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IEEE Open Journal on Immersive Displays 2024年 1卷 187-203页

作者： Zhang, Yuqing Wang, Xu Zhang, Sixin Wang, Chunxiu Zhang, Min Peking University School of Electronic and Computer Engineering Shenzhen518055 China Hong Kong Applied Science and Technology Research Institute Hong Kong

Future flexible intelligent displays are driving demands for greater flexibility, higher resolution, faster refresh rates, and enhanced functionality. This review mainly focuses on recent advances and ongoing challenges in flexible thin-film transistors (TFTs) based on low-temperature polycrystalline silicon (LTPS), amorphous oxide semiconductor (AOS), and low-temperature polysilicon and oxide (LTPO) for flexible backplane technologies. Furthermore, the developments of process and function integration techniques, including device scaling, device stacking, as well as functional circuits and applications, are discussed from the perspective of innovation and significance. Besides, the integration of intelligent systems is briefly introduced, emphasizing the role of synaptic transistors, including metal oxide and carbon nanotube-based ones, in real-time data processing and sensing functions. By providing insights into the status and future directions, this review provides a comprehensive perspective and discussion on the development of next-generation flexible intelligent display technologies. © 2024 IEEE.

关键词： Thin film transistors

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Advancements in Predictive Techniques and Effective Management Strategies for Dengue Fever: A Comprehensive Review 8

Advancements in Predictive Techniques and Effective Manageme...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Muthuselvi, P. Devi, V. Anusuya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Krishnankoil626126 India

ISBN: (纸本)9798350376425

The dengue virus is the source of the arboviral illness dengue. Wide-ranging medical symptoms, ranging from mild dengue fever to potentially lethal diseases like dengue shock syndrome (DSS), are caused by symptomatic dengue infection. The term intelligent dengue fever prediction and management describes the application of cutting-edge computational techniques, including data analytics, to forecast outbreaks, pinpoint high-risk regions, and maximize resource allocation for efficient dengue fever treatment and prevention. Dengue fever is a virus spread by mosquitoes, and the effect on public health is minimized. Predicting and managing dengue fever has limitations, poor forecasting models, inadequate public health facilities, difficulties controlling vectors, and restricted access to treatment in areas where endemic diseases are present. In this review to reduce the impact of illness through early identification, efficient vector control techniques are to predict dengue fever. Here This Work utilized, the epidemiology of dengue fever, dengue hemorrhagic fever (DHF), Pathogenesis of dengue hemorrhagic fever, and clinical Manifestations divided into dengue with warning symptoms, dengue without warning symptoms, severe dengue and using system is intelligent systems in dengue prediction, intelligent dengue management strategies. The early diagnosis and efficient mitigation of dengue fever might be significantly enhanced by using proactive management techniques and intelligent prediction models. This might minimize the impact of Dengue Fever on healthcare systems and public health. © 2024 IEEE.

关键词： Prediction models

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AI-Integrated Sensor Data Analytics for Real-Time Decision-Making in Wireless Sensor Networks 2

AI-Integrated Sensor Data Analytics for Real-Time Decision-M...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Wasim Haidar, S.K. Wilfred Blessing, N.R. Chaubey, Sudhakar Kumar Mehmood, Zahid Kavitha, S.J. Sutherlin Subitha, G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Oman Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India

ISBN: (纸本)9798331505745

The study compares the performance of Wireless Sensor Networks (WSNs) with 20, 30, 40, and 50 nodes in real-time AI-enhanced sensor analytics using the AODV routing protocol. All configurations showed a consistent Packet Delivery Ratio (PDR) of 100%. However, the end-to-end delay increased slightly from 0.2 sec (20 nodes) to 0.3 sec (30, 40, 50 nodes), indicating that larger networks result in longer paths. Throughput decreased from 2560 bps at 20 nodes to 1706.67 bps in larger configurations, probable due to network congestion. Energy efficiency also decreased as the number of nodes increased, suggesting higher energy consumption per transmission. © 2025 IEEE.

关键词： Sensor nodes

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Identification and Detection of Leaf Miner, Pests Infestation in Cucurbitaceae Family in Real Time Infield Scenarios using YOLOv5s Object Detection Model 18

Identification and Detection of Leaf Miner, Pests Infestatio...

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18th INDIAcom;11th International Conference on computing for Sustainable Global Development, INDIACom 2024

作者： Keerthi Prasad, M.A. Shobha Rani, N. Sangamesha, M.A. Vinay, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India The National Institute of Engineering Department of Chemistry Mysuru India The National Institute of Engineering Department of Computer Science Mysuru India

ISBN: (纸本)9789380544519

This study contributes to technological developments in precision agriculture by developing a YOLOv5sbased model with a high F1 score and mAP, highlighting its potential for real-world use in wax gourd plants. In precision agriculture, early disease identification is crucial for effective harvesting. The present research focuses on the creation and evaluation of a YOLOv5s-based model for detecting pests and leaf miner infections in wax gourd plants in real time infield conditions at various stages of plant growth. Using a dataset of over 2000 samples collected in a variety of in-field settings, the model was trained for 200 epochs with a low learning rate (0.00001) and a batch size of 32. The achieved F1 score of 0.96 demonstrates the model's remarkable combination of precision and recall, ensuring precise disease detection. Furthermore, a mean Average Precision (mAP) greater than 0.45 demonstrates the model's proficiency in precise leaf localization and overall detection ability. The model's ability to detect numerous occurrences of disease in different leaves simultaneously is a crucial strength, reflecting the complexities common in real-world agricultural scenarios. The dataset's quantity and diversity help the model be robust and generalizable across diverse infection types and growth stages. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： crop health monitoring Data-driven agriculture object detection precision agriculture Smart agriculture sustainable farming

