Transactions on Emerging Telecommunications Technologies最新文献

{"title":"Advanced Deep Convolution Based Jellyfish VGG-19 Model for Face Emotion Recognition","authors":"P. V. V. S. Srinivas,&nbsp;Gayathri Kota,&nbsp;Bhavitha Kola,&nbsp;Jahnavi Durga Tirumani,&nbsp;Dunti Sarath Sai Chowdary Kantamneni","doi":"10.1002/ett.70176","DOIUrl":"https://doi.org/10.1002/ett.70176","url":null,"abstract":"<div>\u0000 \u0000 <p>For many applications, facial emotion recognition (FER) is an essential yet unsolved procedure. In the past, artificial intelligence methods like convolutional neural networks have typically been used to recognize emotions. However, in terms of complexity and processing time, this method is quite costly. An optimization-based deep convolution network that uses attention-based Densenet-264 for feature extraction is presented in order to address this issue. In the first step, the images are pre-processed using image resizing and equalized joint histogram-based contrast enhancement (Eq-JH-CE) to enhance the image quality. Next, an enhanced attention-based DenseNet-264 architecture is developed for feature extraction, which helps improve classification accuracy. Finally, the extracted features are used by the Advanced Deep Convolutional based Jellyfish VGG-19 model (DeepCon_JVGG-19) for classifying face emotions like angry, disgust, fear, happy, neutral, sad, and surprise. Here, Jellyfish Optimization is used to fine-tune the optimal parameters and increase the performance of the classified model. The Python tool is used for implementation. The JAFFE and FER-2013 are used to test the proposed model performance. The experimental analysis proves the strength of the proposed study by attaining 98.5% accuracy.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Innovative Video Anomaly Detection: TCN-AnoDetect With Self-Supervised Feature Learning”","authors":"","doi":"10.1002/ett.70179","DOIUrl":"https://doi.org/10.1002/ett.70179","url":null,"abstract":"<p>R. Chiranjeevi and D. Malathi, “Innovative Video Anomaly Detection: TCN-AnoDetect With Self-Supervised Feature Learning,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70045, https://doi.org/10.1002/ett.70045.</p><p>The affiliation of the authors was incomplete. This should have read: “Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India.”</p><p>The online version of the article has also been updated.</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “A Secure Multi-Model Biometrics Using Deep Learning Model Based-Optimal Hybrid Pattern by the Heuristic Approach”","authors":"","doi":"10.1002/ett.70181","DOIUrl":"https://doi.org/10.1002/ett.70181","url":null,"abstract":"<p>S. Juluri and M. Gudavalli, “A Secure Multi-Model Biometrics Using Deep Learning Model Based-Optimal Hybrid Pattern by the Heuristic Approach,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70106, https://doi.org/10.1002/ett.70106.</p><p>In Section 3.3.2.1, the header “Cuckoo Optimization Algorithm” was incorrect. This should have read: “Cheetah Optimization Algorithm”</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144197184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “The Role of ChatGPT in Reducing Storage, Energy, and Scalability Overheads in Blockchain-Based Healthcare Systems”","authors":"","doi":"10.1002/ett.70180","DOIUrl":"https://doi.org/10.1002/ett.70180","url":null,"abstract":"<p>N. Almusallam and M. Hasnain, “The Role of ChatGPT in Reducing Storage, Energy, and Scalability Overheads in Blockchain-Based Healthcare Systems,” <i>Transactions on Emerging Telecommunications Technologies</i> 36 (2025): e70151. https://doi.org/10.1002/ett.70151.</p><p>The funding information has been updated. The complete funding statement is below:</p><p>“This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant No. KFU251233].”</p><p>We apologize for this error.</p>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ett.70180","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV Relay-Enabled THz Covert Communications Against Colluding Detection","authors":"Xinzhe Pi,&nbsp;Bin Yang,&nbsp;Yulong Shen,&nbsp;Xiaohong Jiang","doi":"10.1002/ett.70165","DOIUrl":"https://doi.org/10.1002/ett.70165","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper investigates covert communications against colluding unmanned aerial vehicle (UAV) wardens in a UAV-assisted Terahertz (THz) relay system (UTRS), where a transmitter sends confidential signals to its intended receiver via a UAV relay, and two wardens attempt to detect the existence of two-hop transmissions. Considering a powerful adversary with colluding wardens, we examine the decision fusion (DF) and centralized testing (CT) schemes to characterize the colluding detection capabilities. We derive each warden's detection error probability (DEP) under Rician fading with atmospheric attenuation and establish the overall DEPs of two colluding schemes. We then formulate and solve a covert throughput maximization problem by jointly optimizing the transmission power and interference power, subject to the covertness and SINR constraints. Numerical results validate the theoretical analyses and illustrate the influence of key parameters on covert performance. In particular, the covert throughput against wardens under the DF scheme is lower than that under the CT scheme. This work sheds light on the covert communication design of UTRSs against colluding wardens in next-generation wireless communication networks.