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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Hasan, Nowshad Sharif, Md. Maskat Islam, Md Saiful Chowdhury, Arafat Anam Rahman, Miskatur Khan, Md Rokibul Fahad Bahar, Anamul Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chittagong Bangladesh North South University Department of Computer Science Enginnering Dhaka Bangladesh International Islamic University Chittagong Department of Electronics and Telecommunication Engineering Chittagong Bangladesh University of London Department of Computer Science Enginnering London United Kingdom

ISBN: (纸本)9798331534400

Lung cancer is one of the leading causes of cancer-related mortality worldwide. Early detection of lung cancer is crucial for improving survival rates and enabling more effective treatment options. However, manually detecting and classifying various forms of lung cancers is time-consuming and can be inaccurate. Traditional classification methods rely on manual feature extraction, necessitating the experimenter's expertise and professional knowledge. This research introduces a streamlined feature selection method using a deep convolutional neural network (CNN) to effectively tackle the issue. The aim of this proposed lightweight deep CNN is to enhance accuracy while reducing the number of parameters. The established approach involves three key stages: pre-processing, feature extraction, and classification. In the pre-processing phase, K-fold cross-validation ensures robust classification, complemented by data augmentation to augment the image dataset. In the feature extraction stage, modified CNNs effectively extract pertinent features, followed by Random Forest (RF) utilization to distinguish various forms of lung cancer from CT scan images. The proposed Deep CNN-RF model demonstrates effective detection and classification of various types of lung cancer, achieving an impressive accuracy rate of 97.1%. © 2024 IEEE.

关键词： Lung cancer

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A Whispering Widgets and Wireless Wonders Pioneering Change in the World of IoT 7th

A Whispering Widgets and Wireless Wonders Pioneering Change ...

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7th International Conference on Communication, Device and Networking, ICCDN 2024

作者： Tripathi, Amit Kumar Islam, Baharul Murugan, R. Adhvaryu, Rachit Department of Electronical and Electronics Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Engineering Assam Down Town University Assam Guwahati India Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science and Engineering Parul Institute of Engineering and Technology Parul University Gujarat Vadodara India

ISBN: (纸本)9789819753369

The IoT has made it possible to connect and control more devices than ever before, opening up new avenues for efficiency and creativity. However, new problems have emerged as a result of this explosion of linked devices, notably concerning the security and privacy of personal information. To meet these difficulties, we provide a new approach within the context of "Whispering Widgets and Wireless Wonders: Ushering in a New Era for the Internet of Things." Our suggested approach safeguards data during transit and storage by using state-of-the-art cryptographic methods including Elliptic Curve Cryptography (ECC) and the Advanced Encryption Standard (AES). Furthermore, we use blockchain technology to guarantee the immutability and transparency of data by creating a public and unchangeable ledger of all data exchanges. Our research shows that our suggested strategy for securing IoT data is superior to more conventional methods. We test how well our system protects sensitive information, keeps data intact, keeps users anonymous, and how efficient it is overall. When compared to conventional approaches, the "Proposed Method" regularly performs better, demonstrating its strength and applicability for protecting IoT data. High security is guaranteed by the combination of ECC and AES, and data integrity is improved by blockchain technology. Furthermore, our approach places a premium on user privacy, giving people more say over their data inside the IoT ecosystem. Finally, we believe that our suggested solution is a major advancement in achieving more data security and privacy in the IoT context. To the problems caused by the proliferation of linked gadgets, it provides a thorough and effective answer. Our approach prioritizes security, data integrity, and user privacy to let people and businesses get the benefits of the Internet of Things (IoT) without compromising on data privacy or security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025

关键词： Data integrity

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Predictions of Basketball Match Results using Autoencoder with Gated Recurrent Unit Algorithm 1

Predictions of Basketball Match Results using Autoencoder wi...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Kavitha, K.R. Almusawi, Muntather Vybhavi, Gummagatta Yajaman Edwin Prabhakar, P.B. Saranya, N. Naga Sona College of Technology Department of Electronics and Communication Engineering Salem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Computer Science and Engineering Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India

ISBN: (纸本)9798350352931

This research explores basketball match predictions within the field of sports analytics utilized Deep Learning (DL) technique to analyse the outcomes of National Basketball Association (NBA) games based on player performance and team statistics. The challenge in predicting NBA games outcomes and performance analysis various factor, as the high dimensionality of the data and uncertainty surrounding player due to difficult to predictions of basketball game. This paper, proposed Autoencoder with Gated Recurrent Unit (AE-GRU) algorithm to efficiently analyse and predict NBA game outcomes. The data was initially collected from NBA games between 2009 - 2021, focusing on five teams and pre-processing involved min-max normalization to uniformly scale the feature vectors. The Principal Component Analysis (PCA) utilized for feature selection, effectively reducing the high dimensionality of the NBA game data across various teams. The AE-GRU technique then captured the complex feature of basketball game data and handled uncertainty data and enhances performance. The performance of DL model evaluated the various metrics such as accuracy, precision, recall and f1-score metrics on NBA game. © 2024 IEEE.

