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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Mondal, Sagar Debbarma, Anindita Prakash, B. Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai India Srm Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350353068

In today's competitive retail landscape, supermarkets like Big Marts meticulously track the sales data of each product to anticipate consumer demand and optimize inventory management. By analyzing this data, including identifying anomalies and trends through data mining techniques, retailers can develop predictive models using advanced machine learning algorithms. Techniques such as Xgboost, Linear Regression, Polynomial Regression, and Ridge Regression are employed to forecast sales volumes with greater accuracy than traditional methods. Through the application of these models, businesses like Big Mart can make informed decisions to enhance their operational efficiency and strategic planning, ensuring they stay ahead in the dynamic market environment. © 2024 IEEE.

关键词： Sales

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Optimizing Load Balancing using Deep Learning in a Cloud Environment 18

Optimizing Load Balancing using Deep Learning in a Cloud Env...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Dastagiraiah, C. Reddy, Vuyyuru Krishna Rani, K. Pushpa Veeranna, T. Anurag University Department of Computer Science Engineering Hyderabad India Gitam University Department of Cse Rushikonda Visakhapatnam India Mlr Institute of Technology Computer Science and Engineering Hyderabad India Sai Spurthi Institute of Technology Department of Cse Telangana Hyderabad India

ISBN: (纸本)9789380544519

In the context of cloud computing, this research emphasizes how deep learning methods have been used into load balancing. Traditionally, load balancing methods have been developed using hybrid approaches, such as Predict-Earliest-Finish Time with Ant Colony Optimization (ACO) for low-utilized VM optimization and the hybrid Bat technique for overflow VM optimization. However, there has been limited exploration of deep learning approaches in load-balancing research until recently. The Dynamic Virtual Machine Provisioning using Deep Learning (DVMPDL) framework has been proposed and implemented to address this gap. This framework leverages deep learning methodologies to make precise and effective resource allocation decisions, aiming to optimize quality of service (QoS) metrics like throughput, resource utilization, latency, response times, and costs in cloud environments. Specifically, DVMPDL utilizes deep learning techniques to determine the best schedule for virtual machines, taking into account the nature of genome workflow tasks. By intelligently allocating resources based on workload characteristics and system constraints, DVMPDL aims to improve system reliability and performance. The results of the proposed DVMPDL framework are evaluated in terms of make span and cost, comparing them with previously suggested load balancing techniques such as the deadline-constrained, energetic VM provision, and load balance methods. By integrating deep learning into load balancing research, this work aims to enhance the efficiency and effectiveness of resource allocation in cloud environments, ultimately improving overall system performance and user experience. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： and Convolution Neural Networks Deep Learning Load balancing Resource provisioning framework Workflows

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PCB Test-Pad Detection and Clustering using PySpark and Image Processing 3

PCB Test-Pad Detection and Clustering using PySpark and Imag...

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3rd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2025

作者： Gupta, Shikha Panchal, Bhavesh Shaikh, Uzair Afnan Sahane, Arya Pednekar, Kasturi Wankhade, Akansha Rajiv Gandhi Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798331521691

The era of Big Data demands effective and efficient processing and clustering of extensive image data. This paper presents a Spark-based approach to analyze, group, and visualize spatial and color information within image datasets. The method begins with initializing a Spark session, then loading and inspecting the dataset containing the required spatial coordinates (X, Y) and the corresponding RGB color values. We derive statistical insights into the color distributions and perform color-based clustering to identify different color clusters, focusing on isolating greyscale clusters. Then, spatial clustering algorithms are used to derive the patterns in spatial data distribution, facilitating comprehension of color correlations within the spatial settings. Visualizations are created to aid with the understanding and interpretation of clustering results. This methodology presents an effective, scalable solution for analyzing large-scale spatial and colorbased data. © 2025 IEEE.

关键词： big data Bisecting K-means computer vision feature extraction pattern recognition PySpark RGB color analysis spatial clustering surface mount technology (SMT)

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Innovative Approach for Early Detection and Risk Assessment of Lung Cancer Using Machine Learning Techniques

Innovative Approach for Early Detection and Risk Assessment ...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Swain, Satyaprakash Jayasingh, Suvendra Kumar Patra, Kumar Janardan Tripathy, Alok Ranjan Institute of Management and Information Technology Cuttack BPUT Department of Computer Science and Engineering Odisha Cuttack India

ISBN: (纸本)9798350369175

Lung cancer remains an ongoing global health challenge, necessitating the development of innovative methods for early detection and risk assessment. This study presents a comprehensive approach for predicting lung cancer by utilizing a carefully curate dataset consisting of 1000 individuals from Kaggle dataset. The cutting-edge machine learning models, including Support Vector Machines (SVM), Naive Bayes Multinomial (NBM), Meta Bagging, PART and Random Forest (RF are used to enhance the accuracy of our predictions. The dataset we've compiled includes a wide array of patient characteristics, encompassing demographics, lifestyle factors, medical history, and health data gathered through IoT devices. By harnessing the capabilities of IoT technology, it is possible to enable real-time and continuous health monitoring, facilitating a dynamic assessment of lung cancer risk. The finding of this study reveal that the PART model achieves an impressive accuracy of 95%, surpassing other models like NBM (67%), RF (93%), SVM (92%), and Bagging (86%). This innovative approach shows promise in the early detection of lung cancer and the provision of personalized risk assessments, potentially leading to improved patient outcomes and reduced healthcare burdens. © 2024 IEEE.

