding images and sentiment. The work employs a multimodal deep-learning approach to capture the nuanced sentiment expressed through both text and images. Specifically, we integrate the Bidirectional Encoder Representations from Transformers (BERT) model, enhanced with a low-rank adaptation to refine its self-attention mechanism, for textual sentiment analysis and the VGG16 neural network for image feature extraction. Our experiments demonstrate that this multimodal approach surpasses unimodal techniques, achieving convincing results in sentiment classification. © 2024 IEEE.

关键词： Feature fusion Low rank adaption Multimodal Product review

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A novel and proposed triad machine learning-based framework for the prognosis of Huntington’s disease

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International Journal of Information technology (Singapore) 2024年第4期16卷 2603-2610页

作者： Deepak, Shashikant Ojha, Ananta Acharjya, Kalyan Mishra, Reshmi Gantra, Amit Kalaiarasan, C. Upadhyay, Ramakant Walke, Suchita Department of UGDX ATLAS SkillTech University Mumbai India Department of Computer Science and Information Technology Jain (Deemed-to-be-University) Karnataka Bengaluru India Maharishi School of Engineering and Technology MUIT Lucknow India Department of Biotechnology Noida Institute of Engineering and Technology UP Greater Noida India Department of Computer Science and Engineering Parul Institute of Engineering and Technology Gujarat Vadodara India Department of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science Sanskriti University UP Mathura India Department of Computer Engineering Pillai College of Engineering and Technology Mumbai University Mumbai India

A hereditary condition known as Huntington's Disease (HD) results in the gradual degeneration of brain nerve cells, impairing a person's capacity for speech, walking, and reasoning. Owing to its severity, novel strategies are crucial for the creation of techniques that support the accurate categorization of this illness. This research proposes a tripartite machine learning-based framework that uses gait dynamics and temporal parameter extraction to predict the prognosis of Huntington's illness. Phases including data pre-processing, feature extraction, classification, and projected output are included in the suggested triad method. The proposed framework uses a triad combination of classifiers for classification: Support vector machines (SVM), K-nearest neighbor (KNN), and Naïve Bayes (NB). The suggested framework's effectiveness is assessed by comparing it to current machine learning classifiers using parameters from the public repository on the dynamics of gait in neurodegenerative diseases. The experimental results show that the triad classifiers employed in the proposed approach are able to achieve higher accuracy (85.45%), higher sensitivity (78.37%) and higher specificity (76.55%) than the existing classifiers used in the recent studies. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Gait dynamics Huntington’s disease KNN Machine learning (ML) NB SVM

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Automated Video Transformation for PIB Press Release: A Literature Survey 1

Automated Video Transformation for PIB Press Release: A Lite...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Bharadwaj, Rakhi Kachare, Mitali Kadam, Vaibhav Jagtap, Rahul Ghuge, Pranjal Dhore, Manikrao Otari, Samarth Mahajan, Parth Meshram, Pratik Mhaske, Krishna Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350377002

This paper investigates new trends in press release distribution, with a special emphasis on the shift from text-to-video formats. Modern audiences' short attention spans demand a more captivating method of disseminating information. We suggest a software program that does this job automatically by turning press releases into interesting videos. This program creates visually appealing material by using a collection of photos and videos that are not protected by copyright. Furthermore, our software integrates cutting-edge technologies like Natural Language Processing for keyword extraction along with algorithms like Long Short-Term Memory for language translation. By improving accessibility and relevance, these features enable a wider range of users to consume the information. This study demonstrates how technology has the power to change conventional communication techniques, making them more interesting and available to modern audiences. Notably, it's important to clarify that this paper exclusively focuses on conducting a thorough literature survey of existing research papers in this domain. © 2024 IEEE.

关键词： Presses (machine tools)

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Phishing URL Detection using Deep Q-Networks with Convolutional Neural Networks

Phishing URL Detection using Deep Q-Networks with Convolutio...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Birthriya, Santosh Kumar Ahlawat, Priyanka Jain, Ankit Kumar National Institute of Technology Department of Computer Engineering Kurukshetra136119 India

ISBN: (纸本)9798350375237

This research presents an innovative approach to phishing URL detection by integrating hybrid features with Deep Q-Networks and convolutional neural networks. This combined method leverages Deep Q-Networks to enhance decision-making policies and convolutional neural networks to extract critical features from URLs, enabling precise classification. This two-pronged model accurately identifies URLs, categorizing them effectively as either phishing or legitimate. Tested on a specialized dataset, the model shows outstanding performance, achieving an accuracy rate of 99.75%, thereby exceeding previous methods and demonstrating its suitability for deployment across various web environments. Our findings emphasize the scalability and effectiveness of our approach, signifying a major advancement in cybersecurity and phishing detection technologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Framework for Domain Independent Hybrid Recommender Systems: Addressing Challenges of Imperfect Data

