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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ravikumar, B.N. Vidya, M.S. Gowda, Naveen Chandra NarendraBabu, C.R. Veena, H.N. Ramani, D. Roja Bms Institute of Technology and Management Department of Information Science and Engineering Bengaluru India Bms Institute of Technology and Management Department of Computer Science and Engineering Bengaluru India School of Computer Science and Engineering Reva University Bengaluru India Sjb Institute of Technology Department of Computer Science and Engineering Bengaluru India New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331529246

The grading of fruits relies on inspections, experiences, and observations, with a proposed system integrating machine learning techniques to assess fruit freshness. By analyzing 2D fruit portrayals based on shape and color, limitations arise as different fruits may share similar color, size, and shape values. To enhance accuracy, the system combines size, shape, and color analysis with Convolutional Neural Network (CNN) technology. Upon capturing the fruit image, properties like size, shape, and color are extracted, followed by CNN training and testing processes. The proposed work have implements UNet and En-UNet chosen over other architectures like ResNet or MobileNet for segmentation. This innovative approach utilizes CNN to extract comprehensive features, yielding promising results in fruit freshness detection with accuracy of 99% and loss rate of 0.0123. © 2025 IEEE.

关键词： Convolutional neural networks

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Auto Encoders with Cellular Automata for Anomaly Detection 5th

Auto Encoders with Cellular Automata for Anomaly Detection

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Sree, Pokkuluri Kiran Prasad, M. N, SSSN Usha Devi Rao, P. B. V. Raja Murty, P.T.S. Satyamallesh, Asapu Varma, Ch Phaneendra Babu, Gurujukota Ramesh Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Computer Science and Engineering University College of Engineering JNTU Kakinada India

ISBN: (纸本)9783031770777

Combining auto encoders and hybrid cellular automata provides a novel way to identify anomalies in structured data in the field of anomaly detection. Dimensionality reduction and extracting the features is one of the know expertise of Auto encoders. Auto encoders compress and rebuild data into a lower-dimensional space, emphasizing anomalies with more reconstruction errors for non-typical data. Simultaneously, hybrid cellular automata provide a layer of spatial dynamics modelling by controlling the evolution of data through rule-based interactions among neighboring units. The hybrid methodology has the advantages of both systems: the local interaction modelling of hybrid cellular automata and the pattern recognition powers of auto encoders. This novel method is obtaining an accuracy of 99.83% which is promising, when comparing with the baseline methods. The integration of hybrid cellular automata instead of linear cellular automata has improved the overall accuracy and performance of the proposed classifier. We have used various parameters like F1 Score, precision, recall and ROC for measuring the effectiveness of the proposed system. The accuracy reported for anomaly detection in financial tractions is 99.63 and video surveillance is 99.96. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Dimensionality reduction

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Electromyography-Based Gesture Recognition with Explainable AI (XAI): Hierarchical Feature Extraction for Enhanced Spatial-Temporal Dynamics

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IEEE Access 2025年 13卷 88930-88951页

作者： Shin, Jungpil Miah, Abu Saleh Musa Konnai, Sota Hoshitaka, Shu Kim, Pankoo The University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan Chosun University Department of Computer Engineering Gwangju 61452 South Korea

Hand gesture recognition using multichannel surface electromyography (sEMG) is challenging due to unstable predictions and inefficient time-varying feature enhancement. To overcome the lack of signal-based time-varying feature problems, we propose a lightweight squeeze-excitation deep learning-based multi-stream spatial-temporal dynamics time-varying feature extraction approach to build an effective sEMG-based hand gesture recognition system. Each branch of the proposed model was designed to extract hierarchical features, capturing both global and detailed spatial-temporal relationships to ensure feature effectiveness. The first branch, utilizing a Bidirectional-TCN (Bi-TCN), focuses on capturing long-term temporal dependencies by modelling past and future temporal contexts, providing a holistic view of gesture dynamics. The second branch, incorporating a 1D Convolutional layer, separable CNN, and Squeeze-and-Excitation (SE) block, efficiently extracts spatial-temporal features while emphasizing critical feature channels, enhancing feature relevance. The third branch, combining a Temporal Convolutional Network (TCN) and Bidirectional LSTM (BiLSTM), captures bidirectional temporal relationships and time-varying patterns. Outputs from all branches are fused using concatenation to capture subtle variations in the data and then refined with a channel attention module, selectively focusing on the most informative features while improving computational efficiency. The proposed model was tested on the Ninapro DB2, DB4, and DB5 datasets, achieving accuracy rates of 96.41%, 92.40%, and 93.34%, respectively. Additionally, we visualize the attention maps across various classes. These results demonstrate the system’s capability to handle complex sEMG dynamics, offering advancements in prosthetic limb control and human-machine interface technologies with significant implications for assistive technologies. © 2013 IEEE.

