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Appearance and motion-based unusual crowd events detection using multiple moving objects

Multimedia Tools and Applications 2024年第42期83卷 90177-90189页

作者： Babu, K. Atlas, L. Godlin Sreeji, S. Arjun, K.P. Rasmi, A. Senthilvel, P. Gururama Computational Intelligence SRM Institute of Science & Technology Chengalpet Kattankulathur603203 India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gitam University Karnataka Bengaluru India Information Science and Engineering RRIT Bangalore India Saveetha University Chennai602 105 India

Unusual crowd analysis is an important problem in surveillance video due to their features cannot be extracted efficiently on the crowd scenes. To overcome this challenge, this paper introduced the appearance and motion based unusual events detection method by using multiple moving objects. This method includes appearance and motion based computation, and moving objects trajectory analysis. By using these all techniques, the unusual crowds and their activities can be obtained by analyzing moving objects size and the distance among multiple moving objects. Two benchmark datasets such as PETS 2009 and UMN are used for the experimental results. Also, several existing algorithms are compared with the proposed method to evaluate the effectiveness of the proposed work. The comparative analysis results proved that the proposed method achieved best accuracy for unusual crowd events. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： And trajectory analysis Appearance based analysis Distance based analysis Unusual crowd events detection

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Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

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SN computer science 2023年第1期4卷 72页

作者： Theerthagiri, Prasannavenkatesan Ruby, A. Usha Vidya, J. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru Bengaluru India School of Computing Science and Engineering VIT Bhopal University Bhopal India

Diabetes mellitus is characterized as a chronic disease that may cause many complications. Machine learning algorithms are used to diagnose and predict diabetes. The learning-based algorithms play a vital role in supporting decision-making in disease diagnosis and prediction. This paper investigates traditional classification algorithms and neural network-based machine learning for the diabetes dataset. Also, various performance methods with different aspects are evaluated for the K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron algorithms. It supports the estimation of patients who possibly suffer from diabetes in the future. This work shows that the multilayer perceptron algorithm gives the highest prediction accuracy with the lowest MSE of 0.19. The MLP gives the lowest false-positive and false-negative rates with the highest area under the curve of 86%. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classification of diabetes Diabetes prediction Machine learning algorithm MLP

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Next-Gen Image Shielding: Deep Learning's Stealthy Image Encryption for Health-Tech IoT

Next-Gen Image Shielding: Deep Learning's Stealthy Image Enc...

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2023 International Conference on Recent Advances in science and engineering.Technology, ICRASET 2023

作者： Nadhan, Archana S Jacob I, Jeena Gitam Deemed to Be University Gitam School of Technology Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350306927

The healthcare sector has undergone a transformation with the advancement of the Internet of Things, facilitating the seamless incorporation of medical imaging devices within the industry's data systems, leading to faster diagnostics and treatments. Maintaining the privacy of patients is crucial and complex, especially when medical images can reveal intimate and private details about a patient's medical history. We explore the usage of advanced Deep Learning methods for encrypting medical diagnostic images, leveraging a cryptographic-based image encryption and decryption framework. The Proposed approach uses a simple key learning model built on ResNet-50 architecture to convert one visual format into another. This model specifically incorporates a domain known as "hidden factors"during the encryption phase. To revert the encrypted image to its original form, we employ reconstructive networks. By extracting relevant data from the encrypted content and enabling direct data mining from the personal data ecosystem, we offer a system that ensures a good return on investment. Additionally, the suggested method incorporates the Enhanced Encryption technique using a stream cipher. We validated the new approach using two publicly available datasets. Preliminary evaluations suggest that the method provides superior security and impressive operational efficiency. © 2023 IEEE.

关键词： Cryptography

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Intelligent Techniques for Nutrition Level Prediction in Guava Plants

Intelligent Techniques for Nutrition Level Prediction in Gua...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Nadiger, Rakshith S Suresh, Gautham Shobha Rani, N. Prabhu, Akshatha Sangamesh, M.A. Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Mysuru India Gitam University Gitam School of Technology Department of Computer Science & Engineering Bengaluru India National Institute of Engineering Department of Chemistry Mysuru India

ISBN: (纸本)9798350361537

Guava, a valuable tropical fruit crop, faces significant defect challenges due to diseases. Manual disease checks are time- consuming and error prone, leading to delayed action. Our research focuses on image processing to quickly and accurately identify guava diseases using deep learning approach with the help of Guava leaf images. This automation minimizes human intervention, enhances accuracy, and aids disease prevention. Early detection of these diseases improves crop quality and sustainability while reducing pesticide use. This Deep learning- driven approach aims to benefit farmers, consumers, and global food security by advancing guava farming practices. By using the YOLOv8 object detection model for 6 classes, we got an accuracy of 87.97%. © 2024 IEEE.

