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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Poornimathi, K. Deepak Kumar, A. Srinithi, A. Rajalakshmi Engineering College Department of Computer Science and Business Systems India

ISBN: (纸本)9798350353068

Revolutionizing the urban commuting by introducing a ground-breaking AI-powered carpooling portal. This comprehensive platform provides an ultimate solution combining all the latest technologies for transportation challenges. The system ensures effortless scalability and real-time synchronization of user profiles, tip histories and route information across devices. Using the integration of emerging technologies such as Artificial intelligence and Machine learning algorithms, commuters are given a unique advantage of having a seamless travel and an optimized cost for the travel using AI and ML models and they are matched based on their preferences, schedules and historical data that makes the travel more convenient. The system also provides enhanced route security by analyzing the geospatial information to suggest the safest and most efficient routes for carpooling groups. It also streamlines the billing process by introducing Robotic process automation (RPA), ensuring accurate and tamper-free billing process for the carpooling groups. This combination of different technologies makes carpooling into a sophisticated, seamless and efficient mode of sustainable transportation that also is secure while delivering unparalleled convenience and peace of mind to its users. © 2024 IEEE.

关键词： Robotics

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Revitalizing Paddy Yields with computer Vision 2

Revitalizing Paddy Yields with Computer Vision

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Siva M.e, K.P. Vibin, K.C. Kaarnika, A. Ramkumar, T.R. Priyanka, D. SriEshwarCollege of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350327533

A groundbreaking innovation in the field of plant science revolutionizes the precise identification of nutrient deficiencies in paddy plants through meticulous leaf analysis. The methodology commences by breaking down input leaf images into manageable blocks, subsequently subjecting them to rigorous evaluation using specialized convolutional neural networks (CNNs), each meticulously trained to discern specific nutrient deficiencies. The advanced network architecture systematically scrutinizes these blocks for characteristic deficiency symptoms. To achieve an all-encompassing evaluation of the leaf's condition, the responses generated by all CNNs are aggregated, and a multi-layer perceptron synthesizes them into a singular block response. The proposed approach results in a comprehensive assessment of the overall leaf health. In a comprehensive study encompassing seven different deficiency types - including Nitrogen (N), Phosphorus (P), Potassium (K), and Zinc (Zn) deficiencies - a vast dataset comprising 2,750 carefully collected leaf images was employed. The results demonstrate the proposed method's superiority, surpassing the accuracy of expert human assessments in identifying various nutrient deficiencies. The proposed method harnesses the power of CNNs, effectively merging the image analysis, machine learning, and Convolutional Neural Networks (CNNs) to propel nutrient deficiency analysis in paddy plants to unprecedented heights. © 2024 IEEE.

关键词： Convolutional Neural Networks Image Segmentation Leaf Analysis Machine Learning Nutrient Deficiency

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Deep neural network for emojis-based sentiment classification

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Balaji, Prasanalakshmi Alsid, Linda Elzubir Gasm Sagar, Kalpna Prakasam, Shanmugapriya Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science KIET Group of Institutions Delhi-NCR Ghaziabad India Department of Computer Science and Business Systems Rajalakshmi Engineering College Thandalam India

Sentiment classification using emojis on social media has become increasingly crucial in recent years. Social media commonly uses emojis to convey feelings, emotions, and moods. Hence, in this article, a Jellyfish Algorithm based on a Deep Convolution Neural Network (JA-DCNN) is developed for the sentiment classification of the emojis. Initially, the emoji images are collected from the open-source system. The proposed sentiment classification process uses a Jellyfish Algorithm (JA) optimized Deep Convolution Neural Network (DCNN). In DCNN, the hyperparameters are selected with the help of JA to improve accuracy and efficiency. The proposed method aims to improve the training features, maintain the resolution, reduce memory consumption, and improve the system’s accuracy. The proposed method decreases the computation cost and memory consumption, which empowers the system’s performance in emojis-based sentiment classification. The proposed method is implemented in MATLAB, and performance metrics of similarity and quality measurements evaluate performances. To validate the performance of the proposed methodology, it is compared with conventional methods such as MobilenetV2, ResNet18, and ResNet50, which have been proven to obtain better performance metrics. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Classification Deep convolution neural network Emojis Jellyfish algorithm Sentiment analysis

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Blockchain based Single Path and Multi-Path Routing in Payment Channel Network using Artificial Intelligence Techniques 2

Blockchain based Single Path and Multi-Path Routing in Payme...

