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12th International Conference on Soft Computing for Problem Solving, SocProS 2023

作者： Kumar, Gaurav Kumar, Ankit Pandy, Saroj Kumar Singh, Kamred Udham Singh, Teekam Kumar, Lalan Choudhury, Tanupriya Department of Computer Engineering and Applications GLA University UP Mathura India School of Computing Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Master of Computer Application GL Bajaj Institute of Technology and Management Greater Noida India Department of Computer Science and Engineering University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9789819732913

This paper aims to provide a comprehensive overview of existing and state-of-the-art fraud and intrusion detection strategies, specifically focusing on incorporating neural networks. We begin by discussing the fundamental concept of data mining and its significance in detecting anomalies. By leveraging advanced machine learning and neural network techniques, we can effectively uncover subtle patterns and irregularities within complex datasets. We explore how the development of anomalies has enhanced our ability to identify and mitigate various threats, such as malware attacks and unlawful practices. Traditional detection strategies have proven effective, but as deep learning progresses, we uncover new opportunities and insights that can significantly enhance the accuracy and efficiency of anomaly detection systems. Throughout this study, we delve into the most cutting-edge approaches, ranging from broad neural networks to shallower architectures, and their applicability to fraud and intrusion detection. We analyze the strengths and limitations of these strategies, shedding light on their performance characteristics, scalability, and interpretability. By addressing these key issues, we aim to provide a comprehensive summary of the latest fraud and intrusion detection advancements driven by deep learning methodologies. This research contributes to the broader understanding of data mining's potential in uncovering anomalies and bolstering security measures across diverse domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Intrusion detection

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Harvesting Insights: Predicting Vegetable Prices with Hybrid CLA-Conv BiGRU Techniques 3

Harvesting Insights: Predicting Vegetable Prices with Hybrid...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Lal Ambashtha, Kanahaiya Praveen Kumar, Chatakunta Pandey, Vivekanand Sakthivel Padaiyatchi, S. Bharathi, A. Navaz, K. Gnsu Faculty of Information Technology Bihar Sasaram India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Amity University Bihar Patna India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering TN Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350381931

The system delves into the important topic of agricultural market pricing, with a specific emphasis on the ever-changing realm of vegetable supply and prices. Stabilizing the supply and prices of vegetables becomes an enormous task when consider that the system are grown outdoors and that weather variables significantly impact harvests. Such unpredictability has far-reaching consequences for the country's economy, highlighting the critical need to develop efficient methods of precise forecasting. The government has made multiple attempts to stabilize vegetable prices and supplies, but these have all been unsuccessful due to the unpredictable weather patterns of late. This system uses state-of-the-art deep learning methods, notably the Hybrid CLA-Conv BiGRU model, which incorporates a novel preprocessing strategy based on the HP Filter, to conquer this obstacle. A strong prediction model for vegetable prices is constructed using the process, which includes feature extraction, scoring, and selection. © 2024 IEEE.

关键词： Vegetables

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Performance Analysis of Resource Allocation Algorithms for Vehicle Platoons over 5G eV2X Communication 17

Performance Analysis of Resource Allocation Algorithms for V...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mandal, Gulabi Roy, Anik Palit, Basabdatta Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Advanced Technology Development Center Kharagpur India National Institute of Technology Department of Electronics and Communication Engineering Rourkela India

ISBN: (纸本)9798331531195

Vehicle platooning is a cooperative driving technology that can be supported by 5G enhanced Vehicle-to-Everything (eV2X) communication to improve road safety, traffic efficiency, and reduce fuel consumption. eV2X communication among the platoon vehicles involves the periodic transmission of Cooperative Awareness Messages (CAMs) containing vehicle information under strict latency and reliability requirements. These requirements can be maintained by administering the assignment of resources, in terms of time slots and frequency bands, for CAM transmission in a platoon, with the help of a resource allocation mechanism. State-of-the-art on control and communication design for vehicle platoons either consider a simplified platoon model with a detailed communication architecture or consider a simplified communication delay model with a detailed platoon control system. Departing from existing works, we have developed a comprehensive vehicle platoon communication and control framework using OMNET + +, the benchmarking network simulation tool. We have carried out an inclusive and comparative study of three different platoon Information Flow Topologies (IFTs), namely Car-to-Server, Multi-Hop, and One-Hop over 5G using the Predecessor-Leader Following platoon control law to arrive at the best-suited IFT for platooning. Secondly, for the best-suited 5G eV2X IFT, we have analyzed the performance of three different resource allocation algorithms, namely Maximum of Carrier to Interference Ratio (MaxC/I), Proportional Fair (PF), and Deficit Round Robin (DRR). Exhaustive system-level simulations show that the One-Hop information flow strategy along with the MaxC/I resource allocation yields the best Quality of Service (QoS) performance, in terms of latency, reliability, Age of Information (AoI), and throughput. © 2025 IEEE.

