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4th International Conference on Soft Computing for Security applications, ICSCSA 2024

作者： Elango, S. Mohanaprakash, T.A. Siva, D. Gowtham, A. Sasikumar, A.N. Sunitha, T. Department of Computer Science and Engineering SOET CMR University Bengaluru India Department of Computer Science and Applications SRM Institute of Science and Technology Chennai India Madanapalle Institute of Technology & Science AP India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India

ISBN: (纸本)9798331515720

The evolution of human civilization has been intrinsically linked to advancements in technology, leading to the development of multiple languages as mediums of communication. However, this linguistic diversity poses significant challenges in cross-cultural communication. Despite numerous efforts and achieving near state-of-The-Art results in language translation, a universal solution remains elusive. This research study proposes an advanced approach to machine translation by utilizing server-side machine learning to enable seamless communication across various languages. By sending GET or POST requests through JQuery, users can efficiently translate text. The process is structured into three key steps: speech recognition (conversion of spoken words to text), translation using machine learning algorithms, and speech synthesis (conversion of translated text to audio). This approach aims to enhance real-Time translation capabilities, offering a more effective and user-friendly solution for overcoming language barriers. © 2024 IEEE.

关键词： Adversarial machine learning

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Assessing the Impact of Migration from SOA to Microservices Architecture

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SN computer science 2023年第5期4卷 577页

作者： Raj, Vinay Bhukya, Hanumanthu Department of Computer Applications National Institute of Technology Tiruchirappalli Tamil Nadu Tiruchirappalli 620015 India Department of Computer Science and Engineering (Networks) Kakatiya Institute of Technology and Science Telangana Warangal 506015 India

Microservices has become a buzzword in IT and large enterprise firms such as Netflix, Twitter, Spotify and others have started to design their applications by adopting this new architectural style. A few organizations have started migrating their traditional monolithic and SOA-based applications to microservices in order to benefit from the features of new style. Software architects, on the other hand, are in chaos whether to adopt this new style or not as they are unaware of the pros and cons of microservices architecture. Also, the impact of the migration on SOA-based applications in terms of performance and complexity is unknown, leading to dilemma on the migration process. In this paper, a study of the migration impact on the existing SOA based applications to microservices is presented. For this study, change propagation probability and architectural stability metrics are used to examine the effect of migrating a SOA-based application to microservices architecture. The proposed approach is illustrated on a case study application designed using SOA and then migrated to microservices. It is observed from the results that though the impact of migration is high, migrating to microservices has significant benefits and it is best suitable for large enterprise applications. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Change propagation Microservices Migration Service oriented architecture Stability

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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

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computers, Materials & Continua 2023年第1期74卷 881-895页

作者： Nitin Sharma Mohd Anul Haq Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Computer Science College of Computer and Information SciencesMajmaah University11952Al-MajmaahSaudi Arabia Department of Electronics&Communication Engineering DCRUST Murthal131039India Department of Transportation Engineering IITKanpur208016Uttar PradeshIndia Department of Computer Science Applications PIETSmalkha132102India Skill Faculty of Science and Technology Shri Vishwakarma Skill UniversityPalwal121102India Computer Science and Information Systems Department College of Applied SciencesAlMaarefa UniversityRiyadhSaudi Arabia

Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile *** is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight *** the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency *** Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic *** study is based on a comprehensive and detailed literature evaluation in the field of *** major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car *** research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for *** properties of characters are employed to recognise the Indian licence plate in this *** licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were *** comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency.

关键词： Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine

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computer Vision and Deep Learning-Based Model for Detecting Spoofed Faces in Images 2nd

Computer Vision and Deep Learning-Based Model for Detectin...