关键词： Agricultural Sustainability Deep Learning Guava Plant Disease Detection Nutrition level prediction YOLOv8

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Artificial intelligence based machine learning algorithm for prediction of cancer in female anatomy

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Multimedia Tools and Applications 2025年第16期84卷 15735-15761页

作者： Ghantasala, G S Pradeep Hung, Bui Thanh Chakrabarti, Prasun R, Sathiyaraj Pellakuri, Vidyullatha Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh city Ho Chi Minh City Viet Nam Deputy Provost ITM SLS Baroda University Gujarat Vadodara India Department of CSE GITAM School of Technology GITAM University Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India

Breast and cervical cancers account for more than 85 percent of all cancer-related fatalities in developing nations, according to the World Cancer Research Fund. As a result, breast and cervical cancer have become one of the leading causes of mortality among women worldwide. This field is still in its infancy, with only a few studies in gynaecology and computer science looking into the detection of breast and cervical cancer. According to the researchers, medical records and early testing from individuals with breast and cervical cancer will be used in this study to determine the prognosis of those suffering from the diseases. To assess our cervical cancer predictions, we employed machine learning models such as Optimized Hybrid Ensemble Classifier (OHEC), which were trained on patient behavior and variables revealed to be associated with patient behavior. The datasets in this study have a substantial number of missing values, and the distribution of those values has been altered as a function of the missing values. OHEC classifier performance has been shown to improve when the number of features is reduced and the problem of high-class imbalance is resolved, because the accuracy, sensitivity, and specificity of the classifier, as well as the number of false positives, were used to demonstrate the success of feature selection in the suggested model's predictive analysis. This has been demonstrated through the use of numerous tests involving categorization challenges. The study underscores the critical significance of early detection and prognosis in combating breast and cervical cancers, which remain leading causes of mortality worldwide. Through the utilization of machine learning models like the OHEC, the authors have demonstrated the potential for improved predictive accuracy and clinical outcomes. The findings highlight the importance of addressing challenges such as missing data and class imbalance in enhancing the performance of predictive models for effective

关键词： Diseases

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Deep Learning Feature Extraction Architectures for Real-Time Face Detection

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SN computer science 2023年第5期4卷 645页

作者： Ravi Teja, B. Mythili, D. Duvva, Laxmiprasanna Bethu, Srikanth Garapati, Yugandhar Department of Computer Science and Engineering GITAM School of Technology GITAM Deemed-to-be University Telangana Hyderabad 502329 India Department of Computer Science and Engineering Vasavi College of Engineering Telangana Hyderabad 500089 India Department of Computer Science and Engineering CVR College of Engineering Telangana Hyderabad 501510 India

A Video Surveillance system can be used for a variety of purposes, including protection, secure data, crowd flux analytics and congestion analysis, individual recognition, anomalous activity detection, and so on. Video Surveillance systems play the key role in the human detection using the face features extraction. It helps in many applications like terrorists attack, thief identifying by detecting the face of the person but mostly failed in real-time aspect. In this context, we propose a method that significantly aids in the extraction and learning of features. To reduce the face recognition error, we use a bounding box regression model. To train the features, we utilized a CNN-based feature learning model with log-likelihood ratio calculations between inter- and intra-features. To increase the quality of video frames, we used a histogram redistribution image enhancement technique. Finally, a Background Subtracted Faster RCNN for video-based face recognition (BSF-RCNN-VFR) is used to discriminate the groups of detected faces. A comprehensive experiment is carried out on their datasets to demonstrate that the proposed solution performs better, and we compared the existing models with proposed models. We achieved 94.2 accuracy percentage. In this paper, the CNN models like AlexNet, ResNet and datasets like UADFV, Celeb-DF, FF++, DFDC, etc., accuracies are compared. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： CNN Deep Learning Feature extraction

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Early-Stage Disease Prediction in Chilli Plant Using YOLO Models 2

Early-Stage Disease Prediction in Chilli Plant Using YOLO Mo...

