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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Sharma, Aditi Tyagi, Mayank Kumar, Sunil Department of Information Technology Meerut Institute of Engineering and Technology Utter Pradesh Meerut India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Utter Pradesh Meerut India

ISBN: (纸本)9789819717231

In this study, a convolutional neural network (CNN) model was put out for the prediction of customer turnover in the telecom sector. In earlier studies, numerous supervised machine learning models were developed and applied to forecast client attrition. However, in order to create these models, attribute selection must be done by humans, which is tedious, time-consuming, tailored to specific datasets, and usually results in attribute selection problems. In order to eliminate human attribute selection and accompanying issues, this study suggested a convolutional neural network model for forecasting consumer churning behaviour. The fourteen thousand data examples obtained from one of the major cellular operators operating in Nigeria were divided into two datasets. Our model was created and put into use using the Python programming language and the Anaconda distribution. Our first preference was Jupyter notebook. A second CNN model, two multi-layer perceptron (MLP) models, and three additional models were created in order to conduct a comparable comparison. The accuracy rates for the MLP models are 81% and 89% for convolution neural network 1 and convolution neural network 2, and 80% and 81% for MLP 1 and MLP 2, respectively. When a company tries to bring in new clients, the costs are far higher than when it tries to lose old ones. All of their plans and strategies are structured to win over existing customers, lose new ones, and ultimately increase customer satisfaction. One of the biggest issues for this company, though, is user churn. It deprives a bank of a variety of income sources, including fees. Furthermore, in the Islamic banking system, the primary source of a bank’s income comes from consumer deposits. It might result in a bank withdrawing some of its deposits. If a bank ignores the loss of these two sources of income and the potential rise in reputational damage, it may be on the verge of failure. The current study offers a preliminary forecast of customer

关键词： Forecasting

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A Review of IoT-Driven Innovations in Supply Chain Management 3

A Review of IoT-Driven Innovations in Supply Chain Managemen...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chandankhede, Arpit Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Meghe Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Engineering Sawangi Meghe Maharashtra Wardha442001 India

ISBN: (纸本)9798331509675

Supply Chain Management (SCM) worldwide makes major advancements through the Internet of Things (IoT) by enabling real-time monitoring and prediction analytics and automatic decision capabilities in worldwide deliver chains. Through IoT technology integration with Artificial Intelligence (AI) and blockchain and cloud computing the supply chain solves major operational problems including monitoring difficulties and waste reduction and regulatory compliance requirements and operation leadership deficits. The implementation of IoT for supply chain management encounters major adoption barriers from high installation costs combined with security risks and operability complications as well as limited scalability capacity that affects especially small to medium enterprises (SMEs) and developing markets. The evaluation paper examines the shifting impact of IoT in supply chain management which enhances performance metrics and brings greater clarity and strengthens supply chain resilience. A scientist studied existing papers and case studies to reveal several essential advantages which include better inventory management, predictive analysis systems and more effective supply-chain collaborations. The analysis emphasizes effective solutions for security privacy and moral and environmental issues to guarantee sustainable expanded IoT adoption. This paper delivers practical knowledge alongside best practices to help organizations adopt IoT systems which create adaptable efficient deliver chains with sustainability at their core thus achieving long-term market benefits in today's complex global marketplace. © 2025 IEEE.

关键词： Inventory control

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Satellite Imagery Analysis for Crop Type Segmentation Using U-Net Architecture 2

Satellite Imagery Analysis for Crop Type Segmentation Using ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ayushi Buttar, Preetpal Kaur Department of Computer Science and Engineering Sant Longowal Institute of Engineering and Technology Punjab Longowal Sangrur141806 India

Analyzing satellite images plays a critical role in surveying and monitoring agricultural areas, enabling various applications such as precision agriculture, land use planning, and yield estimation. One of the crucial steps in these applications is accurate crop-type segmentation from satellite imagery. This task becomes challenging in the scenarios of smallholder farms due to their irregular field shapes, frequent cloud cover, small plot sizes, and a severe shortage of training data, which make it difficult to apply conventional machine learning methods effectively. In this research work, a technique for segmenting the crop types from the difficult scenario of smallholder farms based on fully convolutional encoder-decoder semantic segmentation architecture, U-Net, has been proposed and its performance has been compared with the traditional machine learning techniques. To evaluate the proposed approach, experimental assessments were conducted on the Kenya satellite imagery crop dataset. The proposed technique achieved an accuracy, precision, recall, and F1 score of 95.3%, 80.2%, 68.1%, and 73.6%, respectively. The results demonstrate that the U-Net model surpasses conventional image classification methods in accurately segmenting different crop types. © 2024 Elsevier B.V.. All rights reserved.

