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Enhanced hyperspectral image classification using multi-scale residual depthwise separable convolutional networks with advanced feature extraction and selection techniques

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International Journal of Information Technology (singapore) 2025年 1-20页

作者： Medikonda, venkata Reddy Reddy, Tatireddy subba Yallamandaiah, s. Karthik, sajja Anil, Chintalapudi Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of Computer Science and Engineering B. V. Raju Institute of Technology Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology & Science for Women Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R.V.R & J.C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India

Hyperspectral image classification faces significant challenges due to high-dimensional data and variability, often leading to inefficiencies in processing and reduced classification accuracy. Existing methods struggle with handling high-dimensional data, exhibit limited generalization capabilities, and lack efficiency in feature selection. To address these issues, this research proposes an advanced deep learning-based approach that integrates noise reduction, contrast enhancement, and min–max normalization for preprocessing. Feature extraction is performed using an Improved ResNet50 model, while optimal feature selection is achieved through the Improved Artificial Rabbits Optimization (IARO) algorithm, which effectively reduces dimensionality while preserving essential features. Classification is carried out using a Multi-scale Residual Depthwise separable Convolutional Network (MRDsCNN), leveraging residual learning and depthwise separable convolutions to enhance performance. The proposed approach significantly improves data quality and model accuracy, providing a robust and efficient solution for hyperspectral image classification. These innovations collectively address the limitations of traditional methods, offering a more reliable and effective classification system. © bharati vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Contrast enhancement model Feature extraction Hyperspectral imaging classification Improved artificial rabbits optimization algorithm Multi-scale residual depthwise separable convolutional neural network

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vision Encoder-Decoder Models for AI Coaching

Vision Encoder-Decoder Models for AI Coaching

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Nayak, Jyothi s Khan, Afifah Khan Mohammed Ajmal Manjeshwar, Chirag banday, Imadh Ajaz B. M. S. College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350338522

This research paper introduces an innovative AI coaching approach by integrating vision-encoder-decoder models. The feasibility of this method is demonstrated using a vision Transformer as the encoder and GPT-2 as the decoder, achieving seamless integration of visual input and textual interaction. Departing from conventional practices of employing distinct models for image recognition and text-based coaching, our integrated architecture directly processes input images, enabling natural question-and-answer dialogues with the AI coach. This unique strategy simplifies model architecture while enhancing the overall user experience in human-AI interactions. We showcase sample results to demonstrate the capability of the model. The results underscore the methodology's potential as a promising paradigm for creating efficient AI coach models in various domains involving visual inputs. Importantly, this potential holds true regardless of the particular visual encoder or text decoder chosen. Additionally, we conducted experiments with different sizes of GPT-2 to assess the impact on AI coach performance, providing valuable insights into the scalability and versatility of our proposed methodology. © 2023 IEEE.

关键词： computer vision

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Enhancing Railway safety with an IoT based system for Real Time Fault Detection and Crack Monitoring 9

Enhancing Railway Safety with an IoT Based System for Real T...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, somasekar Neerugatti, vikram Nalluri, Madhusudana Rao Chukka, santhaiah Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

safety of railway is the major problem worldwide. It has problems like cracks or any fault in the railway tracks. These problems can cause severe accidents if it is not detected regularly and early. In traditional fault finding and crack detecting method, the manual method is involved which is very slow and consist of many errors. by considering all these challenges and issues in track detection method, we have implemented an advanced IoT based railway crack and fault-finding system. It uses advanced sensors, microcontroller, GPs and GsM module which can detect and report faults of the railway track. The system uses real time mechanism. The system is implemented and tested on a prototype railway track to detect crack or any fault. The internet of things technology added modern and accurate fault-finding feature in the system. The IoT plays an important role in implementing advance technologies, as it uses sensors and advance microcontroller. The accuracy rate of fault and crack detection is around 97%, which is for 2 mm crack. Along with this GPs module accurately track fault location. GsM module successfully send the alert message quickly after detecting the fault in the track. Hence the response time is very less and accurate. This system is implemented in order to ensure safety for the railway track and prevent the accidents. Hence the system is useful for improving overall transport safety. © 2024 IEEE.

关键词： Microcontrollers

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Clinical tools to detect Postpartum Depression based on Machine learning and EEG: A Review 2

Clinical tools to detect Postpartum Depression based on Mach...