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

作者： Manoj, R. Girish, Sandra Pushpa, B.R. Rani, N. Shobha Sangamesha, D. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Computer Science and Engineering Gitam School of Technology Gitam University Bengaluru India Department of Chemistry The National Institute of Engineering Mysore India

ISBN: (纸本)9798350383867

Agriculture is an important component of every country's economy, supplying the necessary resources to farmers and their families. The livelihoods of farmers are greatly threatened by crop diseases, which highlights the importance of early identification of diseases in chilli plants at the right stage of growth is crucial for timely fertilizer recommendation. This paper presents an intelligent transfer learning technique to detect thrips and viruses in chilli plants in the early stages of development. Stage-by-stage datasets are collected from chilli plant orchards around Mysore district, Karnataka, to carry out the training using the YOLO model. The dataset consists of three growth stages of chilli plants (15, 25, and 35 days) that are captured in diverse environmental settings with multiple-resolution smart phones. The annotations are created for the diseases healthy, viruses, and thrips at leaf level from whole plants for all three stages. Hyperparameters such as learning rate, the learning optimization algorithm, batch size, epochs, and loss functions are employed to fine-tune the YOLO v5, v6, and v7 models. To achieve high generalisation and reduce overfitting, a weight decay (weight_decay) of 0.0005 is employed. From the evaluation, it was noticed that YOLOv7 outperformed other YOLO models. A mAP score of 0.80 is achieved in stage 1, 0.82 in stage 2, and 0.73 in stage 3 with minimal validation loss and remarkable inference speed. © 2024 IEEE.

关键词： Plant diseases

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Comprehensive Overview and Analytical Study on Automatic Bird Repellent Laser System for Crop Protection

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Journal of Harbin Institute of Technology(New Series) 2024年第1期31卷 38-53页

作者： Sireesha Abotula Srinivas Gorla Prasad Reddy PVGD Mohankrishna S Department of Computer Science and Engineering GITAM Deemed to be UniversityVisakhapatnam 530045India Department of Information Technology Andhra UniversityVisakhapatnam 530003India Department of Computer Science and Systems Engineering Andhra UniversityVisakhapatnam 530003India

Birds are a huge hazard to agriculture all around the world,causing harm to profitable field *** use a variety of techniques to keep them away,including visual,auditory,tactile,and olfactory deterrents. This study presents a comprehensive overview of current bird repellant approaches used in agricultural contexts,as well as potential new ways. The bird repellent techniques include Internet of Things technology,Deep Learning,Convolutional Neural Network,Unmanned Aerial Vehicles,Wireless Sensor Networks and Laser biotechnology. This study’s goal is to find and review about previous approach towards repellent of birds in the crop fields using various technologies.

关键词： Bird repellent crop protection IoT UAV Deep Learning

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Software Defect Prediction Based Ensemble Approach

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computer Systems science & engineering 2023年第6期45卷 2313-2331页

作者： J.Harikiran B.Sai Chandana B.Srinivasarao B.Raviteja Tatireddy Subba Reddy School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237India Department of Computer Science and Engineering GITAM Deemed to be UniversityTelangana502329India Department of Computer Science and Engineering B.V.Raju Institute of Technology NarsapurMedakTelangana502313India

Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance *** it comes to lowering software costs and assuring software quality,SDP plays a critical role in software *** a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more *** methods for detecting and addressing such flaws at a low cost have been *** approaches,on the other hand,need to be significantly improved in terms of *** in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are *** proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative *** find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository *** compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

关键词： Prediction of a software defect deep learning models enhancedWOA firefly algorithm

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A comprehensive survey of text-based semantic similarity with potential applications

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International Journal of Intelligent Information and Database Systems 2025年第2期17卷 143-185页

作者： Sharma, Puneet Rao, K. Yogeswara Kavitha, P. Sakthivel, T. Unitedworld Institute of Technology Karnavati University Gujarat Gandhinagar India Department of Computer Science and Engineering GITAM School of Technology GITAM Rushikonda Visakhapatnam India Department of CSE PES University Karnataka Bengaluru India Firstsoft Technologies Pvt. Ltd. Chennai India

Text-based semantic similarity has recently become a popular research topic, and estimating the semantic similarity between entities for learning and decision-making is a challenging research problem. Semantic similarity quantitatively measures the informativeness between the data objects based on their properties and relationships. Due to the vast availability of measures developed by domain experts from different research fields, choosing an appropriate semantic similarity measure that suits a specific application context is challenging. The primary objective of this systematic literature survey is to offer a comprehensive view of semantic similarity and potential applications that emphasise the enormous diversity of research contributions in a wide range of fields. In this context, it examines various categories of semantic similarity based on the underlying principles of the state-of-the-art approaches with pros and cons. Notably, the prospective application areas are explored extensively by highlighting their significance. Finally, the survey concludes by summarising valuable future research directions. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Latent semantic analysis

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