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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Gomathy M A. Vidhya School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamilnadu Department of Information Technology School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamilnadu.

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Cybercrime has been a worldwide issue since the internet's inception. The various online crimes that take place have an impact on the general public. The various forms of cybercrime committed online and around the world are always evolving due to the development of the internet. In the current digital age, when malware diversity and volume are increasing rapidly, new methods must be used to identify malware more quickly and accurately. Malware, which includes worms, trojans, spyware, and adware, can have serious repercussions, including financial losses, data breaches, and the interruption of essential services. Manual heuristic inspection in malware analysis is neither efficient nor effective in keeping up with the rapid spread of malware or in analyzing new infections. Deep learning enhances automatic malware variant detection and classification as it provides better categorization by building neural networks with more potentially different layers. They have been applied to automate investigations using static and dynamic analysis, where malware with comparable behaviors is grouped and unidentified malware is categorized into families according to how close it is to the other malware. To analyze malware in a file, certain features from the PE header format may mislead the model into classifying malware as benign. However, when the same file is represented as an image, the distinct patterns in different sections make it easier to categorize the file as either malware or benign. Furthermore, as new malware variants emerge daily, the complexity of these patterns increases, making it difficult for traditional models to analyze them effectively. Our research proposes a deep learning model, MDCNN, to address the challenges faced by existing models in accurately categorizing executable files represented as images into malware or benign. This research study extracts various feature sets from malware image files, including patterns, sizes, and textures, we utilized a novel

关键词： Deep learning Analytical models Accuracy Transforms Feature extraction Malware Convolutional neural networks Trojan horses computer crime Spyware

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Medical image registration and its application in retinal images:a review

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Visual computing for Industry,Biomedicine,and Art 2024年第1期7卷 142-164页

作者： Qiushi Nie Xiaoqing Zhang Yan Hu Mingdao Gong Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Singapore Eye Research Institute Singapore 169856Singapore State Key Laboratory of Ophthalmology Optometry and Visual ScienceEye HospitalWenzhou Medical UniversityWenzhou 325027China

Medical image registration is vital for disease diagnosis and treatment with its ability to merge diverse informa-tion of images,which may be captured under different times,angles,or *** several surveys have reviewed the development of medical image registration,they have not systematically summarized the existing med-ical image registration *** this end,a comprehensive review of these methods is provided from traditional and deep-learning-based perspectives,aiming to help audiences quickly understand the development of medical image *** particular,we review recent advances in retinal image registration,which has not attracted much *** addition,current challenges in retinal image registration are discussed and insights and prospects for future research provided.

关键词： computer-aided diagnosis Medical image registration Deep learning Generative model Transformer Retina

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Brain tumor diagnosis from MR images using boosted multi-gradient support vector machine classifier

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Measurement: Sensors 2024年 32卷

作者： Kalaiselvi, S. Thailambal, G. Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Pallavaram Tamil Nadu Chennai600 117 India

A brain tumor develops as a result of uncontrolled and rapid cell proliferation. If not treated in its early stages, it might result in death. Despite several significant efforts and positive outcomes, accurate segmentation and classification remain challenging jobs. The variations in tumor size, shape, and location provide a substantial challenge for brain tumor diagnosis. Therefore, identifying brain tumors manually is challenging, time-consuming, and prone to mistakes. Consequently, there is now a need for high-accuracy automated computer-assisted diagnostics. This paper proposes a novel brain tumor detection method based on a machine learning classifier. Initially, the brain tumor images are collected from the "Magnetic Resonance Imaging (MRI)" database. In the preprocessing stage, anisotropic filtering and "Adaptive Histogram Equalization (AHE)" are performed to remove the noise and enhance the image contrast respectively. Then the images are segmented using "Enhanced Fruitfly Optimization-based Otsu segmentation (EFO-OTSU)". The feature extraction is done using "Principal Component Analysis (PCA)" and "Discrete Wavelet Transform (DWT)". We propose Boosted "Multi-Gradient Support Vector Machine (BMG-SVM)"to use the retrieved characteristics to divide the picture into the tumor and non-tumor sections. Further to enhance the classification performance, we employ the "Black Monkey Optimization (BMO)" algorithm. A few currently used approaches are contrasted with the simulation results of the suggested technique. The final findings show that the suggested technique outperforms the other methods in terms of effectiveness. © 2024 The Authors

关键词： Support vector machines

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Predictive Modelling of Rice Blast Disease Utilizing Ensemble Voting Classifiers in Machine Learning

Predictive Modelling of Rice Blast Disease Utilizing Ensembl...

