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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Hamdani, Mostefa Sahnoune, Ahmed Merzougui, Mokhtar Douara, Abdelmalek Khatemi, Fouad University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Mathematics and Computer Science Algeria University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Science and Technology Algeria

ISBN: (纸本)9798331527396

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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A Hybrid Model for Improving Software Cost Estimation in Global Software Development

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computers, Materials & Continua 2024年第1期78卷 1399-1422页

作者： Mehmood Ahmed Noraini B.Ibrahim Wasif Nisar Adeel Ahmed Muhammad Junaid Emmanuel Soriano Flores Divya Anand Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit RajaBatu Pahat86400Malaysia Department of Computer Science COMSATS University Wah Cantt CampusIslamabad47010Pakistan Department of Information Technology The University of HaripurKhyber Pakhtunkhwa22620Pakistan Engineering Research Innovation Group Universidad Europea del AtlanticoSantander39011Spain

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert *** models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate *** addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation ***,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost *** study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these *** proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry *** article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost *** the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing *** findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

关键词： Artificial neural networks COCOMO II cost drivers global software development linear regression software cost estimation

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An ensemble approach for classification of diabetic retinopathy in fundus image

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Multimedia Tools and Applications 2025年第12期84卷 10567-10586页

作者： Prakash, J. Vinoth kumar, B. Department of Computer Science & Engineering PSG College of Technology Tamil Nadu Coimbatore India Department of Information Technology PSG College of Technology Tamil Nadu Coimbatore India

Diabetes-oriented diabetic retinopathy impacts the blood vessels in the region of the retina to enlarge and leak blood and other fluids. In most cases, diabetic retinopathy affects both eyes. If left untreated, it would eventually lead to blindness. Diabetic Retinopathy is so widespread that around 93 million adults are affected by it on a worldwide scale. Early identification and treatment would reduce the danger of serious diabetic retinopathy visual impairment. To achieve early detection, we can make use of our advancement in image processing to diagnose how severely the patient is affected by diabetic retinopathy. Using a patient’s fundus image and modern deep learning methods, we will be able to classify the result into five categories of severity: unaffected, severe, mild, moderate, and proliferate. Based on the results, the doctor would be able to determine what type of treatment is required to further avoid vision loss. The strategy outlined in this work is to employ five well-known deep learning models, namely Inception ResNet V2, VGG19, Xception, ResNet50, DenseNet112, and integrate their outcomes successfully utilizing ensemble learning. The experimentation outcome displays that the proposed approach classifies all the classes of Diabetic Retinopathy and performs better compared to other methods with a Specificity of 92.02%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood vessels

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with Engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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A Novel Approach to Enhanced Cancelable Multi-Biometrics Personal Identification Based on Incremental Deep Learning

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computers, Materials & Continua 2025年第5期83卷 1727-1752页

作者： Ali Batouche Souham Meshoul Hadil Shaiba Mohamed Batouche Department of Computer and Information Sciences Northumbria UniversityLondonE17HTUK Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these *** of the key developments is the integration of deep learning techniques in biometric ***,despite these advancements,certain challenges *** of the most significant challenges is scalability over growing *** methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and *** challenge underscores the need for more efficient methods to scale *** this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning *** work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic *** leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random *** approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.

关键词： Incremental learning personal identification cancelablemulti-biometrics pattern recognition security deep learning cyber-attacks transfer learning random projection catastrophic forgetting

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in Engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Mango Disease Detection Using Fused Vision Transformer with ConvNeXt Architecture

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computers, Materials & Continua 2025年第4期83卷 1023-1039页

作者： Faten S.Alamri Tariq Sadad Ahmed S.Almasoud Raja Atif Aurangzeb Amjad Khan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan AIDA Lab Department of Information SystemsCollege of Computer&Information SciencesPrince Sultan UniversityRiyadh12435Saudi Arabia Department of Computer Science International Islamic UniversityIslamabad44000Pakistan

Mango farming significantly contributes to the economy,particularly in developing ***,mango trees are susceptible to various diseases caused by fungi,viruses,and bacteria,and diagnosing these diseases at an early stage is crucial to prevent their spread,which can lead to substantial *** development of deep learning models for detecting crop diseases is an active area of research in smart *** study focuses on mango plant diseases and employs the ConvNeXt and Vision Transformer(ViT)*** datasets were *** first,MangoLeafBD,contains data for mango leaf diseases such as anthracnose,bacterial canker,gall midge,and powdery *** second,SenMangoFruitDDS,includes data for mango fruit diseases such as Alternaria,Anthracnose,Black Mould Rot,Healthy,and Stem and *** datasets were obtained from publicly available *** proposed model achieved an accuracy of 99.87%on the MangoLeafBD dataset and 98.40%on the MangoFruitDDS *** results demonstrate that ConvNeXt and ViT models can effectively diagnose mango diseases,enabling farmers to identify these conditions more *** system contributes to increased mango production and minimizes economic losses by reducing the time and effort needed for manual ***,the proposed system is integrated into a mobile application that utilizes the model as a backend to detect mango diseases instantly.

关键词： ConvNeXt model fusion mango disease smart agriculture vision transformer

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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Multiple-Person Tracking with Criss-Cross Attention and Deep Matching 8

Multiple-Person Tracking with Criss-Cross Attention and Deep...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Liu, Zhuoyang Zhang, Liming University of Macau Faculty of Science and Technology Department of Computer and Information Science Macau China

ISBN: (纸本)9798350397932

Multi-pedestrian tracking is one of the important application fields of computer vision, among which the real-time tracking of pedestrians and the ability to solve occlusion problems are two big challenges to improve the recognition accuracy. In this paper, we propose a new standardized tracking-by-detection framework that can effectively alleviate the above two problems. We first introduce the Criss-Cross Attention (CCA) mechanism in the detector. The CCA module can reduce the computational complexity of the detector and improve the detection accuracy. Then a pedestrian deep feature extractor fused with cosine metric learning for two-layer convolution is added to further improve the tracking accuracy and real-time performance. Finally, Kalman Filter prediction combined with pedestrian deep features effectively reduces identity switching under the occlusion. We conduct experiments on the MOT16 datasets and evaluate on HOTA. Experimental results demonstrate the competitiveness of our method against some selected state-of-the-art methods. © 2023 IEEE.

关键词： computer vision

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