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2nd International Conference on Data science and Network Engineering, ICDSNE 2024

作者： Salian, Gayathri P Rao, Manasa K Rashmi, M. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819783359

In the modern era of smart applications, video data is critically important in various contexts. In most of these applications, cameras are frequently incorporated to facilitate authentication. As a result, face recognition is the biometric method most frequently employed to authenticate users in these applications. The vulnerability of face recognition systems to spoofing attacks grows in tandem with their increased usage. As a result, robust countermeasures are required. This paper presents an approach to face anti-spoofing through transfer learning and YOLOv8 optimization. Additionally, a custom dataset was constructed using images obtained from web cameras and an existing dataset to assess the proposed work’s real-time effectiveness. The proposed approach also adds a blurriness threshold during image capture to improve performance. With a mean Average Precision (mAP50) of 0.975, the experimental outcomes highlight the model’s effectiveness in detecting face spoofing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Analyzing the Correlation of Smart Contract Vulnerability and Readability: A Machine Learning Approach 5th

Analyzing the Correlation of Smart Contract Vulnerability ...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Sameera, K.M. Vinod, P. Rafidha Rehiman, K.A. Kumari, Poonam Raj, Harsh Kumari, Rinki Kumar, Aman Mittal, Ritika Department of Computer Applications Cochin University of Science and Technology Kerala Kochi682022 India Department of Mathematics University of Padua Padua35121 Italy

ISBN: (纸本)9789819745395

Blockchain’s rapid growth is revolutionizing sectors like finance and healthcare, providing universal computational access for diverse, decentralized applications (DApps). It also facilitates the creation of DApps, which integrates smart contracts on the blockchain with a user-friendly front-end interface. This research aims to assist researchers and developers in analyzing the smart contract’s code readability level and vulnerability types. In this work, we collect and analyze smart contracts from the "Etherscan" platform to demonstrate the tool’s effectiveness. To the author’s knowledge, this is the first work examining the association between the readability and vulnerability of solidity smart contracts. To examine the degree of readability of smart contracts, we employ a set of readability matrices that the previous research demonstrated related to code readability. Our suggested method evaluates the smart contract automatically and calculates a set of metrics for the level of code readability. Furthermore, We propose a machine learning model to create an automated system that discerns between readable and non-readable smart contracts. The result shows that XGBoost outperformed other classifiers with an impressive 98.856% F-Measure and 98.858% accuracy. Additionally, we utilize the Slither to analyze security issues in smart contracts. Afterward, we examined the most prevalent and critical vulnerability types discovered in each smart contract. Also, it is observed that there is a correlation between non-readability and vulnerability. Our analysis revealed that 72.6% of non-readable files exhibit vulnerabilities, showing that files with high vulnerability are characterized by low readability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Code readability Smart contracts Vulnerability

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A novel hybrid deep learning framework for pedestrian detection based on thermal infrared and visible spectrum images

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Multimedia Tools and applications 2025年 1-27页

作者： Defaoui, Mahassine Koutti, Lahcen El Ansari, Mohamed Lahmyed, Redouan Masmoudi, Lhoussaine LabSIV Department of Computer Science Faculty of Science Ibn Zohr University Agadir80000 Morocco Informatics and Applications Laboratory Faculty of Science My Ismail University Meknes Morocco Conception and Systems Laboratory Faculty of Sciences Mohamed V University Rabat Morocco

For many intelligent systems, designing accurate pedestrian detection approaches is a fundamental task. This paper describes a novel hybrid system to detect pedestrians using both visible and thermal infrared sensors. The proposed method is achieved in two primary stages. The first stage, hypotheses generation (HG), aims to extract the regions that represent suspected pedestrians from the image. It is performed using both thermal and visible images of the same scene. Here, an improved deep learning-based solution with a convolutional neural network named PedTherm-U-net is proposed. It segments the thermal images and maps the segmentation results into the corresponding visible image to extract the ROIs (candidate pedestrians). The second stage, hypotheses verification (HV), classifies the ROIs extracted from the first stage into pedestrians and non-pedestrians. In the HV step, a novel PedVis-ResNet framework to classify the extracted ROIs is introduced. It is a modified version of ResNet model dedicated to classify the visible pedestrian samples. The proposed method has been tested on the publicly accessible OSU Color-Thermal dataset, and the obtained results demonstrate the effectiveness of the proposed method when compared to the recent state of the art pedestrian detection approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Intelligent systems

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Heart Disease Prediction Using Tree Structured Parzen Estimator Optimization 5

