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machine learning and Autonomous Systems (ICMLAS), International Conference on

作者： X. Arogya Presskila J. Relin Francis Raj P. Selva Kumar G. Ram Sankar G. Vinoth Rajkumar P. Sundaravadivel Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Management Studies Francis Xavier Engineering College Tirunelveli India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J. P. College of Engineering Tenkasi India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The rapid evolution of E-Commerce has led to the emergence of Quick Commerce (Q-Commerce), a model that prioritizes ultra-fast deliveries and real-time decision-making. This paper proposes a Hybrid learning-based framework that enhances Q-Commerce operations by integrating Self-Supervised, Supervised, Reinforcement, and Unsupervised learning techniques. The model leverages BERT for self-supervised pretraining, enabling context-aware customer personalization and improved search relevance. LSTMs and CNNs are employed for demand forecasting and product classification, ensuring optimized inventory management. GNNs enhance personalized recommendations by analyzing user-product relationships. For logistics optimization, DQN and PPO are implemented to dynamically adjust last-mile delivery routes, reducing delivery times and operational costs. To ensure fraud prevention, AEs and GANs detect anomalous transaction behaviors, improving security while minimizing false positives. The proposed hybrid deployment strategy, combining cloud-based AI inference and Edge AI at micro-fulfillment centers (MFCs), enhances system responsiveness and scalability. The framework is evaluated using MAE, RMSE, HR, Fl-Score, and ROC curves, ensuring accurate demand prediction, optimized logistics, and effective fraud detection. Future advancements in Autonomous Deliveries, AI-Powered Warehousing, and Blockchain Integration will further revolutionize Q-Commerce, making it an indispensable extension of E-Commerce.

关键词： Costs Scalability Demand forecasting Edge AI Real-time systems Fraud Electronic commerce Security Optimization Logistics

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A Hybrid learning-Based Q-Commerce Framework Evolving from E-Commerce 2

A Hybrid Learning-Based Q-Commerce Framework Evolving from E...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Presskila, X. Arogya Raj, J. Relin Francis Kumar, P. Selva Sankar, G. Ram Rajkumar, G. Vinoth Sundaravadivel, P. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi 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 Francis Xavier Engineering College Department of Management Studies Tirunelveli India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India J. P. College of Engineering Department of Electronics and Communication Engineering Tenkasi India Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331505745

关键词： Marketplaces

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Unleashing WSN Potential: Bowerbird Optimized Energy-Conscious Multipath Clustering with Causal Dilated Cosine for Enhanced Load Balancing

Unleashing WSN Potential: Bowerbird Optimized Energy-Conscio...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Rakesh Kumar Joon Anitha R Saravana Selvan P. S. Latha Mageshwari S. Gayathri Priya K. Malathi Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Kablana Haryana India Department of Artificial Intelligence and Data Science S.A. Engineering College Chennai Tamil Nadu India School of Professional Engineering Manukau Institute of Technology Auckland New Zealand Department of Science & Humanities (Physics) R. M. K. Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Kavaraipettai Tamil Nadu India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Wireless Sensor Networks (WSNs) are essential in the collection of real time data across different fields, including environmental monitoring and process control. However, due to the bounded amount of energy in sensor nodes, network lifetime becomes an issue, which hinders performance and reliability. Thus, optimization of WSN performance in an attempt to minimize energy consumption is essential for reliable data transmission. In conventional routing and clustering models, network load distribution is not achieved adequately, resulting in a more significant loading of nodes and faster exhaustion of energy. To solve these problems, it is necessary to carefully design load distribution and path finding algorithms to support long-term WSN operation. The objective of this research is to design a framework-Ski-CDiCo-BO-that integrates Skill Optimization for clustering, and ensuring an energy-efficient paths while reducing redundant transmissions on nodes and conserving node energies, and uses the Causal Dilated Cosine Architecture for sophisticated load balancing, and Bowerbird Optimization for improving the parameterization of neural networks. Analysis reveals that the presented Ski-CDiCo-BO framework provides improved performance of various parameters including data delivery success rate of above 99.8% and network lifetime gain by up to 99.5%. Also, load distribution accuracy remains above 99.3%, which indicates that the framework is capable of avoiding node overload and end-to-end delay improved upon by 99.6%, indicating that the solution maintains high parameters even in dynamically changing conditions. In conclusion, Ski-CDiCo-BO maximizes WSN efficiency by implementing cutting-edge clustering, load balancing, and optimization techniques, setting new standards for energy-conscious and reliable sensor network performance across diverse applications.

