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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Harshath, S.M. Kongu Engineering College Department of Computer Technology Tamilnadu Perundurai India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Kongu Engineering College MSc Software Systems Erode India

ISBN: (纸本)9798350379945

Unpredictable fruit and vegetable prices create significant challenges for farmer livelihoods. This research proposes an innovative approach using recurrent neural networks (RNNs) to predict both minimum and maximum prices. By analyzing historical market data, these models can forecast potential price ranges, empowering farmers to make informed decisions about planting, harvesting, and sales strategies. This enhanced foresight helps farmers navigate market volatility and improve income stability. © 2024 IEEE.

关键词： Vegetables

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TiTDet: A tiny text detector with scale-sensitive loss and effective fusion factor

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Journal of Intelligent and Fuzzy Systems 2024年第1期1卷 11367页

作者： Wei, Guangcun Fu, Jihua Pan, Zhifei Fang, Qingge Zhang, Zhi College of Intelligent Equipment Shandong University of Science and Technology Shandong Taian China College of Computer Sicence and Engineering Shandong University of Science and Technology Shandong Qingdao China Taian Synergy Software Ltd. Shandong Taian China

The text in natural scenes is often smaller compared to artificially designed text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Due to the small proportion of pixels, low resolution, less semantic information, and susceptibility to complex scenes, tiny text detection often results in many missed detections. To address this issue, this paper draws inspiration from small object detection methods and proposes TiTDet, a detection algorithm more suitable for tiny text. Firstly, this paper incorporates a context extraction module and an attention-guided module. These modules guide contextual information learning through a self attention mechanism, while eliminating the possible negative impact caused by redundant information. Regarding multi-scale feature fusion, this paper proposes a fine-grained effective fusion factor, making the fusion process emphasize small object learning more and highlight the feature expression of tiny texts. In terms of post-processing, this paper proposes a differentiable binarization module, incorporating the binarization process into model training. Leveraging the implicit information in the data to drive model improvement can enhance the post-processing effect. Lastly, this paper proposes a scale-sensitive loss, which can handle tiny texts more fairly, fully considering the positional relationship between the predicted and real regions, and better guiding the model training. This paper proves that TiTDet exhibits high sensitivity and accuracy in detecting tiny texts, achieving an 86.0% F1-score on ICDAR2015. The paper also compares the superiority of the method on CTW1500 and Total-Text. © 2024 - IOS Press. All rights reserved.

关键词： Pixels

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AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dataset Generation and Automated Code Production 8

AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dat...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： El-Gnainy, Nada Shanouda, Mira Essam, Ahmed Ibrahim, Anas Abdallah William, John Elsharkawy, Mariam Moustafa, Passant Ahmed Al Ansary, Mohamed Mahmoud, Hossam Moro, Ahmed Salama, Cherif Siemens Digital Industries Software Cairo Egypt The American University in Cairo Department of Computer Science and Engineering Egypt Ain Shams University Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350362138

This research presents a novel approach to stream-line the configuration of AUTOSAR (Automotive Open System Architecture) modules using Artificial Intelligence (AI)-based tools. Traditional methods of generating AUTOSAR-compliant ARXML files are time-consuming and require meticulous man-ual input, posing significant challenges for developers. In this study, an AI-driven methodology is introduced to address these challenges. By mapping English descriptions to YAML through a Python script and subsequently converting YAML to Autosar XML (ARXML), this approach conserves tokens and significantly accelerates the AI training process. The AI model, trained on a dataset of 12,000 samples and tested on 3,000 unseen inputs, achieved a 100% accuracy rate in converting descriptions. Additionally, a comprehensive configurator tool and a code generator were developed to facilitate seamless integration and deployment of the generated configurations. The proposed solution not only reduces manual effort but also increases reliability in AUTOSAR configuration, offering a practical tool for developers in the automotive software industry. © 2024 IEEE.

