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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thinakaran, K. Anand, M. Kumar, T.Rajesh Atham, Saira Banu Subramanian, K.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai Kattankulathur India Saveetha Institute of Medical and Technical Science Saveetha School of Engineering India Presidency University School of Cse & Is Department of Computer Science and Engineering Bangaluru India Shadan College of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350348910

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error. © 2023 IEEE.

关键词： Airfoils

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning 2

Machine Learning for Climate Change Impact Assessment and Ad...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Balakrishnan, T. Suresh Krishnan, Prabhakar Ebenezar, U. Samson Mohammed Nizarudeen, M. Kamal, N. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Amrita Vishwa Vidyapeetham Center for Cyber Security Systems and Networks Amritapuri-Campus India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India

ISBN: (纸本)9798350384277

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level. © 2024 IEEE.

关键词： Adversarial machine learning

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Forensic Investigation of Malicious Activities in Digital Environments 4

Forensic Investigation of Malicious Activities in Digital En...

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4th IEEE International Conference on data Engineering and Communication Systems, ICDECS 2024

作者： Pasumpon Pandian, A. Anakath, A.S. Kannadasan, R. Ravikumar, K. Abdul Kareem, D. Care College of Engineering Department of Computer Science and Engineering Trichy India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computer Science and Engineering Department of Software Systems Tamil Nadu Vellore India Rrase College of Engineering Padappai Department of Computer Science & Engineering Chennai India Grt Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruttani India

ISBN: (纸本)9798350393354

Several digital dangers were investigated. Malware dominated analysis with 45 attacks. We found 30 phishing attacks. 22 data breaches, 15 cyber espionage, 18 identity theft. This indicates the kind and frequency of harassment digital forensic specialists endure. Digital evidence was crucial to the case. 65 pieces of evidence were collected on computers. 42 incidents showed smartphones and tablets' digital investigative potential. There were 55 examples of communication patterns requiring network logs. In 28 and 12, cloud services and removable media provided proof. A wide range of evidence sources stresses the need for extensive collecting. Digital forensics were essential to the inquiry. In 70% of cases, disk imaging software created forensic copies of metadata-and timestamp-containing storage media. Network packet analyzers examined 48 traffic logs and communication patterns. To analyze volatile memory, 35 cases used memory forensics. In 25 cases, malware analysis platforms decrypted malware. In 60 situations, file recovery software recovered lost or hidden files. Digital forensic specialists' diverse arsenal for thorough investigations is shown here. Legal and ethical considerations dominated the study. The law was important, and all inquiries followed norms. A strong chain of custody protects evidence integrity and admissibility. The data privacy rules were respected. Legal evidence is admissible in court. Ethics ensured objective investigations. This underlines digital forensic investigations' legal and ethical foundation. © 2024 IEEE.

关键词： Cybersecurity

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Adversarial training for graph neural networks: pitfalls, solutions, and new directions 23

Adversarial training for graph neural networks: pitfalls, so...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Lukas Gosch Simon Geisler Daniel Sturm Bertrand Charpentier Daniel Zügner Stephan Günnemann Department of Computer Science & Munich Data Science Institute Technical University of Munich Department of Computer Science & Munich Data Science Institute Technical University of Munich and Microsoft Research

Despite its success in the image domain, adversarial training did not (yet) stand out as an effective defense for Graph Neural networks (GNNs) against graph structure perturbations. In the pursuit of fixing adversarial training (1) we show and overcome fundamental theoretical as well as practical limitations of the adopted graph learning setting in prior work; (2) we reveal that flexible GNNs based on learnable graph diffusion are able to adjust to adversarial perturbations, while the learned message passing scheme is naturally interpretable; (3) we introduce the first attack for structure perturbations that, while targeting multiple nodes at once, is capable of handling global (graph-level) as well as local (node-level) constraints. Including these contributions, we demonstrate that adversarial training is a state-of-the-art defense against adversarial structure perturbations. Project page: https://***/daml/adversarial-training/

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Enhanced Weld Defect Categorization via Nature-Inspired Optimization-Driven Neural networks

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SN computer science 2024年第8期5卷 1035页