关键词： Lung cancer

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A Secure Fingerprint Transmission Protocol for Insecure Communication Channels 1

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2nd International Conference on Intelligent Computing Systems and Applications, ICICSA 2023

作者： Singh, Laiphrakpam Dolendro Arshad Ahmed, Md. Louis, Teresa Ali, Azad Thounaojam, Dalton Meitei Patgiri, Ripon Singh, Khoirom Motilal Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India Department of CSE Indian Institute of Information Technology Manipur Senapati India

ISBN: (数字)9789819754120

ISBN: (纸本)9789819754113

A fingerprint is a recognizable pattern of ridges and valleys on a person’s finger surface. A person’s fingerprints are distinct and unchangeable throughout a person’s life unless distorted through external injury. The fingerprint is one of the most commonly utilized biometric identification techniques. As fingerprints are used in realistic authentication, securing fingerprints is a paramount concern. Moreover, enciphering the whole fingerprint image collected through a fingerprint scanner would require extensive computation. Research has shown that minutiae points are used to determine the uniqueness of a fingerprint. A method is proposed to store and transmit fingerprint data securely. The proposed technique enciphers only the internal angles subtended by the relevant minutiae points and the information about the type of minutiae points. The proposed techniques reduce the computation involved as only the internal angles information and type of minutiae points are enciphered rather than the whole scanned fingerprint image. Statistical and security analyses conducted on the cipher data suggest the proposed encryption is secure. A digital signature is incorporated while transmitting the fingerprint information, thereby maintaining the data integrity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication Protocol

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Securing Neural Network-Based Personalized Medicine for Advanced Liver Cancer Detection in Healthcare

Securing Neural Network-Based Personalized Medicine for Adva...

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

作者： Kasturi, Salvadi Singh, Jagendra Agarwal, Mohit Kumar, Ashwini Mishra, Komal Sharma, Yogita Cvr College of Engineering Department of Computer Science Engineering Vastunagar India School of Computer Science Engineering & Technology Bennett University Greater Noida India Compucom Institute of Technology & Management Department of Computer Science Engineering Jaipur India Chandigarh University Department of University Institute of Computing Chandigarh India School of Management and Commerce Manav Rachna University Faridabad India

ISBN: (纸本)9798350382693

This research is aimed at discussing the critical meeting point of healthcare and artificial intelligence, including the need to protect the required treatment solution. Investigation into the utility of various deep learning techniques such as VGG 16, VGG 19, Convolutional Neural Networks (CNN), and Recurrent Neural Network (RNN) for the prediction of liver cancer stages and their respective treatments has been completed. The generation of 2340 of medical images from multiple sources, including the internet and records of hospitals, as a dataset was performed in a secure cloud. The security measures have allowed only authorized personnel to access the data and ensured the privacy and secrecy of data. The created dataset was then separated into two parts: training, which consisted of training the model on 70% of images, and testing, which implied that 30% of images were used to test the generated model. For each of the run models, performance evaluation measurements such as accuracy, precision, recall, and F1 score were calculated. The results have shown the highest efficiency of VGG 19 model, which resulted in 98.44% of accuracy. For the generated model, a fictitious ROC curve and specificity rate were also provided to showcase the discrimination abilities of each of the submitted models. The results show that the utility of neural network-based personalized medicine is beneficial for the further progression of the early prediction stage of liver cancer and its treatment. The result showed that the proposed models has successfully contributed to the field of healthcare informatics and further opportunities for artificial intelligence application to increase patient outcomes in the form of personalized medicine that may be performed. © 2024 IEEE.

关键词： Convolutional neural networks

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Ontology-based video retrieval using modified classification technique by learning in smart surveillance applications

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 55-64页

作者： Sathiyaprasad, B. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Jeppiaar Nagar Rajive Gandhi Salai Chennai600 119 India

With the rapid advancements in communication and multimedia computing technology, multimedia information, particularly video data, has recently become disproportionately accessible. Video is widely used in a variety of applications, making efficient management and retrieval of the expanding volume of video data critical. To manage such tasks, include automated recognition of the image queries in video retrieval with a reduced degree of system memory usage, spot detection, maintenance time, identification of exact duplicate videos, and so on. This research determination demonstrates the Modified R-Ratio with Viola-Jones Classification Method (MRVJCM) with the relevance of developing techniques and algorithms for automatic recognition of image queries. The R-Ratio Viola–Jones framework has two distinguishable feature maintenance processes, such as Feature Selection and Integration and Feature Cascading. These two distinct features are applied to the motion features (texture, emotions, elements, and shape) within the three features. The proposed techniques and their relevant algorithms are used to retrieve the most accurate videos and to assure the mathematical operation of video retrieval in the operation of the comparable protected system. As a result, the proposed MRVJCM achieves 98% of accuracy, 93% of precision, 92% of recall, and 42% of RMSE. © 2023