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144191087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Classification and Combined Transform Based Feature Extraction Approach for Mental Stress Prediction of Human Beings Using EEG","authors":"Shashibala Agarwal,&nbsp;Maria Jamal,&nbsp;Parmod Kumar","doi":"10.1002/ett.70155","DOIUrl":"https://doi.org/10.1002/ett.70155","url":null,"abstract":"<div>\u0000 \u0000 <p>Stress is a psychological condition in which a person feels overwhelmed with pressure. Early identification of psychological stress is critical for preventing illness progression and saving lives. Electroencephalography (EEG) is often used to collect psychological information such as brain rhythms in the form of electric waves. Traditional deep learning techniques face limitations like temporal dynamics and feature extraction issues. To address these shortcomings, a deep learning-based classification model was created, combining advanced transform-based feature extraction techniques to more effectively predict mental stress by using EEG signals. The process begins by utilizing physiological parameters extracted from the EEG Psychiatric Disorders Dataset. The raw EEG signals undergo pre-processing to enhance their quality, which includes smoothing, alignment, and addressing non-uniform sampling issues. The signals are then decomposed and their components extracted using the Adaptive Flexible Analytic Wavelet Transform (AFAWT). Short-Term Fourier Transform-Randon Transform (STFT-RT) approach is used to extract the key features of signals. Feature selection is optimized using the Young's Double Slit Experiment Optimizer (YDSE) to ensure only the most relevant features are chosen for classification. Finally, these selected features are fed into the Parallel Neural Networks with Extreme Efficiency (ParNeXt v1-DB) model, which utilizes a drop block mechanism to enhance model generalization and prevent overfitting, ensuring highly effective mental stress prediction. According to simulated research, the proposed approach demonstrated significant improvements over existing algorithms. In Dataset 1, the method achieved an accuracy of 97.8%, selectivity of 95.4%, while Dataset 2 recorded an accuracy of 96.3%, PPV of 93.8%. Thus, the proposed method is the most effective method for predicting human mental stress using EEG.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Protected Framework Employing Flexible and Optimum Arrangement in Cloud Computing","authors":"S. Shahul Hammed,&nbsp;S. Pavalarajan,&nbsp;C. Preethi,&nbsp;K. Haripriya","doi":"10.1002/ett.70167","DOIUrl":"https://doi.org/10.1002/ett.70167","url":null,"abstract":"<div>\u0000 \u0000 <p>The idea of cloud computing (CC) originates from making resources available for task execution. Cloud computing is an advancement of supercomputing. The main challenges in CC are the varying resources and workloads, leading to the need for efficient tasking and scheduling. Distributed task scheduling can help us better understand workflow scheduling; autonomous task scheduling that accounts for security and execution time, mutual trust among system participants, better energy efficacy, and system utilization, among other aspects. The MBABE technique expanded as multi-level blockchain attribute-based encryption, which is used to ensure data security. ABE is a combined encryption method that can be effectively utilized for security and access control. Additionally, a new algorithm is presented, which is optimized with a convolutional neural network and snoop slingshot spider optimization (CNN-S3SO). The cost functions are minimized using this S3SO for multipleusers in multiple cloud tasks computing progress. The technique relies on the actions of the arachnid in capturing targets and is utilized to arrange tasks for optimal throughput and minimal makespan. It is additionally recommended as a means of achieving convergence in a brief time frame. In addition, a protocol reliant on blockchain technology is utilized to encode data, ensuring secure transmission. Ultimately, the method is tested through a cloudlet simulator, and its efficiency is assessed through the analysis of the results. The outcome of the resource utilization rate is around 98%. It demonstrates that this methodology outperforms other task scheduling methods.