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arXiv 2025年

作者： Khan, Khalil Syed, Ikram Ullah, Farhan Ullah, Irfan Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Kazakhstan Kazakhstan Dept of Information and Communication Engineering Hankuk University of Foreign Studies Korea Republic of School of Internet of Things Wuxi University Wuxi China School of Computer Science Chengdu University of Technology Chengdu China

Congenital heart disease is among the most common fetal abnormalities and birth defects. Despite identifying numerous risk factors influencing its onset, a comprehensive understanding of its genesis and management across diverse populations remains limited. Recent advancements in machine learning have demonstrated the potential for leveraging patient data to enable early congenital heart disease detection. Over the past seven years, researchers have proposed various data-driven and algorithmic solutions to address this challenge. This paper presents a systematic review of congential heart disease recognition using machine learning, conducting a meta-analysis of 432 references from leading journals published between 2018 and 2024. A detailed investigation of 74 scholarly works highlights key factors, including databases, algorithms, applications, and solutions. Additionally, the survey outlines reported datasets used by machine learning experts for congenital heart disease recognition. Using a systematic literature review methodology, this study identifies critical challenges and opportunities in applying machine learning to congenital heart disease. © 2025, CC BY-NC-SA.

关键词： Adversarial machine learning

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Transforming Tollbooths: IoT-driven RFID Technology for Traffic Optimization

Transforming Tollbooths: IoT-driven RFID Technology for Traf...

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Anshika Tiwari Seema Malik Aaditya Panda Vaishali Rastogi Computer Science Engineering (Internet of Things) Raj Kumar Goel Institute of Technology Ghaziabad India Dept. of Industrial IOT Vivekananda Institute of Professional Studies-Technical Campus(VIPS-TC) Delhi India Computer Science Engineering (Internet of Thimgs) Raj Kumar Goel Institute of Technology Ghaziabad India Raj Kumar Goel Institute of Technology Ghaziabad India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

This paper proposes a transformative solution for automated toll collection systems by integrating internet of Things (IoT) and blockchain technology. The model aims to address challenges such as security breaches, lack of transparency, and unreliable transactions in current toll systems. Leveraging real-time data from IoT and the decentralised nature of blockchain, the proposed approach enhances security, ensures transparency, and improves overall reliability. The goal is to mitigate fraud risks and provide a secure and user-friendly experience, marking a significant advancement in toll collection systems for modern transportation infrastructure.

关键词： Scalability Transportation Transforms User experience Blockchains Fraud Telecommunication computing internet of Things Security Reliability

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AI-Enhanced Multi-Dimensional Stochastic Process Modeling for QoS Analysis in 6G Networks Under Joint Fading and Dynamic Interference Constraints in $\alpha-\eta-\mu$ Environments

AI-Enhanced Multi-Dimensional Stochastic Process Modeling fo...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Deepak Upadhyay Alankrita Aggarwal Pardeep Singh Deepak Suresh Asudani Nookala Venu Aakash Singh Ranswal Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering University Institute of Engineering Apex Institute of Technology UIE Chandigarh University Mohali India School of Computer Science and Engineering Galgotias University Noida India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

In this paper, we investigate AI-enhanced multidimensional stochastic process modelling for QoS analysis in 6G networks subjected to $\alpha-\eta-\mu$ fading and dynamic interference constraints. Simulation results and heatmaps are utilized for the investigation of fading parameters such as shape factors and interference power, that impacts network performance. Our experimental findings show that AI-driven models provide markedly superior results in improving a number of QoS metrics, including throughput, latency, and packet loss compared to traditional methods. The research illustrates how AI improves connectivity, and operational stability of a network which adapts in times of fluctuating conditions. Our results demonstrate the ability of AI to preserve QoS while balancing performance and power, thereby providing a perspective for 6G network design and optimization.

关键词： Fading channels 6G mobile communication Adaptation models Analytical models Stochastic processes Packet loss Quality of service Throughput Stability analysis Optimization

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Deep Learning and Optical Character Recognition for Digitization of Meter Reading 12

Deep Learning and Optical Character Recognition for Digitiza...