作者： Antony Vigil, M.S. Maheswari, K. Minu, M.S. Kulkarni, Gururaj L. Chandra Sekhar Reddy, L. Satishkumar, P. Haldar, Barun Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Ramapuram Chennai India Department of Computer Science and Engineering CMR Technical Campus Telangana Kandlakoya Hyderabad India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Shamshabad India Department of Computer Science and Engineering (Data Science) CMR College of Engineering & amp Technology Kandlakoya(V) Hyderabad Medchal India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Mechanical and Industrial Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

In many different sectors, the safety and dependability of welded structures are crucially dependent on the accurate identification of weld defects. To address this, we present a novel method for categorizing weld defects named as Pelican Optimization-based convolutional Bi-Long Short-Term Memory networks (POCBi-LSTM). With this integrated strategy, weld defect categorization is intended to be more accurate and effective. To train and assess a suggested classification algorithm, a collection of 220 samples with 5various types of weld faults is utilized. The study's technique comprised denoising with a Modified Median Filter (MMF) for noise reduction and binary histogram equalization (BHE) for image enhancement. The Scale Invariant Feature Transform (SIFT) was used to extract features. The outcomes demonstrate that the proposed strategy performs greater than conventional methods and delivers superior recognition accuracy, precision, recall, and F1 scores of 92.9%, 94.5%, 89.5%, and 92.8%, respectively. Experiment findings demonstrate the effectiveness and advantage of the recommended approach in classifying weld faults. Through the use of an integrated strategy, it is hoped to increase the dependability and effectiveness of weld defect categorization by handling local pattern recognition and long-term interdependence efficiently. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Modified median filter Pelican Optimization-Based convolutional Bi-Long Short Term Memory networks (POCBi-LSTM) Weld defects Weld structures

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Revolutionizing Machine Learning Security: The Role of Quantum-Enhanced Federated Learning 2

Revolutionizing Machine Learning Security: The Role of Quant...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Kannan, E. Ravikumar, S. Carmel Mary Belinda, M.J. Vijay, K. Vathani, Anstey Kannan, Sriram Vel Tech Rangarajan Dr. Sagunthala RD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical And Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India Fidelity Investments Data Engineering Chennai India

ISBN: (纸本)9798331534950

In today's age of collaborative machine learning where data sharing happens across organizations and privacy becomes a real issue. Old-school methods of securing systems work, but tend to lack against popular new attacks. The research uses a novel approach that uses quantum computing to reimagine what privacy means. An article which deals with the combination of quantum technologies and federated learning systems, specifically discussing how it may set a new standard for data privacy. The plan is to use quantum key distribution and entanglement-based protocols in order to fortify the defences of collaborative machine learning systems against adversarial attacks or unauthorized access, according a consortium statement. The approach to quantum -enhanced federated learning presented provides a solution not only with secure improvements but also implementing more extreme circumstances in decentralised learning scenarios, such as challenges due to different data available and regulatory demands. This paper provides a theoretical benchmark for quantum federated learning and focuses on the underlying concepts leading to excellent security guarantees. Applications and simulations show that this framework can be used in practice with effectiveness. Furthermore, this work benchmarks the scalability of quantum federated learning for different classical and common machine-learning models. The results are a major step towards the goal of making more sophisticated privacy solutions available and allow to create secure, private-first workspace for collaborative machine learning. By leveraging the latest in quantum computing along with specialized tactics designed to combat rapidly evolving data protection threats, this approach will provide a strong foundation for future applications of shared machine learning within distributed quantum environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Integration of Process Optimization and Automation: A Way to AI Powered Digital Transformation 1

Integration of Process Optimization and Automation: A Way to...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Kotte, Kulasekhara Reddy Thammareddi, Latha Kodi, Divya Anumolu, Venkateswara Rao Arun Kumar, K. Joshi, Shubham INC SLRI Solutions India India Cyber Security Senior Data Analyst Truist Financial India Technical Architect ERP Solutions- Information Technology India Big Data Architect India Bipin Tripathi Kumaon Institute of Technology Department of Computer Science & Engineering Almora India

ISBN: (纸本)9798331518578

One of the most important factors facilitating AI-powered digital transformation is the combination of process automation and optimisation. Organisations may improve operational efficacy, save costs, and streamline procedures by utilising cutting-edge technology like robotic process automation (RPA), machine learning (ML), and artificial intelligence (AI). While automation seeks to reduce human participation in repetitive and rule-based operations, process optimisation concentrates on improving current business processes to attain improved productivity. The development of intelligent, adaptable, and self-improving systems is made possible by the combination of these two methodologies. Predictive findings, real-time decision-making, and customised customer experiences are further made possible by AI-driven analytics. This connection promotes innovation, scalability, and agility across sectors, giving businesses a competitive edge in the quickly changing digital market. The article examines the main principles, advantages, and difficulties of this shift and provides a road map for businesses looking to automate and optimise processes using AI in order to attain operational excellence and sustainable growth. © 2025 IEEE.