关键词： Deep Learning Electromyography (EMG) Hand Gesture Recognition Temporal Convolutional Network (TCN)

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Breast cancer diagnosis using machine learning model 4th

Breast cancer diagnosis using machine learning model

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4th International Conference on Computational Methods in science and Technology, ICCMST 2024

作者： Arthi, T.S. Partheeban, N. Singh, Bhoomika Singh, Aparajitha Tripathi, Nikhil Department of Computer Science and Engineering Galgotias College of Engineering & Technology Greater Noida India Department of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781032911571

Early and accurate detection of breast cancer, particularly Invasive Ductal Carcinoma (IDC), is critical for improving patient outcomes. Traditional diagnostic methods like histopathology and mammography have limitations in terms of time, cost, and potential subjectivity. This study proposes a lightweight Machine Learning (ML) model for IDC classification using digital whole-mount breast cancer slide images. The model aims to achieve high accuracy while being suitable for deployment in resource-constrained environments. We evaluate our model on a publicly available breast histopathology dataset and compare its performance with other Deep Learning (DL) models, focusing on both accuracy and computational efficiency. The results demonstrate that our model achieves a high classification accuracy (around 89.3 percent) with minimal processing power and memory requirements. Furthermore, successful deployment on a Raspberry Pi embedded system validates its potential for real-world application in resource-limited settings, potentially leading to improved breast cancer diagnosis accessibility. Index Terms—Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), Deep Learning (DL), Invasive Ductal Carcinoma (IDC), Multilayer Perceptron (MLP), Raspberry Pi, Tiny Machine Learning (TinyML). © 2025 the Author(s).

关键词： Convolutional neural networks

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A Base Station Deployment Algorithm for Wireless Positioning Considering Dynamic Obstacles

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computers, Materials & Continua 2025年第3期82卷 4573-4591页

作者： Aiguo Li Yunfei Jia Department of Network Engineering Xi’an University of Science and TechnologyXi’an710600China College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710600China

In the context of security systems,adequate signal coverage is paramount for the communication between security personnel and the accurate positioning of *** studies focus on optimizing base station deployment under the assumption of static obstacles,aiming to maximize the perception coverage of wireless RF(Radio Frequency)signals and reduce positioning blind ***,in practical security systems,obstacles are subject to change,necessitating the consideration of base station deployment in dynamic ***,research in this area still needs to be *** paper proposes a Dynamic Indoor Environment Beacon Deployment Algorithm(DIE-BDA)to address this *** algorithm considers the dynamic alterations in obstacle locations within the designated *** determines the requisite number of base stations,the requisite time,and the area’s practical and overall signal coverage *** experimental results demonstrate that the algorithm can calculate the deployment strategy in 0.12 s following a change in obstacle *** results show that the algorithm in this paper requires 0.12 s to compute the deployment strategy after the positions of obstacles *** 13 base stations,it achieves an effective coverage rate of 93.5%and an overall coverage rate of 97.75%.The algorithm can rapidly compute a revised deployment strategy in response to changes in obstacle positions within security systems,thereby ensuring the efficacy of signal coverage.

