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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Vashisht, Rohit Sharma, Rahul Kumar Pant, Maneesh Thakral, Gagan Naresh, Prashant Department of Computer Science & Information Technology KIET Group of Institutions Ghaziabad India Department of Computer Science & Engineering KIET Group of Institutions Ghaziabad India Department of Information Technology JSS Academy of Technical Education Noida India

ISBN: (纸本)9798331530389

The recommendation system of today has made it simple to locate the necessities. Movie recommendation systems are designed to assist movie buffs by making recommendations for films to see, saving them the trouble and effort of having to sort through hundreds or even millions of options. The Recommender System has become a major area of research because it helps people find things online by making suggestions that are similar to what they are looking for. In this paper, we introduce a system called MOVIEHOPE for recommending movies. It uses a content-based filtering method that uses the user's preferences and the item's description to make a profile. In CBF, we use keywords to describe items instead of the user's profile to show what they like or do not like. In plain English, the CBF algorithm likes or recommends things that were liked in the past. It looks at how items have been rated before and suggests the best item that fits. © 2025 IEEE.

关键词： Collaborative filtering

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Driver Behaviour Analysis to Improve Road Safety: A Comprehensive Study 1

Driver Behaviour Analysis to Improve Road Safety: A Comprehe...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Dixit, Shikha Goel, Shivi Jaiswal, Sneha Goyal, Puneet Kumar KIET Group of Institutions Delhi NCR Computer Science Department Ghaziabad India

ISBN: (纸本)9798350353648

Road safety is a shared responsibility that includes following traffic regulations, using defensive driving methods, and prioritizing vehicle maintenance. Consistent alertness behind the wheel, following speed limits, and avoiding distractions like phone use are critical factors in avoiding avoidable road accidents. The number of road accidents, however, has not fallen considerably. Hence, there is a need for a comprehensive study to tackle this problem. This study introduces an innovative approach integrating driver support and infotainment systems to categorize drivers based on their skills and states. Utilizing a machine learning methodology, we classify drivers' maneuvering skills by employing longitudinal and lateral controls, coupled with vehicle speed. This classification relies on a holistic model constructed from sensor data capturing the driving environment, driver behaviors, and vehicle responses. The developed model enables automatic classification of an unknown driver's safety level, providing a personalized approach to enhance road safety. In this paper, our focus shifts to assessing driving skills using results from a driving simulator, particularly in curve driving scenarios. Our methodology employed various machine learning algorithms to train the model, including K-Nearest Neighbors (KNN), Gradient Boosting, AdaBoost, and Random Forest classifiers. Among these, Random Forest exhibited the highest accuracy, achieving an impressive 83% classification accuracy in distinguishing between safe and aggressive drivers. This research contributes to the development of intelligent systems that not only enhance road safety and encourage conscientious driving behaviors but also personalize infotainment offerings, providing a comprehensive approach to driver satisfaction and safety) © 2024 IEEE.

关键词： Intelligent systems

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Behavioral Analysis Using Gamification—A Comprehensive Analysis 11th

Behavioral Analysis Using Gamification—A Comprehensive Anal...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Garg, Gauri Siddiqui, Hiba Nazim Jain, Naman Tyagi, Shivangi Department of Computer Science and Information Technology KIET Group of Institutions Ghaziabad India

ISBN: (纸本)9789819717231

Gamification is the convergence of game designing and behavioral analysis. The goal of gamification is to increase human motivation and performance with respect to a given pastime. Current software answers promise support for analyzing recreation and gamification-related facts. A recent surge in the popularity of online game-inspired software aimed at addressing difficulties in a range of disciplines, including health care, energy efficiency, learning, and industry, has resulted in a surge in its popularity. These apps are largely centered on the concept of gamification, a term that describes the process of developing non-gaming sports that resemble video games. Gamification has evolved into a fantastic strategy for establishing community connections and fostering social interaction in both current and digital situations. This paper provides a comprehensive overview of how gamification is being used continuously for the analysis of behavioral in people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Game design

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Blind Image Restoration and Data Augmentation 2

Blind Image Restoration and Data Augmentation

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Khatter, Harsh Tyagi, Nandini Tayal, Avishi Gupta, Piyush Kiet Group of Institutions Department of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350371055