关键词： Wireless sensor networks Energy consumption Scalability Heuristic algorithms Load management Routing Energy efficiency Reliability Environmental monitoring Optimization

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Indian Food Segmentation and Calorie Estimation Using CatBoost and Masked Convolutional Neural Networks

Indian Food Segmentation and Calorie Estimation Using CatBoo...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Nabin Acharya Ayush Kumar Singh Anshuman Kumar Dwivedi Jayanthi M. G Prashant Kannadaguli Dept. of Information Science and Engineering Cambridge Institute of Technology Bengaluru India Dept. of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bengaluru India Dept. of Computer Science and Engineering Cambridge Institute of Technology Bengaluru India Dhaarini Academy of Technical Education Bengaluru India

Accurate food segmentation and calorie estimation play a pivotal role in effective dietary monitoring and health management. In this paper, after extensive experimentation on a diverse Indian food dataset of 20 classes collected by us, we present two separate innovative models designed to address these tasks with exceptional precision. The first model employs a machine learning approach by incorporating the CatBoost algorithm, while the second model leverages a deep learning technique utilizing the U-Net architecture for high-quality image segmentation. Additionally, we merge the U-Net outcomes with a convolutional neural network (CNN) to enhance the deep learning-based classification. Our models effectively handle categorical features and address imbalanced data, resulting in significantly improved accuracy in food item delineation within images. This advancement enables a more reliable and comprehensive analysis of dietary patterns. We demonstrate the effectiveness and robustness of our proposed models. Comparative evaluations against state-of-the-art methods verify exceptional performance in terms of segmentation accuracy, precision in food classification, and estimation of calorie intake. Notably, our models are specifically tailored for food images and do not take ingredients or any other information into consideration. In comparison CNN model is performs better than CatBoost, achieving an accuracy of 78%.

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Pre-training-free Image Manipulation Localization through Non-Mutually Exclusive Contrastive learning

arXiv

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arXiv 2023年

作者： Zhou, Jizhe Ma, Xiaochen Du, Xia Alhammadi, Ahmed Y. Feng, Wentao College of Computer Science Sichuan University China School of Computer and Information Engineering Xiamen University of Technology China Strategy Affairs Office Mohamed Bin Zayed University for Humanities United Arab Emirates Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education of China China

Deep Image Manipulation Localization (IML) models suffer from training data insufficiency and thus heavily rely on pre-training. We argue that contrastive learning is more suitable to tackle the data insufficiency problem for IML. Crafting mutually exclusive positives and negatives is the prerequisite for contrastive learning. However, when adopting contrastive learning in IML, we encounter three categories of image patches: tampered, authentic, and contour patches. Tampered and authentic patches are naturally mutually exclusive, but contour patches containing both tampered and authentic pixels are non-mutually exclusive to them. Simply abnegating these contour patches results in a drastic performance loss since contour patches are decisive to the learning outcomes. Hence, we propose the Non-mutually exclusive Contrastive learning (NCL) framework to rescue conventional contrastive learning from the above dilemma. In NCL, to cope with the non-mutually exclusivity, we first establish a pivot structure with dual branches to constantly switch the role of contour patches between positives and negatives while training. Then, we devise a pivot-consistent loss to avoid spatial corruption caused by the role-switching process. In this manner, NCL both inherits the self-supervised merits to address the data insufficiency and retains a high manipulation localization accuracy. Extensive experiments verify that our NCL achieves state-of-the-art performance on all five benchmarks without any pre-training and is more robust on unseen real-life samples. https://***/Knightzjz/NCL-IML. © 2023, CC BY.

关键词： Benchmarking

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AI-Powered Early Detection of Sepsis using Infrared Image Multi-Target based Faster Region-based Convolutional Neural Network

AI-Powered Early Detection of Sepsis using Infrared Image Mu...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： Muntather Almusawi Vidya Kamma Arelli Madhavi S. Jeyanthi B Santhiya Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Neil Gogte Institute of Technology Rangareddy Hyderabad India Department of Artificial Intelligence & Machine Learning Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India Department of Computer Applications New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of information Technology Bannari Amman Institute of Technology Sathyamangalam India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Sepsis is a complex and heterogeneous syndrome that continues to pose significant challenges to healthcare globally, particularly in realm of Al-powered early detection. The increasing demand for Intensive Care Unit (ICU) admissions and associated rising costs have urged the need for effective management strategies. The problem of the sepsis addresses the issues of misclassification and insurance organizations that arise from inefficient the medical recorder. This paper, proposed Infrared Image multi-target based Faster Region-based Convolutional Neural Network (FR-CNN) technique efficiently achieve better performance in the sepsis detection. Initially data obtained from the Best care database at KAMC with a focus on patient care in ICU through analysis of medical records. The pre-processing utilized min-max normalization and enhance the image quality which is uniformly to process the feature vector. The FR-CNN architecture is involved to identify the sepsis in multiple within infrared image. The model is trained using labelled data that include various infrared image depicting sepsis related feature and detection the complex patterns associated with sepsis, which is achieve better accuracy. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall and f1-score metrics on Best care database.