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Weight Allocation–Based Network Security and Anomaly Detection Model for Intelligent VANETs","authors":"Aadam Quraishi,&nbsp;Rakeshnag Dasari,&nbsp;Sushilkumar Dangiya,&nbsp;Sateesh Kumar Nallamala,&nbsp;Krishna Kanth Kondapaka,&nbsp;Swaroop Reddy Gayam,&nbsp;Isa Bayhan,&nbsp;Uguloy Berdieva,&nbsp;Rubal Jeet","doi":"10.1002/ett.70174","DOIUrl":"https://doi.org/10.1002/ett.70174","url":null,"abstract":"<div>\u0000 \u0000 <p>Determining the weights of evaluation metrics is one of the key factors influencing the cybersecurity and anomaly detection of intelligent vehicular ad hoc networks (VANETs). To address the limitations of traditional weighting methods, which often overlook the impact of changes in metric attribute states on evaluation weights, this paper proposes a dynamic weight allocation–based network security and anomaly detection model. The model begins by decomposing and analyzing the security and anomaly detection objectives of VANETs, constructing a comprehensive evaluation metric system. The network security assessment model for VANETs presented in this research overcomes the drawbacks of conventional static models by utilizing a dynamic weight allocation technique. Based on current network conditions, a state variable weight method was created that dynamically computes security values by combining incentive and penalty mechanisms. A ranking-based weighting algorithm is employed to analyze the correlation between security and anomaly detection metrics. Subsequently, the proposed dynamic weight allocation algorithm calculates the dynamic weights of individual metrics within the system, enabling a robust assessment of network security and anomaly detection for intelligent VANETs. The evaluation results provide security level classifications and identify anomalies effectively. Experimental results demonstrate that the model significantly enhances the rationality and accuracy of intelligent VANET evaluations, contributing to improved cybersecurity and anomaly detection.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144171434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain Based Delay-Tolerant Resource Optimization in Fog and Cloud Layers Utilizing NNGOA and LS2BiOLSTM","authors":"Guman Singh Chauhan,&nbsp;Kannan Srinivasan,&nbsp;Rahul Jadon,&nbsp;Rajababu Budda,&nbsp;Venkata Surya Teja Gollapalli,&nbsp;Joseph Bamidele Awotunde","doi":"10.1002/ett.70178","DOIUrl":"https://doi.org/10.1002/ett.70178","url":null,"abstract":"<div>\u0000 \u0000 <p>Resource Optimization (RO) in fog and cloud layers enhances performance, minimizes costs, and ensures seamless integration of distributed systems. However, prevailing works failed to perform resource optimization in both fog and cloud layers due to their complex and disparate architectures. Therefore, the proposed work performs resource optimization efficiently in both fog and cloud layers by predicting the network traffic congestion using Neuron Northern Goshawk Optimization Algorithm (NNGOA) and Log Sigmoid Softplus Bidirectional Orthogonal Long Short-Term Memory (LS²BiOLSTM). At first, the Cloud Users are registered and logged in for task assignments. Meanwhile, the Smart Contract (SC) based Service Level Management (SLM) is created for tasks. After that, the signature is created for SLA and is verified during task assignment. For predicting the network traffic congestion in tasks, LS²BiOLSTM is utilized. Then, the predicted congestion tasks are clustered and mapped into a fog layer. Simultaneously, from the Cloud Server (CS), the data center is prioritized using SoftSign Bell-Fuzzy (SSB-Fuzzy). Finally, the resources are optimized efficiently with a high accuracy of 98.1259% using NNGOA, which outperforms the existing methodologies.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-Enabled Blockchain Framework for Internet of Vehicles Safety Monitoring in Smart Cities","authors":"Amrendra Singh Yadav,&nbsp;Vijayant Pawar,&nbsp;Roshni Yadav","doi":"10.1002/ett.70169","DOIUrl":"https://doi.org/10.1002/ett.70169","url":null,"abstract":"<div>\u0000 \u0000 <p>Road accidents pose a significant concern, leading to numerous fatalities and societal disruptions. The Internet of Vehicles (IoVs), when integrated with communication technology, offers a promising solution to mitigate these accidents. However, current IoV systems face challenges such as data integrity, user privacy, centralized storage, and secure authentication. Blockchain technology emerges as a viable solution, providing tamper-proof data storage and a trustless authentication independent of central authorities. However, in blockchain-based IoV systems, the inefficiency of traditional consensus mechanisms suffers from high computational costs, network delays, and limited scalability. To address these challenges, we introduce HybridChain, an IoT-enabled blockchain framework that uses the Reputation-Based Practical Byzantine Fault Tolerance (RB-PBFT) consensus mechanism, which enhances transaction throughput, reduces consensus delay, and mitigates block congestion by incorporating a reputation-based trust model. RB-PBFT ensures that only trusted entities participate in block validation. Furthermore, HybridChain integrates a sidechain-based storage mechanism to manage the large volume of data, ensuring that only essential metadata is recorded on the main blockchain, thereby enhancing scalability and reducing network congestion. The experiment results show that the amount of data transmitted is 4.3 times more in PoW than RB-PBFT with varying vehicles, while in the case of varying block size, it is 3.1 times more in PoW than RB-PBFT.</p>\u0000 </div>","PeriodicalId":23282,"journal":{"name":"Transactions on Emerging Telecommunications Technologies","volume":"36 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144140471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}