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Heterostructure interfaces and dimensionally transformed GaO/ZnO nanostructures for room-temperature hydrogen gas sensors

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International Journal of Hydrogen Energy 2024年 64卷 889-895页

作者： Huang, Bohr-Ran Kathiravan, Deepa Hsieh, Hsin-Rong Saravanan, Adhimoorthy Graduate Institute of Electro-Optical Engineering and Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

This work demonstrates the significant prospective of GaO/ZnO hybrid nanostructures for high-performance hydrogen gas sensing. The wavy grains of GaO support the structural transformation of ZnO nanorod into nanosheets by providing appropriate nucleation sites for ZnO growth. Compared to pure ZnO and GaO, GaO/ZnO exhibited a sixfold increase in H2 response (60% at 500 ppm), excellent selectivity towards H2 among other interfering gases, and outstanding long-term stability (retaining 98% of its initial response after 36 days). This greater performance rises from the unique morphology and different surface interactions within the GaO/ZnO hybrid nanostructure, assisting multiple adsorption sites and various hydrogen interaction pathways. Alloying effects, internal strain, and the presence of both GaO and ZnO regions within the nanosheets further contribute to enhanced sensitivity, selectivity, and stability. © 2024 Hydrogen Energy Publications LLC

关键词： Zinc oxide

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A Comprehensive Review Analysis on Osteoarthritis Using Deep Learning and Machine Learning Technologies for Arthritis Detection 7

A Comprehensive Review Analysis on Osteoarthritis Using Deep...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Thomas, Tinu Prince Mary, S. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India St. Joseph's College of Engineering and Technology Department of Electronics and Computer Engineering Palai India

ISBN: (纸本)9798350372816

Osteoarthritis is a prevalent degenerative knee joint disease, causing considerable pain and mobility issues, thus impacting the independence and quality of life of millions. Deep learning and Machine Learning techniques have emerged as promising tools for enhancing the diagnosis, prognosis, and treatment of osteoarthritis (OA). These technologies allow for the extraction of valuable insights from medical images, clinical data, and genetic information, thereby improving patient care and management strategies. By employing advanced algorithms, healthcare professionals can improve early detection, and treatment plans, and accurately predict disease progression, ultimately leading to better outcomes for OA patients. This paper proposes a comprehensive review of various DL-based and ML-based methods for OA, spanning the years 2020 to 2024. It aims to provide valuable insights for researchers interested in exploring different models and potentially inspiring the development of integrated approaches for even more effective OA detection. © 2024 IEEE.

关键词： Joints (anatomy)

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Optimizing Crop Health: Evaluating CNN Performance in Plant Disease Detection 5th

Optimizing Crop Health: Evaluating CNN Performance in Plant ...

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

作者： Charan Tejas, Parimi Murali Sriram Ganapathi, Marnala Mahesh Babu, Parepalli Surya Sai, Kosuru Amulya, Godi Bonthu, Sridevi Department of Computer Science and Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031770807

Plant diseases represent a significant challenge in agriculture, significantly impacting crop yield. Addressing this issue is imperative to ensure agricultural productivity. Convolutional Neural Networks (CNNs) have emerged as a promising solution for image-based disease detection. Detecting diseases in their early stages using CNN models can potentially enhance crop yield for farmers. Given the multi-class nature of most plant diseases, we conducted a comprehensive comparative study of CNNs to assess their effectiveness. Our study compared three CNN models: AlexNet, VGG-19, and ResNet18. ResNet18 demonstrated superior performance, achieving a validation accuracy of 95.68%. Furthermore, we developed a web application to enable farmers to utilize the trained model effectively. © ICST institute for computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Convolutional neural networks

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Advanced Fraud Detection in Smart Grids Using Deep Neural Networks

Advanced Fraud Detection in Smart Grids Using Deep Neural Ne...

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2025 International Conference on Advances in Renewable Energy and Electric Vehicles, AREEV 2025

作者： Teja, Pallapothu Sasank Kumar, Pane Pavan Sankari, M. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600119 India

ISBN: (纸本)9798331529574

Fraud detection in smart grids is critical to ensure reliable and efficient energy distribution, preventing significant financial losses and maintaining grid stability. This project presents an advanced fraud detection system utilizing deep neural networks (DNN) to automatically identify and prevent fraudulent activities such as energy theft, meter tampering, and unauthorized energy consumption. The DNN model processes vast amounts of data generated by smart meters and other grid sensors, learning complex patterns and anomalies that indicate potential fraud. By integrating feature extraction techniques and real-time monitoring, the system is designed for scalability and accuracy, capable of handling high-dimensional data from modern energy grids. Additionally, this solution improves the grid's resilience and operational efficiency by enabling proactive fraud detection and reducing false positives, helping utility companies safeguard their assets. © 2025 IEEE.

