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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Peneti, Subhashini Rama Krishna, S. Kiran, Ajmeera Tripathy, Hrudaya Kumar Mlr Institute of Technology Department of Computer Science & Information Technology Hyderabad India Gitam University Department of Computer Science & Engineering Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Kalinga Institute of Industrial Technology Deemed to Be University India

ISBN: (纸本)9798350370188

India's banking sector has grown rapidly in modern times. With increased bank functioning, frauds skyrocket. Debit cards, Internet Banking, and ATM cards are among the financial services offered today. These services attracted many new customers worldwide. We present a Machine Learning technique to detect credit card fraud. The global use of credit cards is expanding significantly. Thus, the bank server can track these transactions using Machine Learning. These algorithms detect or anticipate transaction fraud. Each transaction has a few functions in the dataset. We should classify and identify fraudulent transactions from those transactions. The work suggests using Machine Learning calculation for this. © 2024 IEEE.

关键词： Crime

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Shifting From Syntactic to Semantics for Knowledge Exemplification Using Semantic Web (SW) Mining Techniques

Shifting From Syntactic to Semantics for Knowledge Exemplifi...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Zaidi, Taskeen Kumar, Ajay Pundeer, Shivali Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Mechanical Engineering Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

Using Semantic Web technologies is critical to improving knowledge sharing and administrative efficiency and fostering semantic interoperability amongst healthcare information systems. This article offers a thorough evaluation of software technologies that takes into account both the technical and content elements. In-depth discussions of ontology, interoperability guidelines, ontology creation, types of ontologies, ontology editors, and real-world applications of ontologies in healthcare are provided in this study. The proprietor of the website wants to help visitors by giving them useful information that will help them achieve their goals. By examining user behaviour, page content, and the sequence in which URLs are viewed, web mining techniques are utilised to classify users and sites. In order to extract conceptual linkages and apply online structure mining procedures, which concentrate on fully structured forms, this study provides two web mining techniques: web use mining and web content mining. Web structure mining aids in the retrieval process and has been utilised in the development of ontologies. © 2024 IEEE.

关键词： Semantic Web

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Cyber Attack Detection Using Deep Multi-agent Reinforcement Learning with Beth Dataset

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SN computer science 2025年第5期6卷 1-16页

作者： Manikandan, A. Rajan, S. Deepa Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies VISTAS Tamil Nadu Chennai India

The Internet of Things (IoT) transforms connectivity, enabling devices to share data seamlessly via the cloud or wireless networks. However, IoT systems remain vulnerable to sophisticated cyberattacks, and traditional detection methods often fail due to inadequate fine-tuning and ineffective feature extraction. This study addresses these challenges by proposing a novel cyberattack detection framework based on deep multi-agent reinforcement learning (MARL). The framework employs the deep deterministic policy gradient (DDPG) algorithm to enhance the adaptive capabilities of multiple agents within an IoT network environment. A newly curated dataset, BETH, is introduced, encompassing diverse cyberattack scenarios specifically designed for training and evaluating MARL-based systems. Through deep reinforcement learning, the framework autonomously learns to detect and mitigate known and emerging threats. To assess the robustness and generalizability of the proposed model, a fivefold cross-validation process was conducted. Experimental results reveal that the proposed approach achieves an exceptional detection accuracy of 99.95%, demonstrating its ability to adapt to dynamic attack patterns while minimizing error rates. The findings highlight the framework’s potential to improve network security significantly, advancing the field of intelligent, autonomous cyber defence for IoT systems. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anomaly detection agents (ADA) BETH dataset Collaboration and communication buffer (CCB) Cyber-attack detection Deep deterministic policy gradient algorithm Internet of Things (IoT) Intrusion detection agents (IDA) Multi-agent reinforcement learning (MARL)

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Towards Safer Digital Spaces: Automated Detection of Cyberbullying Through Multi-Modal Learning 5

Towards Safer Digital Spaces: Automated Detection of Cyberbu...

