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4th International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2025

作者： Sakib, Abu Noman Md Partha, Sajal Basak Shojib, Mohammad Anik Islam Hasan, Md Mahamudul Shuvo, Badiuzzaman Computer Science and Engineering Khulna University of Engineering & Technology Khulna Bangladesh Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Computer Science and Engineering East West University Dhaka Bangladesh

ISBN: (纸本)9798331530938

The agriculture sector has already seen a favorable advancement because of the involvement of machine learning (ML) methods. Sugarcane industry faces a gruesome drawback because of several types of leaf diseases. Nowadays, ML methods have come handy also to detect the diseases at the very early stage. Our work represents a cutting-edge deep learning method with a goal to classify five popular sugarcane leaf diseases: Bacterial Blight, Mosaic, RedRot, Rust, Yellow, and can also identify which one is Healthy. This study also conducts a comparative analysis of various state-of-the-art models, evaluating their training time, parameters, accuracy and model size using the same dataset. Based on this evaluation, the optimal model was selected for final evaluation. Several pre-trained models were modified to enhance speed and reduce storage requirements. The system can identify the disease category for every given sugarcane leaf image using the EfficientNetB0 model, which demonstrated the best performance, and also provides the probability of correct identification. Several preprocessing steps are involved such as removing noise, addressing missing data, and standardizing images to ensure maximum accuracy. The models were trained, validated, and tested on a dataset of 4,800 sugarcane leaf images from the Sugarcane Leaf Disease Dataset. Among all the models, EfficienNetB0, InceptionV3 and ResNet50 achieved high accuracy (98.75%, 98.54% and 98.54%, respectively). However, EfficienNetB0 was ultimately chosen due to its faster training, smaller size, and fewer parameters, making it ideal for this study. © 2025 IEEE.

关键词： Contrastive Learning

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Proactive Crowdsourced Monitoring and Sensing With Expansible Activity Recognition Based on Internet of Things Localization

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IEEE Internet of Things Journal 2025年第11期12卷 17674-17686页

作者： Chen, Lien-Wu Liao, Chun-Wei Liu, Jun-Xian Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan

This paper proposes a Proactive Crowdsourced Monitoring and Sensing (PCMS) framework with the designed Smart iBeacon device to accurately recognize the activities of an equipped target, exclusively customize the recognition model of a specific target, and actively trigger cooperative tracking of nearby smartphones for an abnormal target based on Internet of Things (IoT) localization. According to our review of relevant research, PCMS is the first framework that provides the following features: i) coarse-grained and fine-grained features can be extracted to accurately recognize target activities through densely connected convolutional networks with improvement design, ii) crowdsourced monitoring and sensing can be actively triggered for a target as the abnormal activity of the target is detected, and iii) deep learning model of target activity recognition can be exclusively customized for a specific target to improve the recognition accuracy based on the dedicated activity data of the target. An Android-based prototype with stationary iBeacon nodes and the Smart iBeacon is implemented to verify the feasibility and superiority of our PCMS framework. Experimental results show that our framework outperforms existing methods and can accurately recognize target activities for abnormal event detection and proactive crowdsourced tracking in a real-time manner. © 2014 IEEE.

关键词： Smartphones

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Enhancing aviation safety: Machine learning for real-time ADS-B injection detection through advanced data analysis

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Alexandria engineering Journal 2025年 126卷 262-276页

作者： Rahman, Md. Atiqur Bhuiyan, Touhid Ali, M. Ameer Department of Computer Science and Engineering East West university Dhaka Bangladesh School of Information Technology Washington University of Science and Technology Alexandria United States Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

Airplanes play a critical role in global transportation, ensuring the efficient movement of people and goods. Although generally safe, aviation systems occasionally encounter incidents and accidents that underscore the need for proactive risk management. This study employs machine learning to detect abnormalities in commercial aircraft operations using Automatic Dependent Surveillance–Broadcast (ADS-B) data. Given the growing reliance on ADS-B technology, concerns regarding its susceptibility to security breaches, such as injection attacks, have intensified. To address these vulnerabilities, we propose a robust ADS-B injection detection system. Employing GridSearchCV for model optimization, it effectively identifies and categorizes injection risks. The system's performance, evaluated using the ADS-B Message Injection Attacks Dataset, achieves outstanding results, including a value of 0.9970 for the accuracy, precision, recall, and F1 score. The proposed classifier also demonstrates a higher area under the curve (0.9999), specificity (0.9956), and Cohen's kappa (0.9954) than existing approaches, while achieving a lower log loss (0.0107). This research significantly enhances aviation security by introducing a highly accurate, computationally efficient, and reliable real-time detection model for ADS-B injection attacks, ensuring the integrity and resilience of modern flight control systems. © 2025 The Authors

