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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Srilakshmi, V. Chetana, V. Lakshmi Dornala, Raghunadha Reddi Vallabaneni, S. Parvathi Ramanjaiah, Ganji School of Computer Science and Engineering Vit -AP University Andhra Pradesh Amaravati522237 India School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amaravati Campus India Walgreens United States Pvp Siddhartha Institute of Technology Department of It Vijayawada India R v Institute of Technology Dept.of Cse Guntur India

ISBN: (纸本)9798350379945

Delivering computing resources over the internet has seen a paradigm shift towards cloud computing. However, there are serious security risks because cloud environments are distributed and shared. Intelligent Security Systems (ISS) that use cryptographic techniques are now necessary to overcome these issues, such as data confidentiality, integrity, and authenticity;this study developed an ISS for cloud computing, concentrating on cryptographic techniques. The RSA-OAEP (Optimal Asymmetric Encryption Padding) and X.509 algorithms are combined to create the ISS and further cover a variety of cryptographic methods, emphasizing their functions in protecting cloud-based systems, including encryption, digital signatures, and key management. It also looks at improving security systems' threat detection and response capabilities by integrating artificial intelligence (AI) algorithms like Grubbs' Test. In addition, several issues and potential paths for intelligent security systems in cloud computing are examined, highlighting the necessity of ongoing study and development to remain ahead of developing cyber threats. © 2024 IEEE.

关键词： Cloud security

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Genetic Testing of Huntington's Disease by Facial Examination of a Mankind using AI Programs 3

Genetic Testing of Huntington's Disease by Facial Examinatio...

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3rd International Conference on Innovations in computer science and Software engineering, ICONICS 2022

作者： Atruba Feroze, S. Faiza Nasim, S. NED University of Engineering and Technology Dept.of Computer and Information System Engineering Karachi Pakistan NED University of Engineering and Technology Dept. of Computer Science and I.T NEDUET Karachi Pakistan

ISBN: (纸本)9781665461412

This Genetic disorder is considered as one of the major challenges faced by Medical sciences as it is inherited by humans from one generation to another. With an increasing number of natives of the World at a higher rate, it causes a major impact on information society socially and economically during their lifetime. The citizens of Pakistan and Indian sub continents are facing genomic disease in probably high frequency as compared to other continents. Occurrence of congenital diseases in Pakistan results in higher percentages of unusual genetic diseases especially in children. After analyzing all the current scenarios, some goals and objectives were settled in order to facilitate and give general awareness to people. The objectives of systematic review are evaluation of diagnostic accuracy of AI for the identification of genome contributions to the pathogenesis of Huntington's disease through facial probe AI to accurately identify genetic disorders. The narrative review is performed in Ned university. In this proceeding sequence, 30 cases of both Normal and Genetically affected Patients were documented in which some certain sorts of tests were performed by using facial recognition. For this FR test, at first a combo list of information of all six emotions of a human being i.e. (Happy, Sorrow, Amazed, Fear, Anger, Revulsion) is gathered in the form of mutated images, which was then later be arranged into a pseudo-random order. Now with the help of Vision Technology we have applied some CV Techniques for the extraction of large pattern sets of features. Lastly the purpose of ML and DL techniques is to receive and compare those morphed image data from the Facial Database in order to identify each of six different emotions of HDNormal Person. This whole procedure is conducted in real time. Hence in this way we may recognize the HD genetic disorder among the people in an efficient way. The detailed procedure is explained in the Methodology section of this narrative revi

关键词： Genome

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A System to Track Fabricated Content in Social media using Machine Learning Approach

A System to Track Fabricated Content in Social media using M...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Kavyashree, M.K. Pemula, Rambabu Harshitha, M. Vidyavardhaka College of Engineering Affiliated to Visvesvaraya Technological University Dept. of Computer Science and Engineering Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Dept. of Computer Science and Engineering Krishnarajapet Mandya India Sjce Jssstu Mysore India Vidya Jyothi Institute of Technology Dept. of Artificial Intelligence Hyderabad India Vidya Vikas Institute of Engineering and Technology Dept.of Information Science and Engg. Mysore India

ISBN: (纸本)9798350388602

In today's society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. Data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning Training Strategies for Severely Imbalanced Data in Organ Segmentation Tasks 6

Deep Learning Training Strategies for Severely Imbalanced Da...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Wang, Hsin-Hui Liu, Chin-Yun Hung, Shih-Kai Chen, Liang-Cheng Hsieh, Hui-Ling Liu, Wei-Min National Chung Cheng University Dept.of Computer Science and Information Engineering Chiayi Taiwan Tzu Chi University School of Medicine Hualien Taiwan Dalin Tzu Chi Hospital Buddhist Tzu Chi Medical Foundation R.O.D.T.C.H Dept. of Radiation Oncology Chiayi Taiwan National Cheng Kung University Dept.of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350301953

