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arXiv 2025年

作者： Ahmmed, Fahud Raihan, Md. Zaheer Nahar, Kamnur Asadujjaman, D.M. Rahman, Md. Mahfujur Tamim, Abdullah Dept. of Computer Science & Engineering Varendra University Rajshahi Bangladesh Dept. of Computer Science & Engineering Khulna University of Engineering & Technology Bangladesh Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Bangladesh Dept. of Computer Science & Engineering Rajshahi University Bangladesh

Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Skin diseases such as Actinic Keratosis and Psoriasis can be fatal. These are treatable if identified early. However, its diagnostic methods are expensive and not widely accessible. In this study, a novel and efficient method for diagnosing skin diseases using deep learning techniques has been proposed. This approach employs a modified VGG16 Convolutional Neural Network (CNN) model. This model includes several convolutional layers. The VGG16 model has been employed using ImageNet weights and modified top layers. The top layer is modified by fully connected layers and a final softmax activation layer to obtain the result. The dataset analyzed is publicly available and titled "Skin Disease Dataset". The VGG16 architecture does not include augmentation by default;data augmentation is typically performed through rotation, shifting, and zooming during preprocessing prior to model training. The proposed methodology achieved 90.67% accuracy using the modified VGG16 model, demonstrating reliability in classifying skin diseases. The modified pre-trained model showed promising results, increasing its potential for real-world *** Codes 68T07 © 2025, CC BY.

关键词： Convolutional neural networks

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Contention-Free Configured Grant Scheduling for 5G URLLC Traffic 23

Contention-Free Configured Grant Scheduling for 5G URLLC Tra...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Tianyu Zhang Xiaobo Sharon Hu Song Han Dept. of Computer Science and Engineering University of Connecticut Dept. of Computer Science and Engineering University of Notre Dame

ISBN: (纸本)9798350323481

5G networks are being designed to support ultra reliable and low latency communication (URLLC) services in many real-time industrial applications. The conventional grant-based dynamic scheduling can hardly fulfill the URLLC requirements due to the non-negligible transmission delays introduced during the spectrum resource grant process. To address this problem, 5G defines a grant-free transmission scheme, namely configured grant (CG) scheduling, for uplink (UL) traffic to pre-allocate spectrum resource to user equipments (UEs). This paper studies CG scheduling for periodic URLLC traffic with real-time and collision-free guarantees. An exact solution based on Satisfiability Modulo Theory (SMT) is first proposed to generate a feasible CG configuration for a given traffic set. To enhance scalability, we further develop an efficient graph-based heuristic consisting of an offset selection method and a multicoloring algorithm for spectrum resource allocation. Extensive experiments are conducted using 3GPP industrial use cases to show that both approaches can satisfy the real-time and collision-free requirements, and the heuristic can achieve comparable schedulability ratio with the SMT-based approach but require significantly lower running time.

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images

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Procedia computer science 2025年 252卷 108-117页

作者： Umme Sara Utpol Kanti Das Juel Sikder Sudipta Roy Chowdhury Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar-4500 Rangamati Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%.

关键词： Skin Lesion Cancer-CNN (SLC-CNN) Skin Cancer Deep Learning Classification Segmentation

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Advancing Wi-Fi Intrusion Detection Systems with Machine Learning Techniques for Enhanced Classification of Wireless Attacks

Advancing Wi-Fi Intrusion Detection Systems with Machine Lea...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Hasseeb Ahmad Bhutta Muhammad Talha Jahangir Areeb Ahmad Bhutta Computer Science Dept. National University of Sciences and Technology Islamabad Pakistan Computer Science Dept. MNS University of Engineering and Technology Multan Pakistan Computer Science Dept. Information Technology University Lahore Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

WiFi usability and scalability have contributed to an improvement in human living standards. However, this has also led to increased vulnerabilities and attack vectors within WiFi networks. Authentic users can face network failures and denial of service caused by attacks on wireless networks by some third-party. Wireless Intrusion Detection System (IDS) is needed to detect wireless attacks. Main of objective of wireless IDS is to monitor network traffic and classify if it is an attack or normal traffic. Modern anomaly based wireless intrusion detection systems use machine learning (ML) to learn from previous attacks in the dataset to learn patterns of the attack. Although this method is effective, but there is also downside to this approach. The time required to train and test these ML models are exceedingly high along with high computational costs. Big data is emerging technologies that is getting advanced day by day, due to which, network technology is increasing rapidly. For that reason, we need to discuss the issue, that is high computational costs. In our proposed solution, we used our custom oversampling technique BBOT (Balanced Boost Oversampling Technique) to rectify class imbalance, generating artificial samples for the classes, enhancing model performance. The results of the experiments show that the combination of feature selection, BBOT, and Decision Tree outperformed all other classifiers with reduced computational overhead, providing a practical and efficient solution for real-time intrusion detection in WiFi networks. When compared to XGBoost, the provided solution achieves a similar level of accuracy, but with significantly reduced training time.