关键词： Crops

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Enhanced Vehicle Detection by Optimized Image Compression in NextG Wireless Network Autonomous Vehicles System 100

Enhanced Vehicle Detection by Optimized Image Compression in...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and Communication (ISAC), and O-RAN. © 2024 IEEE.

关键词： Radio access networks

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juktoMala: A Handwritten Bengali Consonant Conjuncts Dataset for Optical Character Recognition Using BiT-based M-ResNet-101x3 Architecture 3

juktoMala: A Handwritten Bengali Consonant Conjuncts Dataset...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Hasan, Md. Mehedi Hossain, Md. Ali Srizon, Azmain Yakin Sayeed, Abu Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Department of Mechatronics Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350345360

Bengali, the seventh most spoken language in the world by the number of speakers, doesn't have a well-established Optical Character Recognition (OCR) system for handwritten texts. One of the major reasons behind this lacking is contributed to having no complete conjuncts database. No dataset available today covers all the conjunct characters that are used by authors around the globe. In this research, we prepared a complete dataset consisting of 292 consonant conjunct characters, which is the biggest consonant conjunct character dataset to date by the number of classes available in the literature to our knowledge. We applied Big Transfer-based M-ResNet-101x3 Deep Convolutional Neural Network (DCNN) which achieves 91.32% accuracy that outperforms the baseline EfficientNetB7 approach which obtained 81.05% accuracy. © 2023 IEEE.

关键词： Convolutional neural networks

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An Efficient Quantum Enabled Machine Algorithm by Universal Features for Predicting Botnet Attacks in Digital Twin Enabled IoT Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 947-956页

作者： Katta Rajesh Babu Naramula Venkatesh K.Shashidhar Yellampalli Dasaratha Rami Reddy K.Naga Prakash Department of ECE Koneru Lakshmaiah Education FoundationVijayawada 520002India School of Computer Science and Artificial Intelligence SR UniversityWarangal 506371India Department of Electronics & Communication Engineering Siddhartha Institute of Technology & SciencesHyderabad 500091India Department of CSE Chaitanya Bharathi Institute of TechnologyProddatur 520002India Department of ECE Seshadri Rao Gudlavalleru Engineering CollegeProddatur 520002India

In this manuscript,the authors introduce a quantum enabled Reinforcement Algorithm by Universal Features(REMF)as a lightweight solution designed to identify and assess the impact of botnet attacks on 5G Internet of Things(IoT)***'s primary objective is the swift detection of botnet assaults and their effects,aiming to prevent the initiation of such *** algorithm introduces a novel adaptive classification boosting through reinforcement learning,training on values derived from universal features extracted from network transactions within a given training *** the prediction phase,REMF assesses the Botnet attack confidence of feature values obtained from unlabeled network *** then compares these botnet attack confidence values with the botnet attack confidence of optimal features derived during the training phase to predict the potential impact of the botnet attack,categorizing it as high,moderate,low,or not-an-attack(normal).The performance evaluation results demonstrate that REMF achieves the highest decision accuracy,displaying maximum sensitivity and specificity in predicting the scope of botnet attacks at an early *** experimental study illustrates that REMF outperforms existing detection techniques for predicting botnet attacks.

关键词： botnet reinforcement learning 5G-enabled Internet of Things(IoT) network security machine learning

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Dual-Model Approach for Lung Disease Classification using Convolutional Neural Networks and Support Vector Machines 2

Dual-Model Approach for Lung Disease Classification using Co...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shaik, Mohammed Ali Fatima, Afshan Parveen, Masrath Rani, A. Soumya Mohammad, Akheel Rahim, Abdul School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computer Science & Engineering CMR Institute of Technology Hyderabad India Department of Information Technology Vidya Jyothi Institute of Technology Hyderabad India Hyderabad India Department of Computer Science & Engineering Dr. Vrk women's College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Secunderabad India

ISBN: (纸本)9798331504960

This paper proposes a solution of integrating the use of Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) for categorizing lung conditions of people with and without lung disease. The proposed model has achieved a high F1-Score of 97.4%, a high Precision of 93.17% and high Recall of 93. 63 for the Healthy class. The F1 Scores of this study for the Infected Lung class were as follows: the accuracy of F1 Score was 97.4%;Precision was 94.12%;and Recall was 92.06%. The combining of SVM and CNN model was quite accurate with the accuracy of 97% and the model was accurate in multiple domains of assessment. These results suggest that the application of CNNs and SVMs to the task of medical image analysis constitutes a promising and accurate approach for diagnosing lung diseases. © 2024 IEEE.