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2nd International Conference on Computational systems and Communication, ICCsC 2023

作者： Acharya, Anagha Ramesh, Ramya Fathima, Tasmiya Lakhani, Trisha selva Kumar, s. B. M. S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665493932

The expectant mother experiences considerable anatomical and physiological changes during pregnancy that induce stress, and an acute response to stress induces a state of anxiety. When this stress is prolonged, depression may develop insidiously, impairing one's ability to cope with stress. Around the world, up to 15% of new mothers may experience postpartum depression, a mental condition. It has been linked to poor maternal and newborn bonding, reduced breastfeeding initiation, and poor mental health outcomes for mothers, children, and infants. Even though they are common, up to 50% of postpartum issues may go undiagnosed or untreated. Therefore, it is imperative to detect symptoms in the early stages for prevention and intervention. In this paper, we review the existing techniques for PPD detection and prevention and cite their merits and demerits. The secondary questionnaire-based screening tools can only act after the onset of depression but before the development of the disorder, while primary prevention approaches, which use powerful machine learning algorithms, can work before the emergence of symptoms themselves with a higher accuracy. The most accurate classifier has been proven to be support vector Machine in the vast majority of instances. Recently, Electroencephalogram (EEG) has received the most research attention because of its superior features in measuring cognitive abilities in real-Time. Therefore, adopting EEG-based methodologies could help address inconsistencies caused by earlier approaches. Accordingly, the review suggests exploring the use of EEG for improving the detection of postpartum depression to account for early interventions. © 2023 IEEE.

关键词： support vector machines

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Optimized Task scheduling in Diverse Distributed systems with a Hybrid Genetic Algorithm Approach 3rd

Optimized Task Scheduling in Diverse Distributed Systems wit...

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3rd International Conference on Intelligent systems and sustainable Computing, ICIssC 2023

作者： Muthusamy, Ramesh Kumar Kadu, swati Ayyasamy, Karthikeyan Pichuka veera venkata satya, srinivas Arumugam, Ramachandran Ammasai, viswanathan sengan, sudhakar Department of Information Technology V.S.B. College of Engineering Technical Campus Tamil Nadu Coimbatore642109 India Department of AI&DS AISSMS IOIT Pune411001 India Department of Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu Chennai602105 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627012 India

ISBN: (纸本)9789819783540

In Heterogeneous Distributed systems (HDs), efficient task scheduling (Ts) across diverse resources remains a pressing challenge. This paper introduces a Hybrid Genetic Algorithm (HGA) as a solution, blending heuristic techniques with the evolutionary strengths of Genetic Algorithms (GAs) to optimize Task scheduling (Ts). Traditional algorithms often fall short in balancing computational speed with solution quality, especially in large-scale environments. This proposed HGA addresses this by leveraging domain-specific heuristics to provide a promising starting point for the GA's optimization process. The Ts dilemma has been successfully defined by an all-encompassing logical system, which ensures the accuracy of the design of algorithms. In comparison tests, the HGA frequently comes out on top, demonstrating less makespan and a more excellent distribution of tasks among the resources available. Also, resources with more efficient processing capabilities will be provided priority in this recommended heuristic method, which focuses on tasks with higher computational demands. The findings of the present investigation demonstrate that the HGA may transform Ts approaches by providing high-quality solutions while increasing performance. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： scheduling algorithms

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Intrusion detection system using soft computing techniques in 5G communication systems

Intrusion detection system using soft computing techniques i...

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International Conference on Artificial Intelligence, blockchain, Computing and security, ICAbCs 2023

作者： Dhanya, D. shankari Kathir, I. Kuchipudi, Ramu Thamarai, I. Kumar, E. Rajesh Department of Computer Science and Engineering Mar Ephraem College of Engineering and Technology Tamil Nadu Kanyakumari India Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Thiruvallur India Department of Electrical and Electronics Engineering V.S.B. Engineering College Tamil Nadu Karur India Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Computer Science and Engineering Panimalar Engineering College Chennai City Campus Tamil Nadu Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781032669663

The research deals with a thorough survey and starts by reviewing the fundamental knowledge of fuzzy systems over 5G communication. Future directions and scope can be used to demonstrate the desire for 5G communication networks to provide access to new applications and widespread connection. Therefore, the difficulties with the underlying technologies and hardware supporting them should allow for a greater level of service quality. The goal of this study is to create an intrusion detection system that employs fuzzy logic. This study proposes a model for a real-time intrusion detection expert system that seeks to find a variety of security breaches, from outsiders trying to break in to insiders breaking in and abusing the system. Analysis has also been done on the impact of changing the learning algorithms and membership function. The structure can manage very sizeable amount of data with the aid of data collection, storage, retrieval, and analysis, and it integrates the big data stream inside the network to enhance performance of the network. © 2024 The Author(s).

关键词： Intrusion detection

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Forecasting Diabetes: A Machine Learning Approach for Predictive Analysis 15

Forecasting Diabetes: A Machine Learning Approach for Predic...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： sable, Nikita Manohar Kamble, vijaya Chaudhari, Poonam Gogte, Purva Yeshwantrao Chavan college of Engineering Department of Computer Science & Engineering Nagpur India S.B.Jain Institute of Technology Management & Research Department of Computer Science & Engineering Nagpur India GES's R. H. Sapat College of Engineering Mangement Studies and Research Department of MCA Engineering Nashik India Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331300579

Diabetes is a serious condition that is primarily brought on by aging, high blood pressure, pregnancy, insulin, body mass index, and the diabetes gene. Remember that if diabetes is not treated, it can cause damage to other organs and lead to major health issues like high blood pressure, kidney, eye, and heart damage. For the best possible care, diabetes must be detected early. Our research uses a range of machine learning techniques to improve the accuracy of early diabetes identification in patients or persons. To anticipate when diabetes may manifest, we would employ machine learning, ensemble classification algorithms, and a dataset. Naïve bayesian network, logistic regression (LR), K-Nearest Neighbor, random forests (RF), and support vector machines (svM). The research results in a model that clearly demonstrates its ability to predict diabetes. Our results show that the Random Forest model performs more accurately than other machine learning techniques. © Grenze scientific society, 2024.