关键词： Wireless positioning base station deployment dynamic obstacles dynamic obstacle wireless positioning

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Contract-Based Incentive Mechanisms for UAV Networks in Covert Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7378-7391页

作者： Tian, Wen Fan, Hao Liu, Guangjie Du, Miao Liu, Yinqiu Niyato, Dusit Han, Zhu Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing University of Information Science and Technology Key Laboratory of Intelligent Support Technology In Complex Environment Ministry of Education Nanjing210044 China Southeat University School of Computer Science and Engineering China Southeast University Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point covert communication method cannot effectively guarantee the reliability of covert communication. In order to solve this problem, unmanned aerial vehicle (UAV) are widely adopted in covert communication as common relay equipment. However, the UAV maximizes its benefits by taking advantage of the information asymmetry between itself and the covert communication sender. Therefore, it is necessary to encourage UAV-assisted potential users (UPU) to transmit covert data honestly by designing an information asymmetry-based incentive mechanism. In this paper, we propose a UAV-assisted contract-theoretic (UACT) model to improve the covert communication performance, where UPU honestly transmits covert data and the throughput of UACT can be increased. Specifically, we first divide UPU into finite types and model the data transfer contract between the sender, UPU, and receiver. Then, the contract feasibility of UACT is derived with sufficient and necessary conditions. Finally, we analyze the contract optimization in the continuous case of UPUs. Numerical simulations show that the UACT can incentivize UPUs to transmit covert data through UAV assistance and make covert communication performance close to the information symmetry case. © 1967-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Optimizing classifier performance using PCA-FLANN: a fast and reliable approach

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Iran Journal of computer science 2025年 1-14页

作者： Jena, Monalisa Behera, Ranjan Kumar Dehuri, Satchidananda Cho, Sung-Bae Department of Computer Science Yonsei University Seoul 03722 South Korea Department of Computer Science & Engineering Indian Institute of Information Technology Jharkhand Ranchi India Department of Computer Science Fakir Mohan University Odisha Balasore India

This work introduces PCA-FLANN, an innovative hybrid model combining principal component analysis (PCA) with functional link artificial neural network (FLANN) to achieve efficient non-linear dimensionality reduction and robust classification. The approach is motivated by the need to optimize feature selection, recognizing that not all features equally contribute to achieving optimal accuracy. PCA is employed to extract the most relevant features from datasets, retaining essential information while significantly reducing dimensionality, and FLANN processes these reduced features to handle non-linear correlations effectively. To assess its performance, PCA-FLANN is evaluated on six widely-used real-world datasets from the KEEL repository and compared against hybrid models such as PCA-RBFN, PCA-SVM, PCA-RF, PCA-MLP, and standalone FLANN. The results reveal that PCA-FLANN outperforms these models in accuracy, with improvements of 8.5%, 2.94%, 9.09%, 11.1%, and 9.69%, respectively. Additionally, the model demonstrates enhanced computational efficiency and reduced complexity, establishing PCA-FLANN as a powerful and precise solution for classification tasks involving dimensionality reduction. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Classification Feature extraction FLANN Neural network PCA

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Adaptive Kernel Optimization for Probabilistic Learning: Integrating Support Vector Machines with Gaussian Process Frameworks 1

Adaptive Kernel Optimization for Probabilistic Learning: Int...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kanth, Tatiraju V. Rajani Singh, Balveer Singh, Yaspal Bhutada, Sunil Yamaganti, Rohita P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Computer Science Engineering Shivpuri India P.K. University Department of Information Science Department of Computer Science Engineering Shivpuri India

ISBN: (纸本)9798331529246

With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilistic learning, this study investigates Adaptive Kernel Optimization (AKO), a new method that combines the best features of the Support Vector Machine (SVM) and the Gaussian Process (GP) frameworks. Achieving better flexibility in modeling complicated data distributions while keeping computational efficiency is achieved by employing adaptive kernel functions in the suggested strategy. Quantifying uncertainty in addition to deterministic SVM classifications is made possible with the incorporation of GP kernels, which offer probabilistic insights. The suggested approach guarantees resilience across varied and high-dimensional datasets by dynamically adjusting kernel parameters according to data properties. Extensive testing on benchmark datasets shows that, in comparison to conventional SVM and GP approaches, our model generalizability, classification accuracy, and interpretability are much improved. Autonomous systems, healthcare diagnostics, and financial sectors can all benefit from the scalable, adaptive, and probabilistic learning models that this study establishes. © 2025 IEEE.