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

作者： Karthikeyan, M. Saravana Francis Raj, J. Relin Krishnan, R. Santhana Kumari, D. Abitha Murali, S. Joshua, K. Paul Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India RMK Engineering College Department of Computer Science and Engineering Thiruvallur India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India

ISBN: (纸本)9798331530013

Cyberbullying and online harassment present significant challenges to digital safety, demanding robust detection systems capable of identifying abusive content across multiple formats. This work proposes a multi-modal framework that leverages CLIP and RoBERTa embeddings to detect and categorize cyberbullying and harassment in both text and image-based posts. By utilizing two curated datasets, HarassDetect-TextImage and CyberGuard-MultiModal, the model is trained on a diverse range of offensive and non-offensive content. Text data is preprocessed through advanced techniques, while CLIP and RoBERTa generate embeddings capturing the context of both textual and visual inputs. For sequential analysis of threaded posts, a Transformer layer further enhances the model's contextual awareness, enabling it to recognize harassment patterns that unfold over multiple messages. A Support Vector Machine (SVM) classifier then categorizes content into Non-Cyberbullying, Minor, Moderate, and Severe classes, each associated with confidence levels guiding automated actions. High-confidence classifications trigger immediate responses, such as content restriction or user warnings, while moderate-confidence cases are flagged for manual review. This adaptive framework not only improves accuracy but also allows for a continuous feedback loop, updating the model to address evolving patterns of cyberbullying. Evaluation results demonstrate the efficacy of this approach in creating safer online environments through automated and contextually aware cyberbullying detection. © 2024 IEEE.

关键词： Support vector machines

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Toward actionable testing of deep learning models

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science China(Information sciences) 2023年第7期66卷 300-302页

作者： Yingfei XIONG Yongqiang TIAN Yepang LIU Shing-Chi CHEUNG Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education School of Computer Science Peking University Department of Computer Science and Engineering The Hong Kong University of Science and Technology School of Computer Science University of Waterloo Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Department of Computer Science and Engineering Southern University of Science and Technology

Deep learning has become an important computational paradigm in our daily lives with a wide range of applications,from authentication using facial recognition to autonomous driving in smart vehicles. The quality of the deep learning models, i.e., neural architectures with parameters trained over a dataset, is crucial to our daily living and economy.

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SanS: Classification of Sanskrit Mantras using Speech Processing 2024

SanS: Classification of Sanskrit Mantras using Speech Proces...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Keerthan Kumar, T.G. Udaya, Shreyas Koolagudi, Shashidhar G. Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Surathkal India

ISBN: (纸本)9798400709722

Chandas is a classification metric used in Sanskrit Mantras. They are essentially meant to maintain a form of rhythm for each and every mantra, which gives the hymns their distinctive chanting pattern. Sanskrit hymns (mantras) have many different classifications. Often, before chanting, they are invoked as such in the order of Rishi (author of the hymn), Chanda (rhythm), Devata (god to which invoked), and Viniyoga (use of such hymn). One such example is the Gayatri hymn, which is from Gayatri Chanda. Other examples are verses of the Bhagavad Gita, which are entirely in Anushtup Chanda. Chandas is an essential component of Sanskrit Mantras and forms an integral part of it. Knowledge of Chandas is essential for the study of Sanskrit Poetry, and without knowing what a Chanda is, one cannot analyze Sanskrit hymns. Essentially, Chandas are the meters used to keep track of the mantras. Chandas formulate the rhythm of the mantra and the way that it should be chanted. Based on the number of syllables, there are seven different Chandas, and mantras are usually classified into one of these seven. Identification of Chandas is usually specified in the beginning before chanting;however, in cases where the Chandas are not specified, one may have to do it manually, which may be cumbersome, especially for Chandas with many syllables. In this work, we propose a novel approach called Classification of Sanskrit Mantras using Speech Processing technology (SanS) to determine the Chanda of a mantra, given the audio file of a Sanskrit mantra and using a Wavenet architecture. The proposed SanS predicts the type of Chanda by counting the number of syllables, giving 81.57% accuracy compared to other works. © 2024 Copyright held by the owner/author(s).

关键词： Word processing

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A Novel Hybrid Recommendation System for Riyadh Restaurants: Integrating Content-Based and Context-Aware Techniques 4

A Novel Hybrid Recommendation System for Riyadh Restaurants:...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rohilla, Vinita Kaur, Mandeep Chakraborty, Sudeshna Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Maharaja Surajmal Institute of Technology Department of Computer Science and Engineering New Delhi India Galgotia University Department of Computer Science and Technology Greater Noida India