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Federated Learning for Enhanced Intrusion Detection in Smart City Environments 18

Federated Learning for Enhanced Intrusion Detection in Smart...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798331508692

The integration of IoT devices in smart cities enhances urban infrastructure, services, and governance but also introduces significant cybersecurity challenges. Traditional centralized Intrusion Detection Systems (IDS) face several issues, including data privacy concerns and high-power consumption due to centralized data processing. These challenges increase the risks of unauthorized access, data breaches, and privacy violations, undermining user trust and compliance with privacy regulations. Additionally, the centralization of data and processing leads to higher power consumption, making these systems less sustainable for widespread deployment in smart cities. This research addresses these issues by proposing a Federated Learning (FL)based intrusion detection framework for smart cities. FL enables collaborative and privacy-preserving model training across distributed IoT devices, mitigating the need to share sensitive data centrally. By aggregating local model updates, FL ensures data privacy and distributes the computational workload, significantly reducing power consumption compared to traditional centralized systems. The proposed model leverages advanced AI techniques and is trained using the IoTID20 dataset. The Flower framework, utilizing the FedAvg algorithm, facilitates the federated learning process. Our experimental results demonstrate that the global model achieves 98% accuracy, with individual clients achieving accuracies of around 85% to 98%. This approach provides continuous learning mechanisms, anomaly detection, and ensemble learning capabilities, enhancing the resilience of federated intrusion detection systems against emerging threats and adversarial attacks. This research systematically investigates the application of federated learning for intrusion detection in smart city networks, addressing key challenges and advancing the state-of-the-art in decentralized cybersecurity solutions. The proposed framework offers a robust, scalable, and privacyco

关键词： Federated learning

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SeerahGPT: Retrieval Augmented Generation based Large Language Model 18

SeerahGPT: Retrieval Augmented Generation based Large Langua...

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18th International Conference on Open Source Systems and Technologies, ICOSST 2024

作者： Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Department of Computer Science and Software Engineering Jinnah Univesity for Women Karachi Pakistan

ISBN: (纸本)9798331508692

Large Language Models(LLMs) have become widely recognized in recent years for their exceptional performance in language generation capabilities. As a result, an unprecedented rise is seen in its use cases in various domains specifically involving Natural Language Processing(NLP). These models however perform suboptimally when exploited in the field of studies where authenticity of the generated content is a critical aspect. One such domain is usage of LLM for exploration of the life of Prophet Muhammad S.A.W(commonly referred to as Seerah). It is of utmost significance to ensure the authenticity and reliability in the sources used and reported by the LLM due to the sensitive nature of the domain. The contemporary LLMs, however, lack the explainability in their response due to their inherent black-box nature. In our study, we have presented a novel LLM named SeerahGPT that addresses this challenge with the help of retrieval-augmented generation (RAG). This technique enables the model to utilize both parametric and nonparametric memories for generating response of queries. Our model, built on the Llama-2-7b architecture, employs Sentence Transformer embedding to effectively retrieve relevant information. The model's capabilities are augmented by integrating it with a corpus having Islamic texts such as the Quranic translation and Hadith collections, and historical accounts. The model's performance is benchmarked against its base model using both quantitative and qualitative metrics. The comparative analysis with Llama-2-7b revealed that SeerahGPT incorporation with external knowledge sources, provided more authentic and verifiable responses, despite the others exhibiting greater fluency. Performance metrics such as BLEU, ROUGE, and METEOR indicated SeerahGPT's better accuracy and contextual handling. This study paves way for analysis of such sensitive domains in more efficient way that can be utilized in other complex domains such as Islamic theology and Fiqh or legal

关键词： Natural language processing systems

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Innovative Video Classification Method based on Deep Learning Approach

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Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham science Publishers.

关键词： Convolutional neural networks

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A Hybrid Relational Approach Toward Stock Price Prediction and Profitability

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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Closest Celestial Body Search Using KD Trees

Closest Celestial Body Search Using KD Trees

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Priya Ponnusamy, P. Shabariram, C.P. Umayal, V.R. Susmeta, A. PSG Institute of Technology and Applied Research Neelambur Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350348217

A Binary Search Tree is expanded into a KD Tree to handle the multi-dimensional key searches. A discriminator makes the KD tree different from the BST. This discriminator will take branching decisions at every level based on the key search. It can handle multi-dimensional coordinate object searching. K-d trees enable O(klogn) lookup times for the k nearest points to some point x. This is extremely useful, especially in cases where an O(n) lookup time is intractable already. In this work, a kd tree has been created based on how a celestial body is positioned in relation to the sun. It aims at finding the minimum in a particular dimension, searching for a celestial body and its nearest neighbor. © 2023 IEEE.

关键词： Nearest neighbor search

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