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12th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2022

作者： Chong, Yue Jiet Huat Chua, Kein Babrdel, Mohammad Hau, Lee Cheun Wang, Li Universiti Tunku Abdul Rahman Dept. of Electrical and Electronic Engineering Selangor Malaysia Universiti Tunku Abdul Rahman Dept. of Internet Engineering and Computer Science Selangor Malaysia Universiti Tunku Abdul Rahman Dept. of Mechatronics and BioMedical Engineering Selangor Malaysia National Cheng Kung University Department of Electrical Engineering Tainan Taiwan

ISBN: (纸本)9781665487030

One of the important steps in digital transformation is to make all the instrumental devices connected to the cloud. However, replacing the existing analogue meters with the cloud-connected digital meters can be very costly especially for industrial grade meters. In this project, a deep learning model based on Single Shot Detector (SSD) MobileNet V2 and an optical character recognition (Tesseract OCR) engine are developed for the low-cost digitization of analogue meter readings. The deep learning model is trained with a dataset of 750 meters' images, and it is used to detect the region of interest where the meter's readings are located. The OCR is used to convert the readings to string datatype. The image processing techniques via the OpenCV library are adopted for enhancing the quality of the region of interest (ROI). The model is developed in Python and the evaluation is carried for various types of meters, illumination conditions, and backgrounds. The results show that the deep learning model and OCR accuracies are 95% and 93%, respectively. © 2022 IEEE.

关键词： Optical character recognition

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DistB-VNET: Distributed Cluster-based Blockchain Vehicular Ad-Hoc Networks through SDN-NFV for Smart City

arXiv

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arXiv 2024年

作者： Rahman, Anichur Eidmum, Md Zunead Abedin Kundu, Dipanjali Hossain, Mahir Tashrif, Md Tanjum An Karim, Md Ahsan Islam, Md Jahidul Constituent Institute of Dhaka University Savar Dhaka1350 Bangladesh Dept. of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Computer Science and Engineering Green University of Bangladesh Bangladesh

In the developing topic of smart cities, Vehicular Ad-Hoc Networks (VANETs) are crucial for providing successful interaction between vehicles and infrastructure. This research proposes a distributed Blockchain-based Vehicular Adhoc Network ("DistB-VNET") architecture that includes binary malicious traffic classification, Software Defined Networking (SDN), and Network Function Virtualization (NFV) to ensure safe, scalable, and reliable vehicular networks in smart cities. The suggested framework is the decentralized blockchain for safe data management and SDN-NFV for dynamic network management and resource efficiency and a noble isolation forest algorithm works as an IDS (Intrusion Detection System). Further, "DistB-VNET" offers a dual-layer blockchain system, where a distributed blockchain provides safe communication between vehicles, while a centralized blockchain in the cloud is in charge of data verification and storage. This improves security, scalability, and adaptability, ensuring better traffic management, data security, and privacy in VANETs. Furthermore, the unsupervised isolation forest model achieves a high accuracy of 99.23% for detecting malicious traffic. Additionally, reveals that our method greatly improves network performance, offering decreased latency, increased security, and reduced congestion, an effective alternative for existing smart city infrastructures. © 2024, CC BY-NC-ND.

关键词： Vehicular ad hoc networks

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Distress Study Through Machine-Learning Techniques

Distress Study Through Machine-Learning Techniques

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2021 International Conference on Biomedical Innovations and Applications, BIA 2021

作者： Marinova, Ginka Kalcheva, Neli Technical University of Varna Dept. of Computer Science and Engineering Varna Bulgaria Technical University of Varna Dept. of Software and Internet Technologies Varna Bulgaria

ISBN: (纸本)9781665445818

Machine learning as a contemporary approach in research provides an opportunity to cover and process a large amount of data. This is a condition for the synthesis of reliable characteristic descriptors of distress. The process of model selection is a factor in achieving the necessary adequacy and improving the quality of results. Age is a risky reason to take into account the development of cancer. This paper analyzed classification models for predicting distress in patients with the oncological disease, segmented into three age groups. Experimental results of a study conducted on the age of the patient and assessment of distress, using three methods of machine-learning are presented. © 2022 IEEE.

关键词： Support vector machines

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Integrated Modeling of Wireless System Performance under Beaulieu-Xie Fading and κ-μ Co-Channel Interference: A Multi-Dimensional Exploration

Integrated Modeling of Wireless System Performance under Bea...

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International Symposium on Wireless Personal Multimedia Communications (WPMC)

作者： Deepak Upadhyay Kunj Bihari Sharma Rashmi Sharma Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Department of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798350392319

ISBN: (纸本)9798350356137

This paper proposes simulation and analysis to discuss how the fading affects the wireless signal waves on different fading levels. Four types of fading: (i) no fading, (ii) a low level of fading, (iii) moderate level of fading and (iv), high level of fading was considered for simulating the signal amplitude over time. Selected fading levels were ‘added’ to model real world wireless scenarios in which both environmental and interference conditions are at play. The results indicated that in the absence of fading, the amplitude of the signal wave was continuous and not changed significantly; however, from low to severe fading, there could be directly proportional soaring fluctuations in its proper amplitude corresponding to lose of signal strength. Together, these findings underscore the importance of adaptive signal processing techniques and robust modulation strategies in suppressing the degradative effects of fading against the desirable characteristics of proper signal integrity and reliable performance. The findings highlight one reality in communications: understanding fading is critical for designing ultrareliable wireless systems that perform consistently across a variety of operational settings. The knowledge extracted based on this study would further add to novel way of designing robust strategies for Signal resilience and Communication reliability emerged in future wireless networks.