关键词： Vehicle to vehicle communications

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Improved Healthcare System with Quantum Computing

Improved Healthcare System with Quantum Computing

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Chandrasekaran, Sriramakrishnan Agrawal, Rashmi Dutt, Vishal Vyas, Narayan Solution Architect Tcs India Manav Rachna International Institute of Research and Studies India Aryabhatta College Department of Computer Science Ajmer India Mds University Department of Computer Science Ajmer India

ISBN: (纸本)9798350322309

Health care is playing a major role in designing any kind of prediction model and there is a large amount of data related to different diseases is available over the globe. A major part of machine learning lies in the identification of the data related to different diseases and make them helpfulfor people. The major implementation is in the identification ofthe safest medical data systems if the new challenging task where we can implement quantum computing as the main lead to process the medical data and making the new data systems which are most secured to manage the medical data which is useful to identify the fastest method to form a drug and also to manage in the identification of the safe drug mechanism for thedisease which is not even in identification. Quantum computingis a challenging area that is like an extension to the current scenario of medical imaging. Improved healthcare system using machine learning and quantum computing gives a new age to medical sciences to identify the approximate solutions to the different medical diseases. This article works on the implementation of such medical advancements to a specificdisorder based on human genetics and DNA sequencing. The Quantum computing approach succeeded in recognizing the approximate medical sequences for a particular disease. © 2023 IEEE.

关键词： Diagnosis

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Analysis of Various COVID-19 Prediction Techniques

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IEIE Transactions on Smart Processing and Computing 2021年第4期10卷 323-329页

作者： Arora, Rakesh Kumar Gupta, Manoj Kumar Bhati, Bhoopesh Singh Department of Computer Science and Engineering Krishna Engineering College UP India Department of Computer Science and Engineering Trinity Institute of Innovations in Professional Studies UP India Department of Computer Science and Engineering Ambedkar Institute of Advanced Communication Technologies and Research Delhi India

There is no precise treatment available for COVID-19, but treatment of a number of signs/symptoms is possible. This treatment is provided in accordance with the clinical position of the patient. Furthermore, supplementary care is proven as efficient for infected people. COVID-19 prediction techniques follow various steps, which include dataset collection, pre-processing, feature extraction, and classification. Numerous COVID-19 prediction techniques have been proposed in the literature. In this paper, various techniques are systematically reviewed on the basis outcomes, advantages, and disadvantages. Copyrights © 2021 The institute of Electronics and Information Engineers

关键词： COVID-19

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Blockchain Ethereum-Based Decentralized Secure Ride-Sharing Framework for Carpooling System

Blockchain Ethereum-Based Decentralized Secure Ride-Sharing ...

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Ubiquitous Computing and Intelligent Information Systems (ICUIS), International Conference on

作者： J. Wessly R. Durga Department of Computer science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Carpooling involves sharing a car for a journey, typically with two or more individuals. Car-sharing systems play a crucial role in addressing urban challenges by providing shared vehicles and decreasing the dependence on private cars. Car-sharing systems present security concerns due to transmitting sensitive information through a shared centralized network, including identity, location, and access codes. It is crucial to develop secure authentication methods to prevent unauthorized access and use of this information for illegitimate purposes. To address the problem, this research presents a Blockchain Ethereum-based Decentralized Secure Ride-sharing Framework (BEDSRF) for carpooling systems. The study introduces a Role-based Access Control (RBAC) method to determine user roles for accessing their information. Then, based on these roles, the data is encrypted using the Elliptic Curve Cryptography (ECC) method. The BEDSRF approach verifies each node and transmits the information within the decentralized network. Additionally, the Key Policy Authentication Scheme (KPAS) is utilized to authenticate the user's key and enable role-based decryption for the end user. Furthermore, experiments were carried out to confirm the efficiency and resilience of the proposed approach. The findings indicate that the proposed method outperforms existing carpooling schemes in terms of both security and authentication performance.