关键词： Smart power grids

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Exam Invigilation Scheduler 7

Exam Invigilation Scheduler

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Vadlamudi, Anith Kumar Thigulla, Sushma Veeramachaneni, Meghana Vangimalli, Shiva Krishna Chaitanya Reddy Pondugala, Bhaskara Rao B V Raju Institute of Technology Computer Science and Engineering Narsapur India B V Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India

ISBN: (纸本)9798350372816

The process of room allocation for faculty during examinations has been a traditionally manual process, but now, we are embarking on the journey of developing an application centered on room allocation for faculty online. The current manual method involves significant time and complexity in assigning classes without any confusion. Additionally, a considerable amount of paperwork is required for manual allocation, demanding substantial efforts. To counter these challenges, we are presenting the creation of a web application known as the Exam Invigilation Scheduler. In this web application, the admin holds the key to managing all data. Through this application, the admin can efficiently schedule invigilation by allocating tables based on the provided data. Preparation for the invigilation schedule entails storing various details in the system database, including information regarding branches, faculty members, number of rooms, faculty email addresses, and other necessary information. To utilize this application, the admin must log in and, if desiring to arrange exam invigilation schedules, provide details such as the examination date, the number of branches involved in the exam, the faculty availability on the examination day, and the available number of classrooms. Based on the admin's input, the system will construct the exam invigilation timetable. Once the invigilation table is prepared, emails will be dispatched to the respective faculty members, specifying their allocated room number, date, time, and examination details. The final invigilation table serves as a critical tool for conducting examinations. Through this web application, automatic class allocation during examinations is achieved without manual intervention, effectively reducing paperwork, saving time, and preventing confusion among faculty members during exam invigilation. © 2024 IEEE.

关键词： Websites

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Recent Advancements in Cybersecurity: A Literature Review 7th

Recent Advancements in Cybersecurity: A Literature Review

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Lakshman Naik, R. Jain, Sourabh Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

ISBN: (纸本)9789819609239

Smart energy systems are improving internet connectivity, enhancing cybersecurity concerns. Traditional systems use trust authentication and information security measures to protect against cyber threats. However, distributed control systems (DCS) require securing to ensure secure and risk-free operation. Recent advancements in information security have led to a literature survey to highlight the adopted advancements and challenges in cybersecurity in information systems. This dynamic field continually evolves to address emerging threats and challenges. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed parameter control systems

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Improving Bias in Facial Attribute Classification: A Combined Impact of KL Divergence-Induced Loss Function and Dual Attention 27th

Improving Bias in Facial Attribute Classification: A Combin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Patel, Shweta Kisku, Dakshina Ranjan Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur713209 India

ISBN: (纸本)9783031876592

Ensuring that AI-based facial recognition systems produce fair predictions and work equally well across all demographic groups is crucial. Earlier systems often exhibited demographic bias, particularly in gender and racial classification, with lower accuracy for women and individuals with darker skin tones. To tackle this issue and promote fairness in facial recognition, researchers have introduced several bias-mitigation techniques for gender classification and related algorithms. However, many challenges remain, such as data diversity, balancing fairness with accuracy, disparity, and bias measurement. This paper presents a method using a dual attention mechanism with a pre-trained Inception-ResNet V1 model, enhanced by KL-divergence regularization and a cross-entropy loss function. This approach reduces bias while improving accuracy and computational efficiency through transfer learning. The experimental results show significant improvements in both fairness and classification accuracy, providing promising advances in addressing bias and enhancing the reliability of facial recognition systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Face recognition

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Efficient Scheduling of Real-Time Messages over Heterogeneous in-Vehicle Network Domains 100

Efficient Scheduling of Real-Time Messages over Heterogeneou...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Chowdary Kota, Ram Mohan Mayank, Jaishree Sarkar, Amab Indian Institute of Information Technology Design and Manufacturing Department of Computer Science and Engineering Kancheepuram Chennai India Indian Institute of Technology Kharagpur Advanced Technology Development Centre Department of Computer Science and Engineering India