关键词： Adversarial machine learning

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Malicious Traffic Classification in WsN using Deep Learning Approaches 2

Malicious Traffic Classification in WSN using Deep Learning ...

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2nd IEEE International Conference on Communication, security and Artificial Intelligence, ICCsAI 2023

作者： Gupta, Chhote Lal Prasad Rajassekharan, Dinesh sharma, Dilip Kumar Elangovan, Mohanraj Myilsamy, varatharaj Upreti, Kamal Bansal Institute of Engineering and Technology Dept. of Computer Science and Engineering Lucknow India Peninsula College Faculty of Computer Science Peninsula Malaysia Jaypee University of Engineering and Technology Department of Mathematics Madhya Pradesh Guna473226 India K. S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Tiruchengode637215 India V.S.B College of Engineering Technical Campus Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore642109 India Department of Computer Science Delhi-NCR Ghaziabad India

ISBN: (纸本)9798350369960

Classifying malicious traffic in Wireless sensor Networks (WsNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WsNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LsTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WsNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WsNs is essential. Classifying malicious communication in a WsN using a Long short-Term Memory (LsTM) is efficient. WsNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LsTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. © 2023 IEEE.

关键词： data routing deep learning LsTM malicious transmission neural network Traffic data transmission WsN

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Deep Learning in Analyzing Carbon Flux Patterns for Environmental Health: Remote sensing Insights for Climate Mitigation strategies

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Remote sensing in Earth systems sciences 2025年第2期8卷 337-351页

作者： subramani, Hariharan Harish, M. Neela s, selva kumar Radhakrishnan, P. Madupu, L. N. K. sai sandeep, Kasturi sai Nimma, Divya Department of CSE Panimalar Engineering College Chennai India Department of Biomedical Engineering Easwari Engineering College Chennai India Department of Computer Science and Engineering B.M.S College of Engineering Bull Temple Road Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Civil Engineering R.V.R. & J.C. College of Engineering Andhra Pradesh Chowdavaram Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist Andhra Pradesh India University of Southern Mississippi Data Analyst in UMMC Hattiesburg United States

The integration of deep learning with remote sensing techniques has emerged as a transformative approach in analyzing carbon flux patterns to mitigate climate change impacts on environmental health. This study introduces a spatiotemporal Attention Network (sATN) that leverages satellite-derived data and ground-based observations to predict and understand carbon flux variations. Key features such as vegetation indices, soil moisture, and land surface temperature are prioritized, contributing over 60% to the model’s predictive accuracy. The proposed sATN achieved a remarkable R<sup>2sup> score of 0.95, outperforming traditional models like ConvLsTM (0.87) and Transformers (0.90). With a low RMsE of 0.045 and MAE of 0.031, the sATN demonstrated robust accuracy and efficiency, achieving a balanced F1-score of 0.91. The results highlight spatial hotspots like the Amazon Rainforest and temporal trends indicating seasonal vegetation growth and human-induced emissions as critical contributors to carbon flux dynamics. by identifying regions and periods of high carbon activity, this study provides actionable insights for reforestation efforts, urban emissions control, and climate resilience planning. The sATN framework sets a new benchmark for integrating machine learning with environmental science, paving the way for innovative climate mitigation strategies. © The Author(s), under exclusive licence to springer Nature switzerland AG 2025.

关键词： Artificial intelligence Carbon flux Carbon sequestration Climate change Deep learning Environmental monitoring Remote sensing satellite data spatiotemporal Attention Network vegetation indices

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Clock synchronization Protocols and Models for Wireless sensor Networks

Clock Synchronization Protocols and Models for Wireless Sens...

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Geetha s Latha P A Raajya vardhini suganya R M.s. Nidhya V.S.B Engineering College Bangalore India School of Computer Applications Dayananda Sagar University Bangalore

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Clock synchronization is a critical aspect in the operation of Wireless sensor Networks (WsNs), ensuring that sensor nodes maintain accurate time for coordinated communication and data processing. various clock synchronization algorithms have been developed to address the synchronization challenges in WsNs, aiming to align the clocks of distributed nodes efficiently. These algorithms play a crucial role in achieving synchronization by compensating for clock drift, which can significantly impact the accuracy of timekeeping among sensor nodes. Timing constraints are fundamental in WsNs, as they influence network performance and data transmission reliability. Addressing timing constraints is essential for optimizing network operations and ensuring timely data delivery. In this paper we analysed various synchronization protocols, Algorithm and techniques. Finally, we compared features of TPsN and Rbs protocols, based on accuracy, robustness TPsN is best one.

关键词： Wireless sensor networks Accuracy Protocols scalability Data processing Robustness Energy efficiency synchronization Data communication Clocks

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