关键词： Wireless communication Computational modeling Wireless networks Intrusion detection Machine learning Big Data Feature extraction Vectors Computational efficiency Wireless fidelity

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm

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Procedia computer science 2025年 252卷 510-518页

作者： Priya M Vijaya kumar K Vennila P Prasanna M A Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Lecturer Dept. of Computer Science and Engineering Women’s Engineering College Puducherry- 605008 Asst. Professor Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam -611002 Asst. Professor Dept. of Computer Science and Engineering K.Ramakrishnan college of Technology Trichy -621112

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one’s own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers’ opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner).

关键词： Data analytics Sarcasm Lexical dictionary N-gram Deep learning Text mining

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Monocular UAV Motion Planning Method Based on Deep Reinforcement Learning

Monocular UAV Motion Planning Method Based on Deep Reinforce...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Man-Chen Hsueh Chun Ku Xiu-Zhi Chen Yen-Lin Chen Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This paper presents a monocular UAV motion planning method based on the DRL method SDDPG. By processing image data with the self-proposed lightweight dept. estimation model and a pretrained encoder, the information is sent into an improved Actor-Critic Network, which addresses overestimation issues and enhances learning efficiency. The experiments were conducted in Unreal Engine 4 and Airsim, which demonstrate the improvement of the motion planning ability, enabling UAVs to autonomously navigate and safely reach the defined destination.

关键词： Visualization Image coding Navigation Computational modeling Atmospheric modeling Network architecture Autonomous aerial vehicles Deep reinforcement learning Planning Resource management

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Analysis of optimization Techniques for Dynamic Neural Networks 3

Analysis of optimization Techniques for Dynamic Neural Netwo...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mallegowda, M. Nair, Saanvi Khirwal, Sahil Animesh, Manik Mishra, Vasuman Kanavalli, Anita Ms Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Dept. of Cse Bengaluru India

ISBN: (纸本)9798331515911

Models based on dynamic neural networks such as DeBERTa and Vision Transformers ViT have disrupted the two fields. However, there is one complication - their performance requires so much computation which limits its realistic applications. The aim of this study is to propose advanced optimization techniques that can increase the efficiency of these models without compromising their performance. We adopt approaches such as DistilBERT, DeBERTa and Vision Transformer models. Then we carry out detailed empirical analysis of the consequences of these techniques on the model accuracy, time performance, throughput as well as resources consumption. The findings show enhancement models, where some methods improved the throughput by 29% and reduced the GPU usage by 61% at the same or higher accuracy. This reveals the capacity of dynamic neural networks in resource-constrained settings and enhances the overall vision of the use of such models in practice. © 2025 IEEE.

关键词： Neural network models

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Speech-Guided Drone Control System Based on Large Language Model

Speech-Guided Drone Control System Based on Large Language M...

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2025 International Conference on Electronics, information, and Communication, ICEIC 2025

作者： Choi, Seok-Hun Kim, Zeen-Chul Buu, Seok-Jun Gyeongsang National University Dept. of Computer Science and Engineering Jinju-si Korea Republic of Aerospace Experts Corporation Daejeon Korea Republic of

ISBN: (纸本)9798331510756

The growing use of drones in logistics, agriculture, surveillance, and search-and-rescue operations has highlighted the need for more intuitive and adaptable control systems. Traditional control methods, such as joysticks and mobile applications, often necessitate technical expertise, thereby limiting their accessibility in hands-free or time-sensitive scenarios. This paper introduces a Speech-guided Drone Control System that utilizes Large Language Model (LLM) to process and execute natural language commands. The system is composed of three key components: speech-to-text conversion, natural language command interpretation, and drone control execution. The proposed system enables users to issue complex commands through natural speech, which are then processed and broken down into executable drone actions in real time. The system underwent rigorous testing across various levels of command complexity, demonstrating high accuracy and responsiveness even in handling ambiguous or incomplete commands. The results suggest that this approach not only simplifies drone operation but also represents a transformative step toward making advanced drone control universally accessible and highly efficient in critical sectors such as disaster response and precision agriculture. © 2025 IEEE.

关键词： Drones

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