关键词： Adversarial machine learning

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Swin Transformer and Momentum Contrast (MoCo) in Leukemia Diagnostics: A New Paradigm in AI-Driven Blood Cell Cancer Classification

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IEEE Access 2025年 13卷 82441-82466页

作者： Jain, Eshika Kaushik, Pratham Kukreja, Vinay Ati, Modafar Hariharan, Shanmugasundaram Ahuja, Vandana Bhattacherjee, Abhishek Kumar Kaushal, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Abu Dhabi University Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Telangana Hyderabad501218 India Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Banur140601 India Lovely Professional University Department of Computer Science Engineering Punjab Phagwara144411 India Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Applications Punjab Rajpura140401 India

Acute Lymphoblastic Leukemia (ALL) is a fast-growing blood cancer that requires prompt diagnosis for effective treatment. Automated image diagnostics offer potential solutions but often lack clinical robustness. Despite their widespread use in medical imaging, Convolutional Neural Networks (CNNs) struggle to differentiate morphologically similar ALL subtypes due to limited context and feature discrimination. Moreover, integrating contrastive self-supervised learning with hierarchical attention-based models remains underexplored in hematologic malignancy classification. This study aims to develop a robust, automated classification model for ALL subtypes using peripheral blood smear images, employing advanced feature extraction through the Swin Transformer framework, combined with Momentum Contrast (MoCo) for contrastive learning and a Bidirectional Encoder Transformer for classification. The Swin Transformer’s patch-based embedding and multi-level attention enhance feature discrimination across ALL subtypes, while MoCo generates distinct embeddings, minimizing overlap between cell types. BiET is employed to classify the refined feature vectors, leveraging self-attention mechanisms to improve classification accuracy. The model achieved an overall classification accuracy of 92.5%, with the precision of 90.3%, a recall of 91.1%, and an F1-score of 90.7% across four classes (Benign, Malignant Early Pre-B, Malignant Pre-B, and Malignant Pro-B). Class-specific performance metrics indicate that Malignant Pre-B achieved the highest F1-score of 92.4%. The MoCo framework reduced contrastive loss from 0.5 to 0.097 for benign cells, enhancing feature discrimination. An ablation study revealed that omitting the dynamic queue decreased accuracy by 5%, underscoring its importance for effective feature learning. This approach can be extended to other hematologic malignancies, with potential for further improvement using larger datasets and real-time diagnostic workflows to support p

关键词： Diseases

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A Deterministic Approach to Shortest Path Restoration in Edge Faulty Graphs 42

A Deterministic Approach to Shortest Path Restoration in Edg...

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42nd International Symposium on Theoretical Aspects of computer science, STACS 2025

作者： Choudhary, Keerti Dhiman, Rishabh Department of Computer Science and Engineering IIT Delhi India

ISBN: (纸本)9783959773652

Afek, Bremler-Barr, Kaplan, Cohen, and Merritt (PODC’01) in their seminal work on shortest path restorations demonstrated that after a single edge failure in a graph G, a replacement shortest path between any two vertices s and t, which avoids the failed edge, can be represented as the concatenation of two original shortest paths in G. They also showed that we cannot associate a canonical1 shortest path between the vertex pairs in G that consistently allows for the replacement path (in the surviving graph) to be represented as a concatenation of these canonical paths. Recently, Bodwin and Parter (PODC’21) proposed a randomized tie-breaking scheme for selecting canonical paths for the "ordered" vertex pairs in graph G with the desired property of representing the replacement shortest path as a concatenation of canonical shortest-paths provided for ordered pairs. An interesting open question is whether it is possible to provide a deterministic construction of canonical paths in an efficient manner. We address this question in our paper by presenting an O(mn) time deterministic algorithm to compute a canonical path family F = {Px,y, Qx,y | x, y ∈ V } comprising of two paths per (unordered) vertex pair. Each replacement is either a PQ-path (of type Px,y ◦ Qy,z), a QP-path, a QQ-path, or a PP-path. Our construction is fairly simple and is a straightforward application of independent spanning trees. We also present various applications of family F in computing fault-tolerant structures. © Keerti Choudhary and Rishabh Dhiman.

关键词： Restoration

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