ISBN: (纸本)9798350364729

Recommendation systems are often used to deliver consumers a customised set of services. Their main purpose is to provide recommendations or concepts (such as restaurants, tourist attractions, medicines, movies, etc.) that effectively address consumer issues and may be used across several sectors. In today's environment, when there are so many eating alternatives, selecting the ideal restaurant to meet our interests may be difficult. Saudi Arabia's tourist industry has lately emerged as an important economic engine. It is becoming more difficult for customers to choose among the vast array of restaurants available in Riyadh due to the industry's fast expansion in recent years. This research presents a comprehensive recommendation system for Riyadh restaurants that integrates content-based filtering and context-aware location analysis to provide personalised suggestions. Utilising a dataset from Kaggle with information on 20,000 restaurants, the system employs a hybrid approach combining TF-IDF and cosine similarity for content-based filtering with geographic distance calculations for location-based recommendations. The hybrid model balances content relevance with geographic proximity, enhancing user satisfaction by delivering accurate and contextually relevant restaurant recommendations. The outcomes demonstrate an effectiveness of a hybrid method, outperforming individual techniques by generating more relevant and practical recommendations. The uniqueness of this study resides in the use of hybrid filtering, which has not before been used in comparable investigations. A more detailed and easily digestible list of suggested models is what this study report is aiming to provide. According to the findings and conclusion, the proposed strategy is effective in recommending restaurants in Riyadh. © 2024 IEEE.

关键词： Wiener filtering

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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

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SN computer science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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A Novel Framework for Detection of Harmful Snakes Using YOLO Algorithm

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SN computer science 2024年第1期5卷 1-10页

作者： Naresh, E. Babu, J. Ananda Darshan, S. L. Shiva Murthy, S.V.N. Srinidhi, N.N. Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Information Science and Engineering Malnad College of Engineering Karnataka Malnad India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal India Department of CSE SJC Institute of Technology Chikkaballapur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. The idea is to construct a machine learning model to identify the snakes, which will be trained on a vast set of images of snakes. These images will be carefully selected to include the snake patterns, skin color of different species of snakes (extraction of the unique features). This is done to train the model properly to identify different types of snakes that the farmer encounters in the fields. Most famers are unaware and have a need for a way to detect these snakes so that they can either avoid them or get rid of them. Hence, this system can be implemented to prevent thousands of deaths and can help famers alleviate the repercussions of snake bites. Mistakes in recognizing potentially harmful animal species based only on visual cues are major contributors to the high death toll from venomous animal attacks. Since they spend so much time in the fields, where rice and wheat are cultivated, farmers are at a higher risk of being bitten by a snake than the general population. Because of their ignorance, illiterate farmers are more inclined to believe in superstitions, which can lead to their untimely deaths from snakebites despite medical intervention. Animals that would normally pose no threat to humans are responsible for the deaths of thousands of people every year because of environmental factors. However, because it is hard for humans to recognize these dangers, a new design paradigm has been developed to make it simpler. Researchers in the field of animal biology can use it to search for endangered species. Predators may enter gardens and green areas like tea and coffee plantations. There is not yet a plan in place to implement automated sorting for discovering distinctions. By applying the suggested framework to photographs of potentially dangerous animals, several factors useful for studying anim

关键词： Classifier training Convolutional neural network Farmer deaths Feature extraction Snake detection YOLO algorithm

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3D Segmentation of MRI Using Multimodal for Detection of Brain Tumour

3D Segmentation of MRI Using Multimodal for Detection of Bra...

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2024 International Conference on Recent Innovation in Smart and Sustainable technology, ICRISST 2024

作者： Sanjay, D. Devarsh, V. Murali, E. Antony, J Cruz Vignesh, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350309140

Determining brain tumors from MRI can be challenging due to several factors, and it typically requires expertise in neuroimaging and collaboration between radiologists, neurologists, and other specialists. Some tumors may have a subtle or a typical appearance on MRI, making them difficult to distinguish from normal brain tissue. Brain tumors can be highly heterogeneous in terms of silhouette, scope, and internal structure. Tumors can have different types of tissues, such as necrotic and enhancing regions, making it challenging to accurately delineate their boundaries. Brain tumors can be highly heterogeneous in terms of silhouette, scope, and internal structure. Tumors can have different types of tissues, such as necrotic, cystic, or enhancing regions, making it challenging to accurately delineate their boundaries. Deep learning has shown promising results in various medical imaging tasks, including the identification and classification of brain tumors, such as gliomas, from MRI images. In this paper, an attempt to design an ensemble model for the prediction of 3D brain tumor segmentation and performance is going to be evaluated using deep learning models such as UNET, FPN, and UNET+FPN. The above models are evaluated on the BraTS 2021 (Brain Tumour Segmentation) dataset, and comparing the metrics of each model reveals that UNET and, an enhanced model that combines UNET and FPN, UNET+FPN has the highest accuracy of 0.9959 and 0.9954 respectively. © 2024 IEEE.

关键词： Tumors

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