This paper introduces an innovative method and system that harnesses the collaborative potential of Generative Adversarial Networks (GANs), specifically GFP-GAN (GFP Generative Adversarial Network), and StyleGAN, to significantly enhance image pixel quality, with a primary focus on facial images. Concurrently, it facilitates the streamlined creation of augmented datasets, fostering advancements in a multitude of applications. At its core, the proposed method embodies two pivotal functions: Leveraging the capabilities of GFP-GAN;this function orchestrates the intricate process of pixel-level detail restoration, imperfection rectification, and visual quality enhancement in facial imagery. The outcome is a transformative enhancement of pixel precision, revolutionizing the landscape of image processing. Seamlessly integrating StyleGAN with GFP-GAN's output;this function efficiently generates augmented datasets. These datasets, marked by their dynamism and complexity, emerge as indispensable assets in the realm of machine learning, powering applications ranging from facial recognition to object detection and image synthesis. The proposed method's inherent strengths encompass its ability to redefine image pixel enhancement, elevate the standards of facial imagery, and expedite the production of augmented datasets. It capitalizes on the harmonious synergy between GFP-GAN and StyleGAN, delivering a comprehensive and cohesive solution that transcends traditional image processing boundaries. As an outcome, the paper represents a pioneering leap forward in image processing and machine learning, offering an unprecedented combination of pixel enhancement and dataset augmentation capabilities, particularly in the context of facial imagery and its multifaceted applications. © 2024 IEEE.

关键词： Data Augmentation Face restoration GFPGAN Latent Codes StyleGAN3

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Cloud-Hosted IP-PBX System with Asterisk and Oracle Cloud: A Scalable and Cost-Effective Solution for Business Communications 2

Cloud-Hosted IP-PBX System with Asterisk and Oracle Cloud: A...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Akanksha Surya, Yash Kumar, Rupesh Kumar, Vinay Group of Institutions Department of Computer Science Kiet Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350371055

This research paper examines the development of a cloud-hosted phone system using the open-source software Asterisk and the cloud service provider Oracle Cloud. The paper discusses the design, implementation, and testing of the system, as well as the benefits and challenges of using a cloud-hosted approach for phone systems. The study also evaluates the system's reliability, scalability, and cost-effectiveness, highlighting its potential as a viable alternative to traditional on- premises phone systems. The research concludes that the cloud- hosted phone system using Asterisk and Oracle Cloud can provide a flexible, efficient, and cost-effective communication solution for businesses of all sizes. © 2024 IEEE.

关键词： Asterisk Oracle Cloud SIP Ubuntu Server VoIP

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BdSLW60: A word-level bangla sign language dataset

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Multimedia Tools and Applications 2025年 1-25页

作者： Rubaiyeat, Husne Ara Mahmud, Hasan Habib, Ahsan Hasan, Md Kamrul Computer Science Department Natural Science Group National University Bangladesh Board Bazar Dhaka Gazipur1704 Bangladesh OIC Board Bazar Dhaka Gazipur1704 Bangladesh

Sign language discourse is an essential mode of daily communication for the deaf and hard-of-hearing people, yet, research on Bangla Sign Language (BdSL) faces significant limitations, primarily due to the scarcity of datasets. Recognizing word-level signs in BdSL (WL-BdSL) presents multifaceted challenges like proper annotation procedure, capturing the dynamic nature of sign gestures from facial or hand landmarks, developing suitable machine learning or deep learning-based models with substantial video samples, and so on. In this paper, we aim to address these challenges by introducing BdSLW60, a comprehensive BdSL word-level dataset captured in an unconstrained and natural setting (allowing positional, temporal variations, and changes in hand dominance). BdSLW60 comprises 60 Bangla sign words, with a significant scale of 9307 video trials provided by 18 signers under the supervision of a sign language professional. The dataset was rigorously annotated and cross-checked by 60 annotators, ensures the dataset’s quality and reliability. Additionally, we propose a novel relative quantization-based key frame encoding technique for landmark-based sign gesture recognition. We report the benchmarking of our BdSLW60 dataset using the Support Vector Machine (SVM) with testing accuracy up to 67.6% and an attention-based bi-LSTM with testing accuracy up to 75.1%. The availability of BdSLW60 dataset (https://***/datasets/hasaniut/bdslw60), along with the associated code base (https://***/hasanssl/BdSLW60_Code), promises to enhance further research and applications in the realm of Bangla Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Signal encoding

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Spectral Segmentation Augmented with Normalized Cuts for Detection of Early Blight Disease in Potato 5

Spectral Segmentation Augmented with Normalized Cuts for Det...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Deepkiran Pandey, Mrinal Singh, Laxman Department of Computer Science and Technology Manav Rachna University Haryana Faridabad India Department of Computer Science and Technology KIET Group of Institutions UP Ghaziabad India