关键词： Ontology

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Leveraging AI for enhanced cybersecurity: a comprehensive review

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Discover Applied Sciences 2025年第6期7卷 1-27页

作者： Swetha, Thammisetty Kumaran, U. Meena, V.P. Hameed, Ibrahim A. Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical Engineering National Institute of Technology Jamshedpur Jharkhand Jamshedpur831014 India Department of ICT and Natural Sciences Norwegian University of Science and Technology Trondheim7034 Norway

Cybersecurity has become a serious concern in this digital age, with ever-increasing threats aimed at individuals, enterprises, and countries across the globe. AI has been rapidly emerging as an influential technology that possess enormous abilities to enhance data protection measures by automating threat recognition, vulnerability assessment, and incident response. This study focuses on comprehensively examining current literature works dedicated to AI along with its applications in cybersecurity and presents an innovative architecture to deal with challenges that pose enormous risks to worlds community. Explore advanced techniques, including Machine learning (ML) and deep learning (DL), and their implementation in threat intelligence, intrusion recognition, malware analysis, phishing detection, and user authentication. Furthermore, the propounded review deliberates on various limitations such as data scarcity, model interpretability, and vulnerability to adversarial attacks can hinder the effectiveness of existing models in real-world cybersecurity scenarios, leading to reduced accuracy, slower response times, and increased susceptibility to evasion techniques. The future advances, including the evolution of explainable AI, predictive analytics, quantum computing, and real-time management capabilities, are also emphasized. This review paper is intended to offer a systematic understanding of modern AI techniques and prospective solutions in enriching the cybersecurity domain, offering valuable insights for researcher scholars and professionals in diverse fields. © The Author(s) 2025.

关键词： Deep learning

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An Efficient Prediction System for Heart Diseases Using Machine Learning Algorithms 2

An Efficient Prediction System for Heart Diseases Using Mach...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Archana, P. Shashikala, S.V. Department of Computer Science and Engineering BGS Institute of Technology Adichunchanagiri University BG Nagara Mandya India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

One of the most severe and demanding conditions that the arena is currently facing is heart illness. Forecasting cardiovascular illness is one among the most challenging pursuits in the world of medical facts analysis. Machine learning classification algorithms has been shown to be useful in making predictions and options that are solely dependent on substantial amounts of data. This study offers a technique to predict and classify if a person has coronary heart disease and to inform the affected individual about their risk through records or a diagnosis. The prediction version is built using a variety of options and numerous additional classification techniques. This is accomplished by comparing the accuracy of many algorithms to the outputs of each version independently, then choosing the method with the highest forecast accuracy. This paper's main goal is to use data analytics to predict whether or not contamination will persist for the foreseeable future. © 2024 IEEE.

关键词： Prediction models

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An oceanographic data collection scheme using hybrid optimization for leakage detection during oil mining in mobility assisted UWSN

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Multimedia Tools and Applications 2024年第42期83卷 89723-89741页

作者： Choudhary, Monika Goyal, Nitin Gupta, Deepali Sharma, Bhanu Sharma, Nonita Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Seth Jai Parkash Mukandlal Institute of Engineering and Technology Haryana Radaur India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendragarh India Department of Information Technology Indira Gandhi Delhi Technical University for Women Delhi India Immersive and Interactive Technology Lab Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

Data acquisition is the process of collecting, measuring and analysing information using standardised, validated techniques for application-specific tasks. In mobility-assisted underwater wireless sensor networks (UWSNs), where nodes are not fixed due to water current of 3 m/sec, data collection becomes an arduous task. There are few works that provide a mobile sink with optimised data transmission path planning and scheduling. These systems do not transmit the data fast enough to provide real-time data transmission as these methods do not consider the buffer occupancy rate and latency in data acquisition. In this paper, a stimulating transmission path planning technique for mobile sinks using the hybrid Grey Wolf Optimizer Whale Optimization Algorithm (GWOWOA) is proposed. In contrast to other optimization techniques, this hybrid technique includes a number of update processes such as random position update, prey search by the Grey Wolf Optimizer (GWO) and prey search by the Whale Optimization Algorithm (WOA). In this paper, the fitness function is calculated in terms of distance to the mobile sink, buffer occupancy rate, energy level and data acquisition latency. The use of these variables makes the proposed technique innovative. To prove the efficiency of the proposed system, GWOWOA is compared with existing systems. The simulation results show that the proposed system increases the residual energy and accuracy of the collected data and minimises the delay. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data acquisition

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