Heart Disease Prediction Using Tree Structured Parzen Estima...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Hemalatha, S. Kavitha, T. Sivabalaselvamani, D. Karthika, R.T. Hariprasath, N. Desikan, R. Kongu Engineering College Department of Computer Applications Erode India School of Information Science Presidency University Bengaluru India

ISBN: (纸本)9798331530013

Heart diseases involve diseases of the heart and blood vessels like coronary artery disease, arrhythmias and congenital heart diseases. Electrocardiogram or ECG is a test that assesses the activity, rhythm and electrical conduction of the heart and is paramount in diagnosing heart disorders. It should, however, be noted that even though the ECG is important in heart conditions diagnosis, it cannot by itself be a heart disease predictor, but still crucial in determining the health of the heart. TPE is a type of Bayesian optimization algorithm that is predominantly applied in hyperparameter tuning of predictive models. TPE can efficiently search the hyperparameter space as it creates a probabilistic model of the objective function and at every iteration pushes the most optimal hyperparameters and therefore increases the entire model's performance. This approach acts to augment the effectiveness of the models, particularly in complex prediction tasks. Convolutional Neural Networks (CNNs) are multi-layer perceptron neural networks used for images recognition, hence composed of filter layers, pooling layers, and fully connected layers. Their advantages include the ability to capture spatial features making them powerful in image classification and object detection tasks. The combination of TPE optimization with CNNs results in improved performance because hyperparameter tuning is automated and an optimal setting which maximizes the accuracy all that is required, with values as high as 87.3% accuracy in heart disease prediction. © 2024 IEEE.

关键词： Multilayer neural networks

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Comprehensive Literature Review on Fake News Detection: Techniques, Challenges and Emerging Trends 1

Comprehensive Literature Review on Fake News Detection: Tech...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Pal, Suraj Kumar, Devender Baba Mast Nath University India Baba Mast Nath University Department of Computer Science and Applications Rohtak India

ISBN: (纸本)9798350356816

The existence of fake news endangers the public through its creation of manipulated information which confuses our political and financial structures along with general societal debates. Online technology expansion has made authentic information delineation from false information progressively difficult to detect. Combatting misinformation requires essential detection of fake news so advanced technologies such as machine learning natural language processing and deep learning show promising results in resolving these concerns between authentic and false information. Detecting fake news is essential to mitigating the harmful consequences of misinformation, and advanced technologies such as machine learning, natural language processing, and deep learning have proven to be effective tools in addressing this issue. This research explores the diverse automatic detection strategies for fake news while examining their implementation obstacles and reviewing recent technological advances intended to enhance detection system capability. This review creates a pathway to understand fake news detection research progress while detailing prospective methods for upcoming advancements. © 2024 IEEE.

关键词： Adversarial machine learning

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Recognition and Classification of Apple and Sugarcane Plant Leaf Diseases using SVM with DAE Models 2

Recognition and Classification of Apple and Sugarcane Plant ...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sunil Kumar, V. Jyothi, A.P. Sumanth, S. Nagamani, H.S. Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ramaiah University of Applied Sciences Faculty of Engineering and Technology Department of Cse Peenya Bengaluru India Department of Computer Science and Computer Applications Kolar India Department of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350382952

For this leaf disease study, apple and sugarcane plants are identified and classified using a mix of character features and in-depth automated coding. Farm sugar cane leaves were copied for the online apple Plant Village Dataset. Pre-process images to reduce size and improve HLG filters. Image segmentation using DWT and GLCM feature extraction follows. Like the Low-Low (LL) band, DWT images produce 4-orthogonal subbands. Deep Auto Encoder and SVM algorithms retrieved subgroup functions and identified diseases from training data. If classification findings are ignored, the division stage receives data with HL, LH, and HH bands to explain approach consistency. Deep Auto encoder classifies illness using training data after extracting sub-band features. The Auto Encoder and SVM classifier demonstrated that the proposed model outperformed alternatives in LL band. Other subbands performed worse than the LL band. This study found that LL sub band data provided fine image data for better sickness categorization than HH, HL, and LH sub band data. © 2024 IEEE.

关键词： Discrete wavelet transforms

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