关键词： Deep learning

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Cutting-Edge Climate Analysis: Combining MLP-GRU and Remote Sensing Technologies

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 348-363页

作者： Shaik, Reddi Khasim Priya, S. Shanmuga Saranya, N. R, Kotteeswaran Ramya, S. Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India SRM Institute of Science and Technology Tiruchirappalli India Department of EIE St. Joseph’s College of Engineering Chennai India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai Erode India Department of Information Technology Prathyusha Engineering College Chennai India

Climate change poses significant challenges to societies worldwide, necessitating accurate and reliable climate prediction models to inform mitigation and adaptation strategies. The ability to forecast climate variables such as temperature, precipitation, and soil moisture is crucial for a wide range of applications, including agriculture, water resource management, and disaster preparedness. Traditional models have often struggled to capture the complex, nonlinear relationships and temporal dependencies inherent in climate data. In this study, we present a novel Hybrid MLP-GRU Network for climate prediction, leveraging the combined strengths of Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models. This hybrid approach aims to capture both complex, nonlinear relationships and temporal dependencies in climate data, enhancing the accuracy and reliability of predictions. We utilized comprehensive datasets from the Copernicus Climate Change Service (C3S), focusing on essential variables such as temperature, precipitation, soil moisture, and vegetation indices. The data underwent rigorous preprocessing, including normalization, temporal alignment, and feature extraction, to ensure high-quality inputs for the model. The model was trained and validated on extensive climate datasets, demonstrating superior performance metrics with an accuracy of 97.77%, RMSE of 0.140, and MAE of 0.120 on validation data. Comparative analysis with other models, including Linear Regression, Random Forest, LSTM, and CNN, highlights the efficacy of our hybrid approach. The Hybrid MLP-GRU Network consistently outperformed these models in terms of RMSE, MAE, and accuracy. Additionally, hyperparameter tuning, cross-validation, and deployment assessments were conducted to optimize the model’s performance and ensure robust predictions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate data Climate prediction Data preprocessing Feature extraction Gated Recurrent Unit Hybrid MLP-GRU Network Machine learning Multi-Layer Perceptron Nonlinear relationships Temporal dependencies

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An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

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computer Systems Science & engineering 2023年第11期47卷 2097-2118页

作者： Athena George Bejoy Abraham Neetha George Linu Shine Sivakumar Ramachandran Department of Electronics and Communication Engineering College of EngineeringTrivandrum695016India Department of Computer Science and Engineering College of Engineering MuttatharaTrivandrum695008India Department of Electronics and Communication Rajeev Gandhi Institute of TechnologyKottayam686501India Department of Electronics and Communication Engineering Government Engineering CollegeWayanad670644India

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous *** disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive *** diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the *** resonance imaging(MRI)is widely used by neurologists for diagnosing brain *** majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease *** might result in losing the volumetric information obtained from the whole brain *** address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)*** 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the *** pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate *** present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD *** proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two *** findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classi

关键词： 3D CNN alzheimer’s disease attention mechanism classification

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Curvelet Transform Based on Edge Preserving Filter for Retinal Blood Vessel Segmentation

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computers, Materials & Continua 2022年第5期71卷 2459-2476页

作者： Sonali Dash Sahil Verma Kavita N.Z.Jhanjhi Mehedi Masud Mohammed Baz Department of Electronics and Communication Engineering Raghu Institute of Technology(A)Visakhapatnam531162India Department of Computer Science and Engineering Chandigarh UniversityMohali140413India School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityTaif21994Saudi Arabia

Segmentation of vessel in retinal fundus images is a primary step for the clinical identification for specific eye *** diagnosis of vascular pathologies from angiographic images is thus a vital aspect and generally depends on segmentation of vascular *** various approaches for retinal vessel segmentation are extensively utilized,however,the responses are lower at vessel’s *** curvelet transform signifies edges better than wavelets,and hence convenient for multiscale edge *** bilateral filter is a nonlinear filter that is capable of providing effective smoothing while preserving strong *** bilateral filter is an advanced version of bilateral filter that regulates the contrast while preserving the ***,in this paper a fusion algorithm is recommended by fusing fast bilateral filter that can effectively preserve the edge details and curvelet transform that has better capability to detect the edge direction feature and better investigation and tracking of significant characteristics of the *** C mean thresholding is used for the extraction of *** recommended fusion approach is assessed on DRIVE *** results illustrate that the fusion algorithm preserved the advantages of the both and provides better *** results demonstrate that the recommended method outperforms the traditional approaches.