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International Conference on Inventive Systems and Control (ICISC)

作者： A Revathi S Poonguzhali Department of Computer Science School of Computing Sciences VISTAS Vel’s Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

Rice is the second-most significant cereal plant, next to wheat, and the most commonly cultivated staple food in India. Rice production has declined in recent years due to the spread of several rice diseases, changes in weather conditions, and soil quality. Rice blast, also known as RB, is the most devastating of all rice plant diseases and causes a vast amount of annual loss. The key challenge is to minimize crop loss due to rice blast disease while also increasing agricultural output. It is critical to predict the disease ahead of time so that the required safeguards can be taken. As manual prediction of rice blast disease requires a huge amount of labor and time, it is a must to implement an automated system using machine learning (ML) techniques, which are widely used artificial intelligence (AI) methods for prediction models. In the proposed system, the sample data set of 15,400 has been fed as an input to the ML algorithms via Nïve Bayes, random forest, KNN, and voting classifiers for prediction. The results show that the ensemble method of combining the machine learning algorithm-based prediction technique offers improved accuracy than the random forest algorithm, with an accuracy of 99.5%. The proposed methodology will greatly benefit agriculturalists with increased profits and safeguard food for the country.

关键词： Temperature sensors Machine learning algorithms Accuracy Rain Crops Predictive models Prediction algorithms

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Detecting entanglement of quantum channels

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Communications in Theoretical Physics 2021年第11期73卷 77-84页

作者： Chaojian Li Bang-Hai Wang Bujiao Wu Xiao Yuan School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China Advanced Institute of Information Technology Peking UniversityChina Center on Frontiers of Computing Studies Department of Computer SciencePeking UniversityBeijing 100871China

Entanglement is the crucial resource for different quantum information processing *** conventional studies focus on the entanglement of bipartite or multipartite quantum states,recent works have extended the scenario to the entanglement of quantum channels,an operational quantification of the channel entanglement manipulation *** on the recently proposed channel entanglement resource framework,here we study a further task of resource detection—witnessing entanglement of quantum *** first introduce the general framework and show how channel entanglement detection is related to the Choi state of the channel,enabling channel entanglement detection via conventional state entanglement detection *** also consider entanglement of multipartite quantum channels and use the stabilizer formalism to construct entanglement witnesses for circuits consisting of controlled-Z *** study the effectiveness of the proposed detection methods and compare their performance for several typical *** work paves the way for systematic theoretical studies of channel entanglement and practical benchmarking of noisy intermediate scaled quantum devices.

关键词： quantum entanglement quantum channel entanglement detection entanglement witness

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Implementation of a Smart Garbage Monitoring System using GSM 1

Implementation of a Smart Garbage Monitoring System using GS...

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1st IEEE International Conference on Automation, computing and Renewable Systems, ICACRS 2022

作者： Rubini, B. Pradeep Kumar, S. Suganiya, M. Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9781665460842

Recent population crises have increased pollution in urban areas due to garbage overflow. Garbage collection has recently become a significant issue in urban areas. However, collecting and segregating garbage is a complicated process. Because of population growth, every individual, animal, and bird should impact the environment. People use an excess of polythene bags and medical and chemical waste outside dustbins;they are not properly disposing of their waste. Because of the garbage system harms the environment, resulting in foul odors and unsanitary conditions in people's surroundings. As a result, the proposed system explains how to solve this problem using an intelligent dustbin monitoring system. On the top of the dustbin, ultrasonic sensors are to be installed to measure the fill level. When the dustbin overflows, the GSM modem is activated and connected to an Arduino Uno board with an ultrasonic sensor via a microcontroller-based platform. In this intelligent dustbin, the threshold level is high. When this level reaches ultrasonic sensors, activate, until the garbage collection dustbin is empty GSM Modem is continuously alerted to the appropriate authorities. © 2022 IEEE

关键词： Population statistics

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Utilizing Gradient Boosting Models to Identify Risk Factors for Stroke 2

Utilizing Gradient Boosting Models to Identify Risk Factors ...

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

作者： Tamilselvi, P. Harini, C. Rao, Guduri Padma Narasimha Raju, P.V. Subbulakshmi, R. Channabasava, Ugranada Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai India Maisammaguda Secunderabad India SRKR Engineering College Andhra Pradesh Bhimavaram India Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Computer Science and Engineering Karpagam Institute of Technology Coimbatore India Department of Information Science and Engineering Don Bosco Institute of Technology VTU Karnataka Belagavi India