ISBN: (纸本)9798331517786

Time Sensitive Ethernet is quickly emerging to be the preferred choice as the backbone network for in-vehicle communication, due to its high bandwidth, reliability, scalability, backward compatibility, and support for diverse traffic types. However, individual real-time control subsystems which may need to communicate via this backbone network, may be driven by non-Ethernet protocols like CAN, FlexRay, etc. Beyond ad hoc approaches, this highlights the need for a systematic message scheduling mechanism that enables seamless real-time message transmission over heterogeneous network domains. In this context, this work proposes a formal SMT (Satisfiability Modulo Theories) formulation for statically scheduling a set of persistent, periodic real-time messages. While the sources and destinations of these messages are in different CAN-network domains, the message transmissions must be conducted via an intermediate Time Sensitive Networking (TSN;IEEE802.1Q) backbone. Although, this formal strategy achieves high resource utilization while guaranteeing end-to-end timeliness, it is computationally exponential in nature with overheads becoming prohibitively expensive even for moderate problem sizes. Hence, we propose a lower overhead scheduling strategy called CAN-THER which can deliver efficient and quick solutions even for large problem sizes. The proposed scheduling strategies have been analyzed and compared using extensive simulation based experiments. Results reveal that CAN-THER is able to achieve performance that is up to 90% of the SMT formulation, while producing solutions at speeds that are approximately 104 times faster for problems having up to 12 CAN flows and 15 TSN switches. © 2024 IEEE.

关键词： Internet protocols

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nits_teja_srikar at GenAI Detection Task 2: Distinguishing Human and AI-Generated Essays Using Machine Learning and Transformer Models 1

nits_teja_srikar at GenAI Detection Task 2: Distinguishing H...

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1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Teja, L.D.M.S. Sai Yadagiri, Annepaka Vardhan, M. Srikar Pakray, Partha Department of Computer Science & Engineering National Institute of Technology Assam Silchar788010 India

ISBN: (纸本)9798891762053

This paper presents models to differentiate between human-written and AI-generated essays, addressing challenges posed by advanced AI models like ChatGPT and Claude. Using a structured dataset, we fine-tune multiple machine learning models, including XGBoost and Logistic Regression, along with ensemble learning and k-fold cross-validation. The dataset is processed through text cleaning, lemmatization, stemming, and part-of-speech tagging, followed by TF-IDF vectorization before training. Our team nits_teja_srikar achieves high accuracy, with DistilBERT performing at 77.3% accuracy, standing at 20th position for English, and XLM-RoBERTa excelling in Arabic at 92.2%, standing at 14th position in the official leaderboard, demonstrating the model’s potential for real-world applications. © 2025 International Conference on Computational Linguistics.

关键词： Computational linguistics

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Unified Approach Integrating Machine Learning Algorithms for Detection of e-Mail Phishing Attacks 1

Unified Approach Integrating Machine Learning Algorithms for...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Mathur, Abeer Dubey, Abhinav Sudhakar Mahara, Pranav Singh Gandhi, Usha Devi School of Computer Science & Engineering Vellore Institute of Technology Department of Software Systems Vellore India School of Computer Science Engineering & Information Systems Vellore Institute of Technology Department of Information Technology Vellore India

ISBN: (纸本)9798350353648

With the ever-rising risk of phishing attacks, which capitalize on vulnerable human behavior in the contemporary digital space, requires new cybersecurity methods. This literary work contributes to the solution by novel incorporation of three techniques: Support Vector Machine (SVM), Natural Language Processing (NLP) and Random Forest (RF). Utilizing NLP for deep analysis of text in phishing e-mails, SVM for strong categorization and Random Forest's decision making by group helps in improving detection. This combination of language and pattern analysis along with group learning builds a fortified system. This approach differs from prior work on a variety of factors such as reliability, security, integrity, scalability, interpretability, transparency, and robustness. Thorough evaluation on a reliable dataset aimed to demonstrate the method's effectiveness, which resulted in 98.7 % of accuracy in detection. This majorly aims at showcasing the potential of ensemble techniques to combat against cyber-attacks like phishing for laying a strong foundation for a secure digital environment. © 2024 IEEE.

关键词： Phishing

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