关键词： Risk management

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Moth flame optimization algorithm for efficient feature selection and hybrid classification technique for intrusion detection system

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Multimedia Tools and Applications 2025年 1-21页

作者： Keerthanarani, U. Selvakumar, V. Singaravelan, S. Raja, S. Edwin Thenmozhi, K. Shunmugam, D. Arun Dept. of Computer Science and Engineering P.S.R Engineering College Sivakasi India Dept.Of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai Avadi India Dept. Of Computer Science and Engineering Coimbatore Institute of Engineering and Technology Coimbatore India Dept. Of Electronics and Communication Engineering P.S.R Engineering College Sivakasi India

The rapid expansion of the Internet of Things (IoT) brings numerous benefits but further presents fresh difficulties, especially in terms of security. The distributed and interconnected nature of IoT devices makes them susceptible to various security threats. An Intrusion Detection System (IDS) plays a crucial role in safeguarding IoT networks by monitoring, detecting, and responding to potential security incidents. The Internet of Things (IoT) connects billions of devices, sensors, and systems, transforming industries and everyday life. However, this extensive interconnectivity also introduces significant security risks, making Intrusion Detection Systems (IDS) a critical component for safeguarding IoT environments. Feature selection is a crucial aspect of building effective Intrusion Detection Systems (IDS). Selecting relevant features not only helps in achieving better classification accuracy but also contributes to reducing false alarm rates. In the proposed system a moth flame optimizer is utilized to select the efficient features from the dataset, overall accuracy of over 97%. Further it is classified employing Random Forest and J48 classifier which attains high classification accuracy and low false alarm rate. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature Selection

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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Beyond chat-GPT: a BERT-AO approach to custom question answering system

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Multimedia Tools and Applications 2025年第13期84卷 12513-12530页

作者： Sophia, J. Jinu Jacob, T. Prem Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Sathyabama Institute of Science and Technology Chennai India

This work introduces a novel Custom Question Answering (CQA) model leveraging Adam optimized Bidirectional Encoder Representations from Transformers (BERT-AO). This model tackles the challenge of combining textual and visual information for accurate question answering. The core concept lies in understanding the intricate relationships between words in text and the visual features within images. The proposed CQA model with BERT-AO incorporates separate branches for processing text (BERT-AO) and images (CNN), both utilizing self-attention mechanisms. This enables a deeper understanding of context within both textual and visual data. Training is conducted on a comprehensive dataset encompassing factual, open-ended questions, answers, and images, providing a strong foundation for the model’s learning process. The performance of the proposed CQA Model with BERT-AO attains 20.46%, 36.86%, 43.98%, 19.97%, and 17.604% higher accuracy and 27.42%, 41.56%, 42.78%, 18.72% and 16.15% higher F-Score value compared with existing methods like Enhancing Bridge Inspection Visual Question Answering via Pre-Training with External Image-Text Data (VLP), LOIS: Exploring Instance Semantics for Visual Question Answering (LOIS), Bidirectional Multimodal Attention Networks for Visual Question Answering (BiGAN), A Pairwise Attention Framework with Word and Sentence Embedding for Visual Question Answering (DALNet-WSE) and Enhanced GNN Focused Attention and Hybridized Optimization Approach for Visual Inquiry Resolution (GNN-SA-MRFO) respectively. This highlights its effectiveness in combining textual and visual information for superior question answering capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Semantics

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Designing a novel network anomaly detection framework using multi-serial stacked network with optimal feature selection procedures over DDOS attacks

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International Journal of Intelligent Networks 2025年第1期6卷 1-13页

作者： Pradeep, K. Jeevan Shukla, Prashanth Kumar Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

- Distributed denial-of-service (DDoS) attacks are the major threat that disrupts the services in the computer system and networks using traffic and targeted sources. So, real-world attack detection techniques are considered an important element in executing cybersecurity tasks. The present DDoS techniques are prone to False Positive Rates (FPR) and also it didn't acquire the complicated patterns presented in the attack traffic. Internet of Things (IoT) is a complicated network with resource-constrained devices and networks that are prone to different security threats like DDoS attacks. Later, the Software Defined Networking (SDN) with IoT models is used to enhance the access control techniques and security models. DDoS attacks are considered as an important threat in the IoT networks. Hence, it is important to construct a novel network anomaly detection model with a deep learning mechanism to resolve the limitations of the existing techniques. Initially, essential data required for the validation are gathered from the IDS ISCX 2012 dataset. The optimal features are selected from input data using the Predefined-Mud Ring Algorithm (P-MRA). The optimally selected features are provided to the Multi-Serial Stacked Networks (Multi-SSN), which is the fusion of Convolutional Autoencoder (CAE), Gated Recurrent Unit (GRU), and Bayesian Learning (BL) networks. Here, the essential features for the validation are acquired from the CAE and GRU. Then, these features are stacked and given to the BL mechanism for detecting the anomalies in the network. Further, several experimental validations are performed in the developed framework over traditional network anomaly detection mechanism. © 2024 The Authors