ISBN: (纸本)9798350383867

This research looks at the usage of the models of gradient boosting to find out the ones that are relevant in the stroke incidence. With the help of a large dataset ranging from demographics to clinical and imaging characteristics, the three most effective gradient boosting algorithms (XGBoost with LightGBM and CatBoost) were evaluated in terms of their ability for stroke risk prediction. Model performance metrics, including accuracy, area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, precision, and F1 score, were estimated through re-scheduling validation proses. Through feature importance analysis, I discovered which of predictors, such as age, hypertension, diabetes and others contribute to the stroke risk prediction and where age and hypertension occupy the highest positions. The result of external validation in separate cohorts was the confirmation of the predictive properties of the data assortment as well as the generalizability of the presented models in variety of populations. Also, the role of these models has been examined from the perspective of their clinical usefulness and ability to inform the implementation of targeted preventive interventions that will result in improved outcomes regarding heart health. We evaluated various lifestyle adjustments, medications and precision monitoring plans as being the most likely to have a positive effect on stroke rates and patient outcomes. Overall the present study proves the problem-solving capability of the gradient boosting model in a tailored risk assessment of stroke and stroke prevention, enabling us to implement the necessary interventions to minimize the impact of the diseases on the healthcare systems and individuals. © 2024 IEEE.

关键词： Risk assessment

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A Precise Model of Insect Flight Vitality and Development of Unmanned Micro Aerial Vehicle

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Journal of The Institution of Engineers (India): Series B 2021年第5期102卷 915-926页

作者： Anandan, R. Kalaivani, K. Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies PallavaramTamil Nadu India

In recent times, scores of innovations are taking place in the field of unmanned aerial vehicle (UAV) technology that includes continuous development of design of on-board processing, memory, storage, and communication capabilities. Since UAVs are now being used extensively in many areas of expertise that include commercial, military, civilian, agricultural, and environmental applications, it is very essential that these UAVs communicate efficiently. The communication may be of the UAV-to-UAV (U2U) type or it could be with the already existing prevalent networking infrastructures UAV-to-Infrastructure (U2I) type of communication. A new prototype is developed based on the insect flight dynamism. These insect wings are very thin membranous structure. They are subjected to many forces and follow aerodynamic flying and hovering. The current research trend on this subject is the simulation of insect wing structure and utilization to create a prototype for the development of unmanned micro air vehicles. In the present study, three flying insects such as dragon fly, cicada, and jewel beetle are selected and the wing aerodynamic parameters are analyzed by mathematical models. The comparative scanning of the data revealed that cicada wing structure is their aerodynamic characteristics may serve as a prototype. The results are discussed with relation to the base data for the flapping-wing system in micro air vehicle. In this paper, we have tried to put forward an approach that is capable of providing real-time positioning and tracking of a UAV. The approach has three parts: tracking device, backend server, and mobile app. © 2021, The Institution of Engineers (India).

关键词： Aerodynamics

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Privacy and Security using Double Signature Based Cryptocraphy using DS-SHA256 in Cloud Cimputing

Privacy and Security using Double Signature Based Cryptocrap...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), International Conference on

作者： K. Prathapkumar A. Thirumurthi Raja Department of Computer Science School of Computing Sciences Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai India

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

Cloud computing is a popular technology that offers clients various services remotely daily. Nowadays, cloud computing is becoming a fast-growing industry that contains the potential to provide extensible services via the internet using software and hardware virtualization. It has become difficult for cloud companies to manage the enormous volume of data and diverse resources in the cloud due to its rapid growth. Most clients range from individual users to large corporations or business houses. To prevent over-provisioning and under-provisioning problems, it is essential to ensure the availability of the resources to the end user with the least amount of administration. As a result, resources must be equitably distributed among various stakeholders without sacrificing the organization’s profitability or customer satisfaction. The enormous load from dynamic sources with various formats makes resource allocation and scheduling a complex *** also leads to several security issues, which include privacy, interoperability, credibility, following critical standards, and collaborations become a significant concern in cloud computing. We proposed cloud service provider dynamic scheduling (CSPDS) to address these issues. The proposed system is efficiently designed to handle various formats and huge loads in the cloud environment. The cloud service provider (CSP) plays a primary role in our proposed methodology (CSPDS), which controls and monitors the entire network. The CSPDS enhances the network’s QoS (quality of service) in resource monitoring, resource utilization, and resource allocation. Next, the migration model provides the best resource utilization, and the cryptography modules improve the users’ overall data security in the cloud. Finally, in the experimental section, a comparison work is conducted between the proposed CSPDS with existing resource allocation& task scheduling using a hybrid machine learning (RATS-HM) [13] technique and Resource Allocation Algor

关键词： Cloud computing Privacy Machine learning algorithms Computational modeling Quality of service Dynamic scheduling Resource management

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Exploring the Power of Deep Learning in Anomaly Detection: A Comprehensive Review and Analysis 12th

Exploring the Power of Deep Learning in Anomaly Detection: A...

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12th International Conference on Soft computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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