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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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Advancing Android Malware Detection with BioSentinel Neural Network using Hybrid Deep Learning Techniques 18

Advancing Android Malware Detection with BioSentinel Neural ...

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

作者： Rani, D. Sandhya Gnaneshwar, K. Sampurnima Pattem, K. Sekhar, Soma Krishna, G. Bala Kakarla, Shirisha Cvr College of Engineering Department of Computer Science Engineering Rangareddy Ibrahimpatnam India Sreenidhi Institute of Science and Technology Department of Computer Science Engineering-Cyber Security Hyderabad India Vardhaman College of Engineering Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9789380544519

The paper introduces the BioSentinel Neural Network (BSNN), a novel hybrid deep learning model designed to enhance malware detection, particularly focusing on zero-day threats. The BSNN model integrates diverse neural network architectures, leveraging Graph Neural Networks (GNNs) for feature extraction and Transformers for sequential data analysis. It demonstrates significant improvements in accuracy (93.16%), precision (90.89%), recall (88.19%), and F1-score (90.45%) over traditional methods. The model exhibits a high detection rate of 88% for zero-day malware from 1000 samples tested and shows computational efficiency with an average inference time of 30 milliseconds. This research addresses critical gaps in current malware detection approaches, highlighting the potential of hybrid deep learning techniques in the cybersecurity domain. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Cybersecurity Deep Learning Hybrid Neural Networks Malware Detection Real-Time Analysis Zero-Day Threats

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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TIKI-TAKA Optimized Algorithn Based on an Enhanced Security System for Teleradiology in DICOM using Two-Level DWT Watermarking

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International Journal of COMADEM 2023年第3期26卷 47-54页

作者： Karthiyayini, S. Muthumari, A. Department of Information Technology Mohamed Sathak Engineering College Tamilnadu Kilakarai India Department of Computer Science and Engineering Syed Ammal Engineering College Tamilnadu Ramanathapuram India

Tele radiology plays a significant role in medicine that allows secure transmission of medical imaging data over a wide range of communication network. It guarantees data reliability and enables convenient way of communication for diagnostic purposes and clinical interpretation. However it raises the issue of legal, privacy, ethical issue and other copyright authenticity. Hence security and privacy remains to be a predominant problem in the medical field. To address this issue the present study has been motivated by the concept of watermarking schemes. Improved tele radiology architecture was developed in this study with efficient watermarking scheme that incorporates the integrated activity of biometrics and cryptography. In accordance to the entropy model, the fingerprint feature obtained from DICOM image is retrieved for key generation. 2L-DWT and TT-OA algorithm is employed to obtain high efficiencywith respect to PSNR (77 dB), 121s of encryption, SSIM (0.99), and embedding efficiency (97%). © 2023 COMADEM International. All rights reserved.

关键词： Discrete wavelet transforms

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A Comprehensive Approach for Early Detection of Leaf Infection 5th

A Comprehensive Approach for Early Detection of Leaf Infecti...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Priya, L. Kiruthika, A. Kumar, P. Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819788606

The agriculture industry is fundamental to the foundation of a country and is essential to the promotion of economic prosperity. It is crucial to ensure that Monitoring the health and detecting leaf infections in plants is crucial for maintaining sustainable agriculture. The actions taken to identify leaf infection represent a significant challenge in the agricultural industry. Manually recognizing plant diseases by analysing leaf images can be a time-consuming undertaking. In response to this challenge, continuous advancements in technology, particularly in image detection, have become instrumental. These advancements enable the accurate and prompt recognition of leaf infections and pests, facilitating the development of early treatment strategies. The paper objective is to precisely recognize diseases in leaf images. The essential steps in this procedure involve pre-processing, training, and recognition. Artificial Intelligence emerges as a ground breaking innovation, empowering the training of machines with image datasets to autonomously identify diseases in crops. Using this proposed methodology this system aims to improve the speed, accuracy, and overall effectiveness in identifying and managing crop infections across diverse agricultural settings through the utilization of machine learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Agricultural economics

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A two-stage CNN model for the classification and severity analysis of retinal and choroidal diseases in OCT images

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International Journal of Intelligent Networks 2024年第1期5卷 10-18页

作者： George, Neetha Shine, Linu N, Ambily Abraham, Bejoy Ramachandran, Sivakumar Department of Electronics and Communication Engineering Rajiv Gandhi Institute of Technology Kottayam Kerala India Department of Electronics and Communication Engineering College of Engineering Trivandrum Kerala India Department of Electronics and Communication Engineering Government Engineering College Idukki Kerala India Department of Computer Science and Engineering College of Engineering Muttathara Kerala Thiruvananthapuram India Department of Electronics and Communication Engineering Government Engineering College Wayanad Kerala India