Agriculture includes the production of crops, animals, aquaculture, fisheries, and forests for both food and non-food use. The primary factor in the rise of sedentary civilization among humans was agriculture, which allowed people to live in cities by producing surpluses of food from the farming of domesticated species. Uncertainties in the Agricultural Sector directly imply a steep decline in every other aspect of any society. The Agronomical Products, be they raw vegetables, or produced fruits, are all prone to Parasites and Microorganisms. In this research, we will focus on Solanum Tuberosum, and its disease Early Blight, of which, Alternaria Solani is a Causal Vector. For the detection of Early Blight in Potato Leaves, we will make use of the Image Segmentation Technique, based on Spectral Clustering, with Normalized Laplacian Cuts. The model works by segmenting, or rather segregating the affected part of the Potato leaf from the unaffected part, resulting in the formation of a few clusters, based on which the affected leaves can be classified from the healthy leaves. Though the model is a parametrized model, the results have been explored for different values of the parameters, σi, and σx that correspond to the varying configuration of the Gaussian Kernel. The efficiency of the Proposed Model have been compared with existing Image Clustering Models, like, Fuzzy C-means Clustering, Agglomerate Clustering, etc. © 2024 The Authors. Published by Elsevier B.V.

关键词： Image segmentation

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Road Accident Analysis and Classification System 2

Road Accident Analysis and Classification System

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Ayush Pratap Srivastava, Arth Jain, Anuj Khatter, Harsh Kiet Group of Institutions Department of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350371055

Road accidents persist as a critical global concern, resulting in substantial loss of life and economic burden. This research paper delves into the underlying causes of these accidents, which often stem from a multitude of factors including weather conditions, road surface conditions, and driver behavior. Leveraging machine learning algorithms and data visualization tools, we have developed a predictive model that identifies accident severity. The system integrates real-time weather data, providing a dynamic and comprehensive view of road conditions. By accurately forecasting accident severity, we aim to significantly reduce response times for emergency services and enhance overall traffic safety. This paper provides an overview of our approach, detailing the technologies employed and their potential impact on mitigating the pervasive issue of road accidents. © 2024 IEEE.

关键词： Machine Learning Algorithms Predictive Model Road Accidents Road Surface Conditions Weather Conditions

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Supervised Machine Learning in Cardiology: A Predictive Model for Heart Disease 11

Supervised Machine Learning in Cardiology: A Predictive Mode...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mall, Anamika Singh, Ankita Bansal, Ayush Gupta, Hriday Kumar Kiet Group of Institutions Department of Computer Science and Engineering UP Ghaziabad India

ISBN: (纸本)9798350350357

One of the worst diseases in the world, heart disease takes a great toll on lives every year. In order to treat this potentially fatal illness, a tool or gadget must be practical, accurate, and dependable. This will allow for prompt detection or prediction, which will lead to efficient treatment and a decline in death rates. The heart is the most important organ in the human body, second only to the brain, and it is vital to the circulation of blood to all organs. A swift and accurate diagnostic method is imperative to mitigate the high mortality associated with cardiac disorders. Predicting the incidence of these disorders holds paramount importance in the medical domain. In the contemporary era, machine learning techniques prove invaluable for predicting and automating the interpretation of extensive and intricate datasets in various fields, including medicine. This research introduces a statistical model for heart disease, aiding medical examiners and cardiac practitioners in forecasting based on fundamental aspects of a patient's health history. The model, constructed using a Decision Tree, achieves an impressive accuracy of approximately 97%. The crucial functions of machine learning, data analytics, and data mining highlight how important these technologies are to improving the diagnosis and treatment of cardiac disorders. © 2024 IEEE.

关键词： Decision trees

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Emerging Security Challenges in Advanced Cloud Computing Environment 2

Emerging Security Challenges in Advanced Cloud Computing Env...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Kumar, Anshul Tiwari, Gagan Malviya, Aadarsh Noida International University School of Sciences Department of Computer Science Greater Noida India Dronacharya Group of Institutions Department of Computer Science Greater Noida India

ISBN: (纸本)9798350390179

Cloud computing is a archetype that permits users to access a shared pool of computing resources on an on-demand or pay-per-use basis. It offers numerous benefits to individuals and organizations, including significant cost savings in both capital and operational expenditures. However, there are several obstacles that prevent widespread adoption of cloud computing, with security being a major concern. Insufficient security measures can have negative consequences for this computing model, consequential in individual, ethical, and economic harm. The security issues that various cloud entities - such as cloud facility providers, data holders, and cloud users - face will be covered in this paper. It will specifically focus on the crypto-cloud, which encompasses various aspects of announcement, multiplication, and service level agreements (SLAs). By examining the causes and effects of different cyber-attacks, the paper aims to propose necessary security enhancements. © 2024 IEEE.

关键词： Cloud computing Crypto-cloud Issues in cloud Virtualization Security challenges

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