关键词： Privacy Shared transport Authentication Elliptic curve cryptography Ubiquitous computing Blockchains Security Automobiles Reliability Resilience

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Intelligent network intrusion detection system using optimized deep CNN-LSTM with UNSW-NB15

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International Journal of Information Technology (Singapore) 2025年 1-17页

作者： Thaljaoui, Adel Department of Computer Science and Information College of Science at Zulfi Majmaah University Al-Majmaah 11952 Saudi Arabia Preparatory Institute for Engineering Studies of Gafsa Street Al Khayzourane-Zarroug Gafsa 2112 Tunisia

In the era of interconnected technologies, the ongoing threat of intrusion attacks poses significant risks, exposing individuals and organizations to potentially severe repercussions. Network Intrusion Detection System (IDS) has a crucial role in securing these networks. In this paper, we propose an hybrid model combining the convolution neural network (CNN) and the long short-term memory (LSTM) with hyper-parameters optimization using Bayesian optimization method for attacks detection in IoT. We train and test our hybrid model using the benchmark dataset UNSW-NB15 for evaluating its effectiveness in intrusion detection. The performance of our model is assessed using various metrics, including accuracy, precision, recall, and F1-score. In addition, we conduct a comparative study to highlight the effectiveness of our proposed model, which achieved remarkable accuracy on the testing set. This evaluation underscores the model's potential for attacks detection. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Convolutional neural network (CNN) Deep learning Internet of Things (IoT) Intrusion detection system (IDS) Long short-term memory (LSTM) Optimization

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An Improved Deep Forest Classification and Intelligent Segmentation for skin Cancer Prediction Technique Inspired by Whale Optimization

An Improved Deep Forest Classification and Intelligent Segme...

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Advances in Modern Age Technologies for Health and Engineering science (AMATHE), International Conference on

作者： Siva Prasad Reddy K.V S. Meera Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Pallavaram Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Fast malignant melanoma tumor identification is vital for successful therapy. Melanoma has now been universally acknowledged to be the most lethal form of malignancy over all else since, even if not caught and addressed promptly, it possesses a substantially higher propensity to migrate towards remaining regions of physique. Definitive presentation of various disorders relies largely on quasi therapeutic machine vision / procuring of disorders scanned pics. These methods offer a tool for autonomous image enhancement that allows for a quick and precise appraisal of the abscess. Deficiency of vast samples constitutes one of primary hurdles for designing a trustworthy autonomous classification scheme. This research suggests integrating Deep—learning & Image—processing to develop an algorithm for artificially recognizing melanoma. This survey's phase involves amassing a repository of dermoscopy pics, pre—Processing, separation utilizing hybridized Fuzzy-C-Means, and weighted enhancement employing the whale-optimization-algo. Following the subdivision of the dermoscopic imagery, the chapped skin cellular membranes traits are captured utilizing extraction of features. For the layering of the retrieved attributes, Long -shortterm -memory {LsTm}, bidirectional LsTm && Deep-Forest predictors are deployed. After using freely searchable sample group, the reported classifier reliability was Ninety Seven Percent && the segmentation effectiveness reached ninety three percent.

关键词： Hair Training Object segmentation Melanoma Feature extraction Solids Skin

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Preprocessing and feature extraction of MRI Liver Tumour images using A novel multi-class identification (NMCI) framework

Preprocessing and feature extraction of MRI Liver Tumour ima...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： A. Bhagya S. Perumal Department of Computer Science Vels Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

One of the leading and most serious causes of death worldwide is liver tumours. Currently, one of the most well-known computer Vision disciplines (CV) is Medical Imaging (MI), which aids radiologists and physicians in the early detection and diagnosis of liver tumours. In order to diagnose patients, radiologists and doctors read hundreds of images using manual or semi-automated technologies, such as Magnetic resonance imaging (MRI). Consequently, a fully automated approach is required to use MRI scans, the most common and extensively used imaging modality, for early tumour detection and diagnosis. Implementing machine learning (ML) techniques in finding the tumour affected parts are the main focus of the proposed work. The multiclass liver tumour classification procedure followed in the tumour identification process follows, Random Forest (RF), Random Tree (RT), Logistic Model Tree (LMT) and J48 with multiple automated Region of Interest (ROI). Hemangioma, cyst, hepatocellular carcinoma, and metastasis are the four basic tumour classes, which are to be classified before severity increases. Transformation procedures followed in image processing are conversion of collected MRI images into grayscale, then boosting the contrast with histogram equations. The Gabor filter was used to minimise noise, and an image sharpening technique was used to enhance image quality. Furthermore, employing texture, binary, histogram, and rotational, scalability, and translational (RST) algorithms, 55 characteristics of various pixel dimensions were obtained for each ROI. Out of these 55 characteristics, 20 optimised features were chosen for classification using the correlated feature selection approach. RF and RT outperformed J48 and LMT, with respective accuracy of 97.48% and 97.08%, according to the collected data. The new framework that has been suggested will aid doctors and radiologists in the diagnosis of liver tumours.

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