Framework for Domain Independent Hybrid Recommender Systems:...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vibhute, Amey Netaji Subhas Institute of Technology Department of Computer Engineering Delhi India

ISBN: (纸本)9798350382693

Recommender systems are an indispensable aspectof numerous contemporary online platforms, aiding users inuncovering new and pertinent content. However, both content-basedfiltering (CBF) and collaborative filtering (CF) systemsexhibit distinct strengths and limitations. CBF systems typicallyexcel at addressing cold start issues but their recommendationsmay falter when metadata is incomplete or absent. CF systems,on the other hand, can provide superior recommendationquality when ample data is accessible, but they may fail wheninsufficient data is available. To mitigate these limitations, thispaper puts forward a hybrid recommender system that harnessesthe strengths of both CBF and CF methodologies. Our systemassigns confidence scores to both models and dynamically assignsweights based on those scores to generate the most advantageousrecommendations. This approach empowers us to circumventthe drawbacks of each model and capitalize on their respectivestrengths. Our framework has been evaluated on a vast dataset ofuser ratings and movie metadata. The findings demonstrate thatour hybrid system outshines both CBF and CF systems in termsof recommendation quality, particularly for cold start users andunpopular items. Notably, our hybrid system empowers users tofine-tune the balance between the two models using a handfulof parameters which allows users to tailor the recommendationengine to their specific data and preferences. © 2024 IEEE.

关键词： Matrix factorization

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Real-Time Caption Generation for the American Sign Language Using YOLO and LSTM

Real-Time Caption Generation for the American Sign Language ...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Jana, Urmi Paul, Subhashish Bhandari, Dinabhandhu Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350382693

The lack of captions available for live video events on online platforms makes them inaccessible to the speech and hearing deprived communities. In this study, we examine two models: the pre-trained You Only Look Once (YOLO) framework and a Long Short Term Memory (LSTM) model that uses the Mediapipe framework from Google. The LSTM model is constructed by extracting the landmark features of the subjects face, torso and hands from the video frames and using them to predict the depicted gesture. This sets it apart from existing deep learning methods that use only a Convolutional Neural Network (CNN) framework to extract spatial features. We evaluate the performance and demonstrate the superiority of the proposed model against a LRCN model using our custom dataset. The proposed model offers a valuable approach to making live videos accessible to both viewers and speakers of the disabled community, who are currently depending on human translators. The ability to extract keypoints beforehand further enhances the models capabilities and speed. © 2024 IEEE.

关键词： LSTM Mediapipe Sign lanuage YOLO

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Climate Geoscience–Based Disaster Management in Healthcare Analysis by Markov Q-Transfer Adversarial with Cloud Computing

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 474-483页

作者： Srinivas, Jhade Rebecca, B. Bhargavi, Kovvuri N Swetha, Velma Reddy Spandana, S. Degala, Divya Priya Department of Computer Science and Engineering-Data Science KG Reddy College of Engineering and Technology Moinabad Telangana Hyderabad India Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Dundigal 500043 India Department of CSE Aditya University AP Surampalem India Department of Computer Science and Engineering (AI-ML) Malla Reddy Engineering College Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Telangana Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), which are capable of precisely identifying and locating the relevant areas of interest within satellite imagery or other types of remote sensing data. This helps with rescue planning, restoration efforts, and disaster evaluation. Difficulties in the provision of healthcare services, due to natural disasters, are made worse by the existing problems prevalent in most healthcare systems. The aim of this research is to propose a novel technique in climate change–based disaster management with their public healthcare analysis using cloud computing with deep learning techniques. In this research, the cloud network has been used in LiDAR image collection for climate change–based disaster-struck regions and their public healthcare analysis. This collected image has been classified and optimized using a deep Markov Q-transfer adversarial neural network with binary grey whale swarm optimization. Experimental analysis has been carried out for various disaster management–based public healthcare datasets in terms of classification accuracy, specificity, NSE, sensitivity, and F-1 score. The proposed technique attained a classification accuracy of 97%, F-1 score of 94%, specificity of 96%, sensitivity of 95%, and NSE of 57%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial neural network Cloud computing Disaster management LiDAR image Whale swarm optimization

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