关键词： Fading channels Wireless communication Fluctuations Wireless networks Modulation Signal processing algorithms Reliability engineering Adaptive signal processing Robustness Signal integrity

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Enhanced Network Experimentation with Machine Learning-Driven Analysis of Beaulieu-Xie Fading and κ-μ Co-Channel Interference Effects

Enhanced Network Experimentation with Machine Learning-Drive...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Raj Ranjan Anil Kumar Pardeep Singh Deepak Suresh Asudani Deepak Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India School of Computing DIT University Dehradun India School of Computer Science and Engineering Galgotias University Noida India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The purpose of this work is to study how to improve large-scale network experiments with machine learning (ML) algorithms, thereby studying the two typical fading models, Beaulieu-Xie model and k-µ co-channel interference. Methods like comparison between the conventional static configuration and ML-based adaptive algorithms were evaluated in terms of traditional performance metrics such as Bit Error Rate (BER), Signal to Noise Ratio (SNR), throughput, and latency. Simulations employing the Beaulieu-Xie model accurately reproduced real-world fading environments in urban, suburban, and rural settings with more fidelity than conventional Rayleigh and Rician models. Our sensitivity analysis of k-µ interference parameters revealed their marked impact on SINR over ranges, thus underlining the importance for careful parameter identification in interference codelength results showed that ML-powered adaptive algorithms always did better than a static configuration in dynamic network situations. These transmission parameters were set in real-time based on online predictions, provided by dynamically adapting algorithms which improved performance network metrics over time. Moreover, sensitivity analysis results pertaining to study parameters further underscored potential improvements in the predictive power and confidence of network simulations via ML models. In summary, the integration of ML for network analysis has great potential in terms of accuracy adaptability and efficiency, so that ML is viewed as a corner stone to drive the design and operation features of wireless networks under complex an evolving environments.

关键词： Fading channels Adaptation models Wireless sensor networks Sensitivity analysis Heuristic algorithms Wireless networks Bit error rate Throughput Real-time systems Signal to noise ratio

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Robust Signal Processing Approaches for Enhancing Communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges 3

Robust Signal Processing Approaches for Enhancing Communicat...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Moray, Rashmy Kumari, Savita Upadhyay, Deepak Sharma, Rashmi Ranswal, Aakash Singh Venu, Nookala Symbiosis International Deemed University India School of Computing Science and Engineering Galgotias University Greater Noida India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Department of Cse Nagpur Campus Pune India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798331542375

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks. © 2025 IEEE.

关键词： Network coding

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Enhancing Cyberbullying Detection in Bengali Language Through Hybrid Ensemble Method: The Code Against Hate Initiative

Enhancing Cyberbullying Detection in Bengali Language Throug...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： MD. Zunead Abedin Eidmum Bakhtiar Muiz Rakib Hossen Anichur Rahman Md. Jahidul Islam Dept. of Internet of Things and Robotics Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Dept. of Cyber Security Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research (NITER) Constituent Institute of Dhaka University Savar Dhaka Green University of Bangladesh Dhaka

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Celebrities and public figures, especially women, face a new challenge every day on social media comments known as cyberbullying, which raises the need for an effective cyberbullying detection system. Detecting cyberbullying on social media is crucial to preventing public harassment. While cyberbullying is an everyday event, it can result in depression and suicide. Timely identification can remove those comments early and help law enforcement take necessary actions in certain cases. However, the investigation process is very hard and time-consuming, especially for the Bengali language, as Bengali is a large and diversified language. Therefore, the main goal of a cyberbullying detection system in the Bengali language is to classify cyberbullying comments accurately. This paper introduces a state-of-the-art hybrid model that uses a unique feature extraction method that combines Bidirectional Encoder Representations from Transformers (BERT) embedding with transformed numerical and categorical features and one machine learning model named Support Vector Machines (SVM) and two deep learning models named multi-head self-attention based Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) with Particle Swarm Optimization (PSO). To improve the performance further, we ensemble those models together. The proposed model with LSTM and Bagging method achieves a high accuracy rate of 99.26%. The proposed ensemble model outperforms the existing works and sets a new standard for Bengali Language cyberbullying detection methods in social media comments.

关键词： Support vector machines Accuracy Cyberbullying Bidirectional control Feature extraction Transformers Encoding Numerical models Long short term memory Bagging

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