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

作者： Swathi, Pothurajula Rao, Kasukurthi Venkata Department of Computer Science & Systems Engineering Andhra University College of Engineering Visakhapatnam India

ISBN: (纸本)9798331504960

Blockchain-based Payment Channel Network (PCN) routing for payment transactions includes the transfer of funds between multiple parties through a network of interconnected payment channels. These channels permit off-chain transactions, balance payment channels, enhance speed, and reduce the transaction costs. However, finding the best route for transactions between parties is challenging due to the dynamic nature of the network. The routing concept for PCN is divided as single-path routing and multi-path routing to enhance transactions based on the requirements of the users/organizations. Channel-based strategy, off-chain transactions, channel rebalancing techniques, and payment execution methods are considered essential in PCN transactions for an efficient routing. This study analyses the following methodologies: Deep Neural Network (DNN), Deep Reinforcement Learning (DRL), Graph NN (GNN), as well as genetic optimization algorithm utilized for efficient routing. Currently, distance-based methods like Markov chain-based routing are also being employed in PCN for finding an optimal path for payments. This survey examines the strengths and challenges of these Deep Reinforcement learning-based routing approaches. © 2024 IEEE.

关键词： Markov chains

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Hybrid TCN-transformer model for predicting sustainable food supply and ensuring resilience

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Alexandria engineering Journal 2025年 127卷 380-391页

作者： Alrashdi, Ibrahim Abd El-Aziz, Rasha M. Taloba, Ahmed I. Farsi, Mohammed Department of Computer Science College of Computer and Information Sciences Jouf University Al-Jouf Sakaka73211 Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Yanbu966144 Saudi Arabia

Major concerns occur in maintaining a sustainable food supply due to population expansion, supply chain interruptions, and climate-related changes. Traditional forecasting models, such as ARIMA, LSTM, and GRU, fail to deal with the dynamic fluctuations and long-range correlations. These methods often have reduced accuracy, high computational cost, and poor generalization when applied to shifting patterns in the food supply system. Based on the abovementioned limitations, this study proposes a Hybrid TCN-Transformer Model by combining the strength of Temporal Convolutional Networks (TCN) with Transformer Attention to provide accurate and efficient food supply prediction. Thus, the project's key objective is to develop a deep learning system that effectively handles massive quantities of temporal data while preserving dependence in short and long durations. The proposed method is novel since it integrates TCN's causal convolutions for extracting sequential patterns. It uses a Transformer Model's self-attention mechanism to capture the complex interactions across time steps. Hybrid design enables faster training, increased interpretability, and better prediction accuracy than current methods. Results from experiments have revealed that the suggested model surpasses the performance of the stand-alone TCN, ARIMA, LSTM, and GRU models in terms of accuracy of predictions, efficiency of computations, and adaptability. Findings thus far suggest deep learning-based predictive analytics has helped improve the food supply chain management by mitigating food wastage, making it environmentally resilient. © 2025 The Authors

关键词： Deep learning

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D2LFS2Net:Multi-class skin lesion diagnosis using deep learning and variance-controlled Marine Predator optimisation:An application for precision medicine

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 207-222页

作者： Veena Dillshad Muhammad Attique Khan Muhammad Nazir Oumaima Saidani Nazik Alturki Seifedine Kadry Department of Computer Science HITEC UniversityTaxilaPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

In computer vision applications like surveillance and remote sensing,to mention a few,deep learning has had considerable *** imaging still faces a number of difficulties,including intra-class similarity,a scarcity of training data,and poor contrast skin lesions,notably in the case of skin *** optimisation-aided deep learningbased system is proposed for accurate multi-class skin lesion *** sequential procedures of the proposed system start with preprocessing and end with *** preprocessing step is where a hybrid contrast enhancement technique is initially proposed for lesion identification with healthy *** of flipping and rotating data,the outputs from the middle phases of the hybrid enhanced technique are employed for data augmentation in the next ***,two pre-trained deep learning models,MobileNetV2 and NasNet Mobile,are trained using deep transfer learning on the upgraded enriched ***,a dual-threshold serial approach is employed to obtain and combine the features of both *** next step was the variance-controlled Marine Predator methodology,which the authors proposed as a superior optimisation *** top features from the fused feature vector are classified using machine learning *** experimental strategy provided enhanced accuracy of 94.4%using the publicly available dataset ***,the proposed framework is evaluated compared to current approaches,with remarkable results.