关键词： Lung cancer

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READS: Resource efficient attack detection system for drones

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Multimedia Tools and Applications 2024年 1-20页

作者： Vishnu, Aditya Arora, Sumedha Kerala University of Digital Sciences Innovation and Technology Kerala Thiruvananthapuram India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahemdabad India

Unmanned Aerial Vehicles (UAVs), commonly known as aerial drones, have gained popularity due to their versatility and applications in various industries. However, their increasing use has raised concerns about cybersecurity and the vulnerability of networked UAVs to malicious attacks. A deep learning-based Attack Detection System (ADS) is essential for mitigating cyber threats to UAVs. It can provide proactive protection even against unknown attacks. Also, for resource constraints of UAVs, ADS must be resource-efficient to optimize the deep learning model for embedded hardware, making a low code footprint. Therefore, this work focuses on developing a Resource Efficient Attack Detection System (READS) for drones that uses a lite deep learning-based network activity classification algorithm for detecting cyber threats on UAVs. The entire system has been implemented in three phases, where the first phase includes dataset training. The second phase involves running multiple conventional models to choose the best conventional model for intrusion detection using accuracy, precision, and recall. This is followed by phase three, which converts the selected conventional Dense Neural Network (DNN) model into a lite weight (Tiny) model that works on UAVs. The selected Lite DNN model is evaluated on a real-time testbed. The optimized model could lower the storage space and generates 97.69% accuracy. The proposed system consumes 30% of the original power. Hence, it allows efficient threat detection while minimizing the impact on UAV resources. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Drones

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Sybil Assault Detection in VANET with Efficient Network Analysis

Sybil Assault Detection in VANET with Efficient Network Anal...

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2024 MIT Art, Design and technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Mukherjee Mathur, Shubhra Jadhav, Nagesh Gupta, Ravindra Design and Technology University Pune Sarvepalli Radakrishnan University School of Computing MIT Art Department of Computer Science and Engineering Bhopal India Design and Technology University School of Computing MIT Art Department of Computer Science and Engineering Pune India Sarvepalli Radakrishnan University School of Computing Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350362879

A developing technology that can be highly helpful for both intelligent transportation services and safety and security is the Automobile ad hoc network, or VANET. Nevertheless because of the great mobility and wireless connection of cars, there are numerous security risks that jeopardize people's safety and security when driving. The Sybil assault, in which the perpetrator makes up a false identity to obtain significant influence within the network and delay certain services and manipulate others by fictitious voting, is one of the main security issues. People can avoid being duped by an attacker or falling into traps when attacks are detected early. This study describes two methods for detecting Sybil attacks: the first uses the Poisson Distribution Method in order to forecast traffic on the street, whereas the latter examines network efficiency for packet delivery ratio (PDR) in both benign additionally malicious environments. According to the simulation's findings, PDR lowers the number of fictitious cars in the network. Our method is straightforward and efficient because it doesn't involve a lot of computational overhead and doesn't breach users' privacy concerns. © 2024 IEEE.

关键词： Poisson distribution

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SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgment Prediction with Legal Clue Tracing

SEMDR: A Semantic-Aware Dual Encoder Model for Legal Judgmen...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Liu, Pengjie Zhang, Wang Ding, Yulong Zhang, Xuefeng Yang, Shuang-Hua College of Science Northeastern University China School of Computer Science and Engineering Southern University of Science and Technology China University of Reading Department of Computer Science United Kingdom

ISBN: (纸本)9781665410205

Legal Judgment Prediction (LJP) aims to form legal judgments based on the criminal fact description. However, researchers struggle to classify confusing criminal cases, such as robbery and theft, which requires LJP models to distinguish the nuances between similar crimes. Existing methods usually design handcrafted features to pick up necessary semantic legal clues to make more accurate legal judgment predictions. In this paper, we propose a Semantic-Aware Dual Encoder Model (SEMDR), which designs a novel legal clue tracing mechanism to conduct fine-grained semantic reasoning between criminal facts and instruments. Our legal clue tracing mechanism is built from three reasoning levels: 1) Lexicon-Tracing, which aims to extract criminal facts from criminal descriptions;2) Sentence Representation Learning, which contrastively trains language models to better represent confusing criminal facts;3) Multi-Fact Reasoning, which builds a reasons graph to propagate semantic clues among fact nodes to capture the subtle difference among criminal facts. Our legal clue tracing mechanism helps SEMDR achieve state-of-the-art on the CAIL2018 dataset and shows its advance in few-shot scenarios. Our experiments show that SEMDR has a strong ability to learn more uniform and distinguished representations for criminal facts, which helps to make more accurate predictions on confusing criminal cases and reduces the model uncertainty during making judgments. All codes will be released via GitHub. © 2024 IEEE.

关键词： Semantics

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