Radiotherapy is one of the common methods for cancer treatment. Developing a radiotherapy plan requires professional medical physicists or physicians to manually contour the organ boundaries in CT series, which is time-and labor-consuming. If artificial intelligence (AI) could assist with the task, it could alleviate the workload of medical staff, especially when medical resources are tight. We propose an AI-based automatic organ segmentation system trained by clinical datasets. However, this task is prone to be non-robust models in CT image series where the background occupies the majority of the scene. To remedy such data imbalance situation, we propose adopting three strategies during the model training steps: region classification, knowledge discovery in database, and sampler. The major segmentation task is based on U-Net and ResNet34 model where all convolution layers and batch normalization are replaced with group normalization and weight standardization to ensure effectiveness in small-batch data training. In this study, 33 organs throughout the body were segmented. The ablation experiments were conducted to prove all the training models have better performance than the original method. In the future, if a hospital needs to train model with their own private datasets, the three above strategies can be adopted to prevent unsuccessful training. © 2023 IEEE.

关键词： Radiotherapy

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Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

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Journal of Mobile Multimedia 2023年第4期19卷 935-962页

作者： Bhatnagar, Sonika Dayal, Mohit Singh, Deepti Upreti, Shitiz Upreti, Kamal Kumar, Jitender Dept. of Computer Science & Engineering Government Polytechnic Baheri Bareilly India Dept. of Applied Science Bharati Vidyapeeth’s College of Engineering New Delhi India Dept. of Information Technology ABES Institute of Technology Ghaziabad India Dept.of Information Technology Asian Education Group (AEG) Noida India Dept. of Computer Science & Engineering Dr. Akhilesh Das Gupta Institute of Technology and Management New Delhi India Dept. of Computer Science & Engineering J.C Bose University of Science & Technology YMCA Haryana Faridabad India

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n²), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n = 256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison. © 2023 River Publishers.

关键词： blockchain security Digital signature hyperledger fabric smart contract

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Application of Advanced Data Mining and computer Vision Techniques in License Plate Recognition

Application of Advanced Data Mining and Computer Vision Tech...

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Smart Technologies, Communication and Robotics (STCR)

作者： Ayorinde Alase Danish Multani Sai Rithwik Amajala Udhaya S K Kamal Upreti Electrical and Computer Engineering University of Arkansas at Little Rock Little Rock Arkansas US UST Global Solutions Gurgaon Haryana India Computer Science and Engineering India Computer Science and Engineering Easwari Engineering College Chennai India Dept. of Computer Science Christ University Delhi NCR India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

As urbanization is expanding rapidly and vehicular traffic is on the rise, efficient and automated vehicle identification is a must. Smart transportation, safety monitoring, & police work all heavily rely on Automatic License Plate Detection (ALPD). Traditional heuristic-based image processing techniques are incapable of handling environmental variations; Artificial intelligence (AI) & machine vision solutions are therefore used. YOLO, Faster R-CNN, and SSD are some of the most effective CNN-based and object identification algorithms that demonstrate cutting-edge accuracy in license plate recognition. The paper studies the usage of deep learning algorithms with prepossessing and advanced localization methods for ALPDs' optical character recognition (OCR) and character segmentation. The research also studies integrating ALPD with edge computing and IoTs to develop real-time smart traffic solutions. It also examines machine learning techniques, deep learning innovations, and conventional methods, emphasizing how well various models perform in comparison in terms of accuracy, computational efficiency, and real-time processing power. By employing cutting-edge designs, this study seeks to increase the scalability and resilience of license plate recognition systems, which will support future urban development, security applications, as intelligent transportation management.

关键词： YOLO Deep learning Computational modeling Lighting Real-time systems Computational efficiency Smart transportation Internet of Things License plate recognition Edge computing

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A Multi-Model Rouge Nodes Detection System for Fog Computing

A Multi-Model Rouge Nodes Detection System for Fog Computing

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computer science, computer engineering, & Applied Computing (CSCE), Congress in

作者： Thanh Duc Bui Brajendra Panda Dept. of Computer Science and Computer Engineering University of Arkansas Fayetteville USA

With the increasing speed of advance of both Internet of Things (IoT) and the demands of utilizing IoT devices in different locations with high compatibility, fog computing has been foreseen as a sustainable solution. However, new challenges related to fog computing's cyber security also emerge. Among various cyber-attacks on the fog computing system, one standard method is to implement corrupted internal nodes that allow malicious/compromised access from attackers, threatening the confidentiality of users' information and the system's performance. In this paper, we propose a method to quantify the distribution of each attribute value from historical access logs from each node within a fog computing system. We also utilize an unsupervised machine learning method to separate nodes into smaller clusters with respect to different models to improve the forecasting ability of the system. Finally, our models are evaluated over simulated data using real access logs. Our experimental results illustrate that our proposed method achieves some advanced performance over applying an overall classification, even using the same classification machine learning methods and dataset.