关键词： Blood vessel extraction curvelet transform fast bilateral filter C mean thresholding

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A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intelligence and Anomaly Detection 2

A Hybrid LSTM-GAN Model for Predictive Cyber Threat Intellig...

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

作者： Sankara, Khadija Danladi Saritha, G. Anitha Elavarasi, S. Saradha, S. Arul Kumar, D. Skyline University Nigeria Kano State Nigeria Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sona College of Technology Salem India Erode India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331504960

Cybersecurity is increasingly challenged by complex and evolving threats that require predictive intelligence and advanced anomaly detection. This paper presents a novel Hybrid LSTM-GAN (Long Short-Term Memory - Generative Adversarial Network) model designed for predictive cyber threat intelligence and real-time anomaly detection. By leveraging LSTM’s ability to capture temporal dependencies in sequential data and GAN’s strength in generating synthetic samples, this hybrid model enhances the detection of subtle and emerging cyber threats. The model first uses LSTM layers to analyze patterns within time-series data, identifying potential anomalies. A GAN is then employed to generate realistic threat scenarios, enhancing the model's ability to recognize unusual activity and improve generalization. This two-step approach is crucial for capturing complex patterns and reducing false positives, which are common in conventional detection methods. Experimental results on benchmark cybersecurity datasets demonstrate that the proposed model achieves a 92.5% detection accuracy, a 15% reduction in false positives compared to standalone LSTM models, and a 25% improvement in threat prediction capabilities over traditional methods. These values indicate that the Hybrid LSTM-GAN model is a powerful tool for predictive threat intelligence, providing proactive and reliable anomaly detection across various cybersecurity applications. © 2024 IEEE.

关键词： Cyber attacks

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Heart Disease Prediction Evaluation of Machine Learning Models with PSO-Optimized K-Fold Cross-Validation

Heart Disease Prediction Evaluation of Machine Learning Mode...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Gupta, Nikita Jadon, Kuldeep Singh Soni, Preeti Gupta, Nitin Dhurandher, Sanjay Kumar Department of Computer Science & Engineering India Netaji Subhas University of Technology Department of Information Technology New Delhi India National Institute of Electronics and Information Technology New Delhi India

ISBN: (纸本)9798350364866

Heart disease prediction is a critical aspect of modern healthcare and demands accurate and efficient models for timely diagnosis. Machine learning models in heart disease prediction analyze patient data, with cross-validation ensuring reliable performance, aiding model selection, and optimizing parameters for enhanced accuracy. The k value in cross-validation determines the number of folds into which the dataset is divided for training and validation. In the proposed work, optimization of the k-value in cross-validation is done using Particle Swarm Optimization (PSO), which is utilized to improve the evaluation of the predictive accuracy of various models. PSO dynamically adjusts the k-value, balancing computational efficiency with accurate model evaluation. The result shows that among various ML models, Random Forest, with an 86.95% mean accuracy, emerges as the preferred choice, offering reliable predictions for improved diagnostics. © 2024 IEEE.

关键词： Prediction models

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Fusion of Region Extraction and Cross-Entropy SVM Models for Wheat Rust Diseases Classification

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computers, Materials & Continua 2023年第11期77卷 2097-2121页

作者： Deepak Kumar Vinay Kukreja Ayush Dogra Bhawna Goyal Talal Taha Ali Department of Computer Science&Engineering Chitkara University Institute of Engineering and TechnologyChitkara UniversityPunjab140401India Department of Electronics&Communication Engineering Chandigarh UniversityPunjab140413India Department of Dental Industry Techniques Al-Noor University CollegeNineveh41018Iraq

Wheat rust diseases are one of the major types of fungal diseases that cause substantial yield quality losses of 15%–20%every *** wheat rust diseases are identified either through experienced evaluators or computerassisted *** experienced evaluators take time to identify the disease which is highly laborious and too *** wheat rust diseases are predicted at the development stages,then fungicides are sprayed earlier which helps to increase wheat yield *** solve the experienced evaluator issues,a combined region extraction and cross-entropy support vector machine(CE-SVM)model is proposed for wheat rust disease *** the proposed system,a total of 2300 secondary source images were augmented through flipping,cropping,and rotation *** augmented images are preprocessed by histogram *** a result,preprocessed images have been applied to region extraction convolutional neural networks(RCNN);Fast-RCNN,Faster-RCNN,and Mask-RCNN models for wheat plant patch *** layers of region extraction models construct a feature vector that is later passed to the CE-SVM *** a result,the Gaussian kernel function in CE-SVM achieves high F1-score(88.43%)and accuracy(93.60%)for wheat stripe rust disease classification.

关键词： Wheat rust diseases agricultural region extraction models intercropping image processing feature extraction precision agriculture

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A comprehensive survey on 6G-security: physical connection and service layers

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Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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