关键词： Training Measurement Accuracy Databases Sepsis Software systems Vectors Robustness Convolutional neural networks Medical diagnostic imaging

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GSLB: the graph structure learning benchmark 23

GSLB: the graph structure learning benchmark

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

作者： Zhixun Li Liang Wang Xin Sun Yifan Luo Yanqiao Zhu Dingshuo Chen Yingtao Luo Xiangxin Zhou Qiang Liu Shu Wu Jeffrey Xu Yu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences and Department of Automation University of Science and Technology of China Department of Automation University of Science and Technology of China School of Cyberspace Security Beijing University of Posts and Telecommunications Department of Computer Science University of California Los Angeles Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University

Graph Structure learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB.

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Standardized Post-Quantum Cryptography and Recent Developments in Quantum computers

Standardized Post-Quantum Cryptography and Recent Developmen...

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Advances in computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Shiney Chib Harshitha Raghavan Devarajan Swathi Chundru Padmaja Pulivarthy R. Augustian Isaac KrishnaMurthy Oku Department of Management Datta Meghe Institute of Management Studies Nagpur Maharashtra Department of Computer Science New York University Motivity labs INC Samsung Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai Tamil Nadu Department of Automation platform Development Tech Lead Infrastructure/Cloud Architecture AT&T Services Inc

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Traditional cryptographic frameworks are seriously threatened by the quick development of quantum computing, especially those that depend on public-key cryptosystems like RSA and ECC. Post-quantum cryptography (PQC) seeks to create cryptographic methods that are impervious to quantum computer assaults in order to overcome this difficulty. The National Institute of Standards and Technology (NIST), which has selected and standardised quantum-resistant techniques like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, has sparked recent advancements in PQC. At the same time, the need for reliable PQC solutions has increased due to developments in quantum computing, such as IBM's improvements in qubit stability and Google's “quantum supremacy” assertion. In order to protect data security in the post-quantum age, PQC regulations must be adopted globally, as highlighted by the intersection of these fields. Current developments in PQC standardisation and the changing state of quantum computing technology are highlighted in this paper.

关键词： Training Technological innovation Quantum computing Qubit Switches NIST Planning Internet Encryption computer security

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Risk Management in Robotic Process Automation

Risk Management in Robotic Process Automation

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dileep Kumar Yendluri Ramya Thatikonda Reshmi Thatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of Central Missouri Williamsburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA’s impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation’s transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures.

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Lung Nodule Segmentation and Classification Using Conv-Unet Based Deep learning

Lung Nodule Segmentation and Classification Using Conv-Unet ...

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Network, Multimedia and Information Technology (NMITCON) International Conference on

作者： Akash V Monish Sai. R Amith K B Jayanthi M.G. Prashanth Kannadaguli Dept. of Artificial Intelligence and Machine Learning Cambridge Institute of Technology Bangalore India Dept. of Information Science and Engineering Cambridge Institute of Technology Bangalore India Dept. of Computer Science and Engineering Cambridge Institute of Technology Bangalore India Dhaarini Academy of Technical Education Bangalore India

This paper proposes a Convolutional U-Net architecture, a variation of the standard U-Net architecture for the segmentation of lung nodules and classification using Deep learning on computerized Tomography (CT) scans. The Primary steps employed are Preprocessing, Segmentation and Classification of nodules. In the preprocessing step, the lung region is segmented using techniques such as normalization, median filtering, Kmeans clustering, morphological and thresholding operations to extract lung Region of Interest (ROI) and nodule masks. The Conv-Unet design adds more convolutional layers to the standard U -Net architecture to help capture complicated patterns and boundaries of lung nodules for more accurate segmentation. Categorization of the segmented lung nodules is done using a CNN network on the LIDC-IDRI, and LUNA16 dataset. Overall, this model achieves a dice score of 62% and classification accuracy of 82% displaying appropriate performance in comparison with other variations of the U-Net architecture.

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