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A Multistep Ahead Predictive Analysis of Crude Oil 2

A Multistep Ahead Predictive Analysis of Crude Oil

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2nd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2023

作者： Mohapatra, Srikanta Kumar Singh, Kamakhya Narayan Sarangi, Prakash Kumar Sahu, Premananda Mohanty, Jayashree Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School Of Computer Application KIIT Deemed University Odisha India Hyderabad India School of Computer science and Engineering Lovely professional University Punjab India Chandigarh University Dept.of AIT-CSE Punjab Chandigarh Mohali India

ISBN: (纸本)9798350333763

The population is increasing in a rapid manner and the number of vehicles is increasing in the same ratio. Also, crude oils are very useful towards the energy needs in the industries. As a result, a day will come when we face the issue of feeling the vehicles with the petrol to run the vehicle and to run the industries. So, the solution is to find out the period up to which we can use the crude oil which is currently present in the world. To get a conclusion the most populated countries India and China is taken as reference. For simulation purpose time series based Artificial Neural Network application along with Facebook Prophet is used. Facebook Prophet is used to predict future situations in time series data related to oil production and consumption in two top countries by population, China, and India, over a period of 32 years. NeuralProphet is used for modelling time-series based on neural networks forecasting the near-term oil crisis. The dataset used in this study contains three characteristics, namely population, oil production, and consumption, and is obtained from different government sources. The best part of this model is that it can predict multi step ahead of given current data as required. The method based on NeuralProphet is best suit for the time series prediction for short period or long period. It can be used in multistep ahead prediction methods. It is predicted that by 2050 the scarcity of the petroleum is most. So, the solution is either to reduce the uses of vehicles, control the population or simply go for an alternate fuel like hydrogen could be considered a suitable substitute for oil. This prediction highlights the need for sustainable energy sources to meet the energy demand in the future. In conclusion, this paper presents NeuralProphet approach for forecasting future oil consumption and production trends combining the Facebook Prophet and NeuralProphet method. © 2023 IEEE.

关键词： Time series

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Recurrent 3-D Multi-Level Visual Transformer For Joint Classification of Heterogeneous 2-d AND 3-D Radiographic Data

Recurrent 3-D Multi-Level Visual Transformer For Joint Class...

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IEEE International Conference on Image Processing

作者： Muhammad Owais Muhammad Zubair Taimur Hassan Divya Velayudhan Irfan Hussain Naoufel Werghi Dept. of Mechanical and Nuclear Engineering KUCARSDept Khalifa University UAE Faculty of IT & Computer Science University of Central Punjab Pakistan Dept.of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE Dept.of Computer Science C2PS Khalifa University UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of $96.67 \%$, F1-score of $96.88 \%$, mean average precision of $96.75 \%$, and mean average recall of $97.02 \%$. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.

关键词： Visualization Solid modeling Accuracy Image processing Image retrieval Lung Manuals

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Towards Comprehensive Breast Cancer Detection: Fusion of Dense Layer Model Features

Towards Comprehensive Breast Cancer Detection: Fusion of Den...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Gadawalsa Kishor Kumar Debendra Muduli Kshirabdi Tanaya Rath Durbar Sundar Ray Santosh Kumar Sharma Computer Science dept.of Engineering C.V Raman Global University Bhubaneswar Odisha

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

For an early diagnosis and successful treatment, ultrasonography image analysis is essential for identifying breast cancer. In this discipline, deep learning has become a powerful tool that has demonstrated notable success in several medical imaging tasks,regarding the identification of cancer in the breast. In order to achieve accurate identification, our suggested model combines characteristics from three pre-trained Convolutional Neural Network (CNN) models: DenseNet201, DenseNet169, and DenseNet121. In this study, we use ImageNet weights and ultrasound image datasets to evaluate these models’ performance. After extensive testing, our model reached 94.57% accuracy, which is astounding. The model’s performance was evaluated through the use of confusion matrices and metrics such as precision & F1-score, which we determined using the Adam optimizer. The efficacy of deep learning-based techniques in identifying breast cancer from ultrasound pictures is highlighted by this study. It illustrates makes major contributions to medical analysis and helps discover breast cancers early.

关键词： Accuracy Computational modeling Transfer learning computer architecture Ultrasonography Medical services Breast cancer Convolutional neural networks Reliability Testing

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