关键词： contrast enhancement deep learning dermoscopic images features optimization fusion skin cancer

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uFOIL: An Unsupervised Fusion of Image Processing and Language Understanding

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IEEE Access 2025年 13卷 31683-31700页

作者： Rahman, Md. Abdur Hasan, Md. Tanzimul Farooq Howlader, Umar Kader, Md. Abdul Islam, Md. Motaharul Hung Pham, Phuoc Mehedi Hassan, Mohammad United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh Providence College Department of Mathematics and Computer Science ProvidenceRI02918 United States King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia

In academic institutions, processing and evaluating documents such as exam scripts remains a labor-intensive process susceptible to human error. Traditional digitization systems face significant challenges in handling the complexities of mixed handwritten and printed text and varying document structures. These challenges are exacerbated by the absence of annotated datasets due to privacy concerns, particularly in contexts involving sensitive exam evaluations. To address these issues, this study introduces uFOIL, an unsupervised ensemble-based framework that integrates advanced image and language processing techniques to automate the extraction and validation of key information. The framework employs a majority voting mechanism that combines four state-of-the-art optical character recognition systems. Furthermore, a transformer architecture is incorporated to enhance contextual understanding and the structured formatting of extracted text that follows a post-processing confidence scoring mechanism. The proposed framework achieves high performance, with accuracies of 95.77% and 96.48% for student names and IDs, respectively;and 95.07% for total mark validation based on a dataset of exam script samples. Additionally, experiments on the benchmark ICDAR2013 dataset suggest the framework’s strong applicability achieving precision, recall, and F1-scores of 95.89%, 97.86%, and 96.87%, respectively. © 2025 The Authors.

关键词： Optical character recognition

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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Mixed-decomposed convolutional network:A lightweight yet efficient convolutional neural network for ocular disease recognition

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 319-332页

作者： Xiaoqing Zhang Xiao Wu Zunjie Xiao Lingxi Hu Zhongxi Qiu Qingyang Sun Risa Higashita Jiang Liu Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhenChina Tomey Corporation NagoyaJapan Guangdong Provincial Key Laboratory of Brain‐inspired Intelligent Computation Department of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhenChina Singapore Eye Research Institute SingaporeSingapore

Eye health has become a global health concern and attracted broad *** the years,researchers have proposed many state-of-the-art convolutional neural networks(CNNs)to assist ophthalmologists in diagnosing ocular diseases efficiently and ***,most existing methods were dedicated to constructing sophisticated CNNs,inevitably ignoring the trade-off between performance and model *** alleviate this paradox,this paper proposes a lightweight yet efficient network architecture,mixeddecomposed convolutional network(MDNet),to recognise ocular *** MDNet,we introduce a novel mixed-decomposed depthwise convolution method,which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low-resolution and high-resolution patterns by using fewer computations and fewer *** conduct extensive experiments on the clinical anterior segment optical coherence tomography(AS-OCT),LAG,University of California San Diego,and CIFAR-100 *** results show our MDNet achieves a better trade-off between the performance and model complexity than efficient CNNs including MobileNets and ***,our MDNet outperforms MobileNets by 2.5%of accuracy by using 22%fewer parameters and 30%fewer computations on the AS-OCT dataset.

关键词： artificial intelligence deep learning deep neural networks image analysis image classification medical applications medical image processing

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ABES: attention bi-directional ensemble SVM for early detection of brain tumors

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Neural Computing and Applications 2024年第26期36卷 16179-16193页

作者： Subramaniam, Erana Veerappa Dinesh Krishnasamy, Valarmathi Department of Computer Science and Business Systems Ramco Institute of Technology Rajapalayam India Department of Electronics and Communication Engineering P.S.R. Engineering College Sivakasi India

Brain tumor is the most serious and deadly disease, and it is formed due to abnormal cell production. There are two different sorts of tumors including benign (non-cancerous) and malignant (cancerous), and the third level of a brain tumor is cancerous, which is a highly deadly form of cancer. Detecting brain tumors early is crucial for accurate diagnosis and efficient treatment planning. Although numerous techniques are established, the prediction techniques are time-consuming, have higher computational complexity, prone to overfitting, and have limited accuracy. Thus we proposed an Attention Bi-directional Gated Recurrent Unit Ensemble Support Vector Machine (ABES model) for detecting brain tumors. Here, the Bi-GRU layer learns the most important context information for every image. The attention layer also uses the attention mechanism to assign weights to each Bi-GRU layer output. The ensemble SVM classifier performs the categorization of brain tumors. Then, the training set is used to train the Attention BiGRU model and Ensemble SVM classifiers. The ABES classifier weights are optimized by utilizing the improved whale optimization algorithm. To demonstrate the efficiency of our proposed method, we compare it to four existing methods including Fully Automatic Heterogeneous Segmentation-based Support Vector Machine, Whale Harris Hawks Optimization-based Deep Learning, Machine learning-based back propagation neural networks, and Deep Convolutional Neural Network. Also, the ABES model is validated using the MRI dataset and the Figshare brain tumor dataset. Accuracy, precision, specificity, and sensitivity are the performance metrics used to evaluate classification performance, which achieves 97.2% accuracy, 98.61% Precision, 96.48% Specificity, and 97.34% Sensitivity, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Support vector machines

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