关键词： Internet of things

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THE IMPACT OF USING AI AND CHATGPT IN ORGANIZATIONS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 53-64页

作者： Tudose, Cătălin Boiangiu, Costin Anton Antal, Jasmyna Cristina Bănăţeanu, Cristiana Breazu, Laura Piele, Mihai Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine learning, natural language processing, and ChatGPT in various business areas. A central aspect of the study is a survey that reflects professionals' views on AI's effectiveness, data security concerns, and its influence on workplace dynamics across multiple industries. The research reveals both the benefits, such as improved efficiency and innovation, and challenges, including data security risks and potential overreliance on technology. Concluding, the article emphasizes the need for a balanced approach to AI adoption in businesses, advocating for a combination of technological and human expertise for optimal results. This study provides key insights for companies looking to leverage AI for sustainable growth and competitive advantage. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Adversarial machine learning

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Clustering-Based Intrusion Detection System Meets Multi-Critics Generative Adversarial Networks

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IEEE Internet of Things Journal 2025年第11期12卷 16112-16128页

作者： Wang, Haofan Kandah, Farah Mendis, Thilina Medury, Lalith Auburn University Computer Science and Software Engineering AuburnAL36849 United States

Network security has continuously been a major focus of research and concern on a global scale. The Intrusion Detection System (IDS), as a crucial defensive measure against network attacks, has undergone multiple iterations and evolutions since its inception to adapt to the ever-changing network environment. Due to the widespread issue of data imbalance in network security datasets, a single machine learning or deep learning model often struggles to effectively handle different types of attacks. In this work, we propose a Multi-Critics Generative Adversarial Networks (GAN) Clustering-Based IDS (MCGC-IDS) model to address the issue of data imbalance. The quality of the generated data is analyzed using correlation heatmaps and PCA plots, which later is used to update the dataset that is utilized for feature extraction with autoencoders. Subsequently, CNN-LSTM models are employed to analyze clusters formed by the Weighted Fuzzy C-means (WFCM) clustering algorithm to achieve enhanced performance for the IDS system. This model is then compared with two existing models. The results indicate that while the GAN-generated data retains the original dataset distribution, it also addresses the issue of imbalance. Moreover, the subsequent multi-layered processing enables the overall model to more effectively handle various types of attacks. Finally, when this model is tested on a similar dataset, the UNSW-NB15, it continues to demonstrate superior performance, indicating its strong generalizability. © 2014 IEEE.

关键词： Generative adversarial networks

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Query-based information retrieval system adopting whale manhattan optimization-based deep belief neural network

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Multimedia Tools and Applications 2025年第12期84卷 10587-10607页

作者： Dahiya, Deepak School of Engineering and Computer Science University of Pittsburgh Johnstown Johnstown15904 United States

The Information Retrieval system aims to discover relevant documents and display them as query responses. However, the ever-changing nature of user queries poses a substantial research problem in defining the necessary data to respond accurately. The Major intention for this study is for enhance the retrieval of relevant information in response to user queries. The aim to develop an advanced IR system that adapts to changing user requirements. By introducing WMO_DBN, we seek to improve the efficiency and accuracy of information retrieval, catering to both general and specific user searches. The proposed methodology comprises three important steps: pre-processing, feature choice, and categorization. Initially, unstructured data subject to pre-processing to transform it into a structured format. Subsequently, relevant features are selected to optimize the retrieval process. The final step involves the utilization of WMO_DBN, a novel deep learning model designed for information retrieval based on the query data. Additionally, similarity calculation is employed to improve the effectiveness for the network training model. The investigational evaluation for the suggested model was conducted, and its performance is measured regarding the metrics of recall, precision, accuracy, and F1 score, the present discourse concerns their significance within the academic realm. The results prove the superiority of WMO_DBN in retrieving relevant information compared to traditional approaches. This research introduces novel method for addressing the challenges in information retrieval with the integration of WMO_DBN. By applying pre-processing, feature selection, and a deep belief neural network, the proposed system achieves more accurate and efficient retrieval of relevant information. The study contributes to the advancement of information retrieval systems and emphasizes the importance of adapting to users' evolving search queries. The success of WMO_DBN in retrieving relevant inform

关键词： Search engines

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