关键词： Decision making

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Cross-cultural contextualisation for recommender systems

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1659-1670页

作者： Hong, Minsung An, Sojung Akerkar, Rajendra Camacho, David Jung, Jason J. Big Data Research Group Western Norway Research Institute Vestlandsforsking Sogndal6851 Norway Department of Computer Science and Engineering Chung-Ang University 84 Heukseok-ro Heukseok-dong Dongjak-gu Seoul Korea Republic of Information Systems Department Technical University of Madrid Madrid28031 Spain

Cultural Heritage (CH) domain is rapidly moving from traditional heritage sites into smart cultural heritage environment through various technologies. As one of the important technologies in the smart space, Recommender Systems (RSs) have been widely utilised to personalised services and matching visitors’ goals and behaviours. Whereas, cultural difference is often considered a barrier to technology transfer or adoption. However, few studies focus on how the cultural factor influences recommendation despite cultural difference largely affects user preferences in the RSs. Furthermore, existing researches have mainly analysed evaluation results of their recommendation to reveal cultural differences, rather than utilising the cross-cultural factors into RSs. In this paper, we propose a novel concept of cross-cultural contextualisation and a model to compute the cross-cultural factor affecting users (countries or cultures) preferences by using matrix factorisation and clustering techniques. In addition, we discuss how to apply the model to RSs in CH domain through cross-domain recommendation techniques. Note that the two computational techniques were used to analyse cross-cultural factors which impact to user preferences, rather than to recommend items. In other words, the proposed model and computing results capable of utilisation into the other RSs as well as various research fields. Results of experiments with a real-world dataset showed effectiveness of the proposed model and supported that there is cultural difference influencing users’ rating behaviours. Furthermore, a systematic analysis of dataset and the experimental results presented that individual users could be considered as country-wise groups for the model to analyse the cross-cultural factors. © Springer-Verlag GmbH Germany, part of Springer Nature 2019.

关键词： Recommender systems

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Hybrid Model for Lung Cancer Prognosis: Integrating SVM and 3D CNN on Nodule data 2

Hybrid Model for Lung Cancer Prognosis: Integrating SVM and ...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Carmel Mary Belinda, M.J. Kannan, E. Alex David, S. Kannan, Sriram Ravi Kumar, S. Vathani, Anstey Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Fidelity Investments Data Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India

ISBN: (纸本)9798331534950

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated. © 2024 IEEE.

关键词： Lung cancer

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Comparison of Hate Speech Identification in Kannada Language Using ML and DL Models

Comparison of Hate Speech Identification in Kannada Language...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gayathri, T. Mahalakshmi, K. Shilpa, M. Jayanthi, M.G. Kannadaguli, Prashanth Cambridge Institute of Technology Department of Information Science Bangalore India Cambridge Institute of Technology Department of Computer Science Bangalore India Cambridge Institute of Technology Department of Cse Bangalore India Dhaarini Academy of Technical Education Senior Data Science Trainer Bengaluru India

ISBN: (纸本)9798350308167

The problem at hand is to create a discrimination system specifically for Indian languages, with an emphasis on Automatic Speech Recognition (ASR) implementations. Hate speech poses a serious challenge to online websites and social media, as well as causing harm, such as spreading hate, inciting violence, and promoting inequality. Macro skills are discriminatory, there is an urgent need for a similar system for regional languages as the country has many different languages and unique cultures. Therefore, this paper intends to gauge the overall performance of 4 characteristic engineering strategies and 4 gadget learning algorithms to examine their overall performance on a publicly-to-be-had dataset with two distinct classes. The experimental consequences confirmed that the bigram capabilities when used with the help vector machine set of rules great carried out with 88% accuracy in ML and 91% of accuracy in DL. This observation has practical implications and can be used as a basis for detecting automated hate speech messages. Moreover, the output of different affinity could be utilized as country-of-artwork strategies to compare destiny research for existing computerized text classification techniques. © 2023 IEEE.

关键词： Audio Deep Learning Hate Machine Learning Offensive speech Social Network speech

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