The advancements in medical imaging techniques have brought exponential increase in the quantity and complexity of data which often require human expertise for interpretation and decision making. However, in real-world clinical settings, there is often a shortage of experts available for timely diagnosis and triage. An automated diagnostic technique aids clinicians in the precise diagnosis and effective management of diseases. In this article, a two-stage classification model using convolutional neural network (CNN) is proposed for the classification and severity analysis of retinal and choroidal diseases in optical coherence tomography (OCT) images. The proposed model can identify abnormal conditions such as Pachychoroid disorders, macular edema, and Drusen. The images are initially classified into four categories-healthy, Drusen, Pachychoroid and macular edema classes. The severity of Pachychoroid conditions, including central serous chorioretinopathy, polypoid choroidal vasculopathy, and choroidal neovascularization, is subsequently determined through a second-level classification. A modified version of the VGG16 architecture is used for the initial classification. A fine-tuned CNN with the same architecture is then employed to determine the severity of Pachychoroid diseases. Further, we make use of the UNet architecture for assessing the severity of macular edema. The proposed hybrid approach for classification and analysis achieved promising results, demonstrating consistency on par with human experts in diagnosing and grading ocular diseases. © 2024

关键词： Optical tomography

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Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

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computer Systems science & engineering 2023年第6期45卷 2795-2810页

作者： R.Sabitha C.Gokul Prasad S.Karthik Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,***,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to *** recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and *** traditional models,the solution has been made for defending the networks,only to specific ***,in real-time applications,the kind of attack that is launched by the adversary is not ***,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be *** that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various ***,for efficient model design,the work includes the part of feature evaluation of some general attacks of *** IDRM also includes determination of optimal secure paths and Node security for secure routing *** performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN *** is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

关键词： Enhanced security model wireless sensor networks improved defensive routing mechanism secure paths node security

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Optimizing abnormality detection in fundus images with Triplet-OS and orchard search optimization model

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Neural Computing and Applications 2024年第36期36卷 23181-23194页

作者： Venkatraman, K. Hemalatha, R. Radhika, S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha Nagar Thandalam Chennai India Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India Department of EEE Sathyabama Institute of Science and Technology Chennai India

The accurate and early detection of abnormalities in fundus images is crucial for the timely diagnosis and treatment of various eye diseases, such as glaucoma and diabetic retinopathy. The detection of abnormalities in fundus images using traditional methods is often challenging due to high computational demands, scalability issues, and the requirement of large labeled datasets for effective training. To address these limitations, a new method called triplet-based orchard search (Triplet-OS) has been proposed in this paper. In this study, a GoogleNet (Inception) is utilized for feature extraction of fundus images. Also, the residual network is employed to detect abnormalities in fundus images. The Triplet-OS utilizes the medical imaging technique fundus photography dataset to capture detailed images of the interior surface of the eye, known as the fundus and the fundus includes the retina, optic disk, macula, and blood vessels. To enhance the performance of the Triplet-OS method, the orchard optimization algorithm has been implemented with an initial search strategy for hyperparameter optimization. The performance of the Triplet-OS method has been evaluated based on different metrics such as F1-score, specificity, AUC-ROC, recall, precision, and accuracy. Additionally, the performance of the proposed method has been compared with existing methods. Few-shot learning refers to a process where models can learn from just a small number of examples. This method has been applied to reduce the dependency on deep learning [1]. The goal is for machines to become as intelligent as humans. Today, numerous computing devices, extensive datasets, and advanced methods such as CNN and LSTM have been developed. AI has achieved human-like performance and, in many fields, surpasses human abilities. AI has become part of our daily lives, but it generally relies on large-scale data. In contrast, humans can often apply past knowledge to quickly learn new tasks [2]. For example, if given

关键词： Medical imaging

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An Automated Bird Species Classification Using Deep Learning 2

An Automated Bird Species Classification Using Deep Learning

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

作者： Saritha, N. Shruthi Department of Computer Science and Engineering Adichuchanagiri Institute of Technology Chikkamagalur India Department of Computer Science and Engineering Navkis College of Engineering Hassan India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Birds are nice-looking from their aesthetic looks and life styles, around 50 billion of birds are living on the earth where some birds are in threat of extinction. For people, distinguishing and classification of bird species appears to be troublesome with exposed eye. In this document, we aim to construct a deep learning-driven approach. an automated bird species identification system using convolution neural network (ResNet50V2 architecture) and with assorted dataset. This system will able to identify and classify the bird species with an accuracy of 99. ©2024 IEEE.

关键词： Convolutional neural networks

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Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2399-2412页

作者： G.Rajasekaran J.Raja Sekar Department of information technology mepco schlenk engineering collegesivakasi626005india Department of computer science and engineering mepco schlenk engineering collegesivakasi626005india

Abnormal behavior detection is challenging and one of the growing research areas in computer *** main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain *** this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the *** stage,OPLKT-EMEHOs algorithm is used to generate the opticalﬂow from *** stage,the MIIs opticalﬂow is applied as input to 3 layer CNN for detect the abnormal crowd *** of Minnesota(UMN)dataset is used to evaluate the proposed *** experi-mental result shows that the proposed method provides better classiﬁcation accu-racy by comparing with the existing *** method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.

关键词： Crowd behavior analysis anomaly detection Motion Information

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