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Enhancing breast cancer histopathological image classification using attention-based high order covariance pooling

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Neural Computing and Applications 2024年第36期36卷 23275-23293页

作者： Waqas, Muhammad Ahmed, Amr Maul, Tomas Liao, Iman Yi School of Computer Science Faculty of Science and Engineering University of Nottingham Malaysia Campus Selangor Dar ul Ehsan Semenyih43500 Malaysia Department of Software Engineering University of Malakand Khyber Pakhtunkhwa Lower Dir Chakdara18800 Pakistan Department of Computer Science Edge Hill University Lancashire L39 4QP United Kingdom School of Computer Science Faculty of Science University of Nottingham NottinghamNG8 1BB United Kingdom

Breast cancer, the most common cancer affecting female patients, presents serious challenges for proper detection. Although computer-aided diagnostic techniques have progressed, their accuracy and efficacy remain limited. To overcome these challenges, we introduce DHA-Net, a new deep learning system that combines an effective attention module (EAM) and a high-order pooling layer with a ResNet-18 backbone. DHA-Net is tested using three well-known breast cancer histopathology image datasets: BreakHis, BACH2018, and a closely related Kaggle-Breast cancer histopathology dataset. Our experiments show that DHA-Net not only improves on existing state-of-the-art approaches, but significantly outperforms them in classifying breast cancer images. This work emphasizes the novel combination of an EAM with high-order pooling, demonstrating DHA-Net’s potential to improve diagnostic accuracy and serve as a more effective tool for medical imaging applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Lung cancer

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Fractional Processing Based Adaptive Beamforming Algorithm

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computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： Syed Asghar Ali Shah Tariqullah Jan Syed Muslim Shah Ruhul Amin Khalil Ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square

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A Secure and Efficient Framework for Multi-Round Data Trading Over the Internet of Artificially Intelligent Things

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IEEE Internet of Things Magazine 2022年第1期5卷 119-124页

作者： Zheng, Xu Zhang, Lizong Hui, Bei Tian, Ling Cai, Zhipeng School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Information and Software Engineering University of Electronic Science and Technology of China China Georgia State University Department of Computer Science United States

IoT data trading has greatly benefited the popularization of both the Internet of Things (IoT) and Artificial Intelligence of Things (AIoT). Current solutions mainly treat the dataset owned by each device as a commodity and focus on maintaining a data market. Meanwhile, inspired by recent achievements in distributed machine learning and federated learning, AIoT can actually realize its services by retaining the data locally on devices and only exchanging model parameters. However, existing IoT data trading methods fail to meet this secure and efficient process. Therefore, this article proposes a novel framework for data trading over AIoT. It allows the service provider to trade on deep learning model training instead of purchasing the full datasets. Key factors for the security issues and data pricing are addressed for the framework, including the design of the learning parameters and the comprehensive privacy concerns. Then a novel data trading strategy is proposed for the service provider to jointly maintain the model performance and the budget consumption. Evaluation results reveal the improvements on the model performance. Finally, some open problems in the design of data trading in AIoTs are discussed. © 2018 IEEE.

关键词： Internet of things

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A Transfer Learning-Based Framework for Enhanced Classification of Perceived Mental Stress Using EEG Spectrograms

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IEEE Access 2025年 13卷 89266-89286页

作者： Khan, Sheharyar Noorani, Sadam Hussain Frnda, Jaroslav Rauf, Usman Arsalan, Aamir Saeed, Sanay Muhammad Umar University of Engineering and Technology Department of Computer Engineering Taxila47050 Pakistan HITEC University Department of Computer Science Taxila47050 Pakistan University of Žilina Faculty of Operation and Economics of Transport and Communications Department of Quantitative Methods and Economic Informatics Žilina01026 Slovakia Fatima Jinnah Women University Department of Software Engineering Rawalpindi46000 Pakistan

Stress is a significant health concern, impacting both physical and mental well-being. Prolonged exposure to stress can lead to numerous physical health issues, including cardiovascular diseases, and a weakened immune system. This study presents a novel methodology for classifying perceived mental stress using electroencephalography (EEG) signals. By utilizing the publicly available Leipzig Study for Mind-Body-Emotion Interactions dataset, we analyze EEG data collected from 53 participants over a 7-minute resting-state duration. Our approach involves transforming EEG signals into spectrograms using the Short-Time Fourier Transform (STFT), resulting in a time-frequency representation of the input signals. We employ transfer learning to fine-tune three pre-trained deep neural networks i.e., ResNet50, EfficientNetB0, and DenseNet121 for classifying stress into two and three levels. Our findings demonstrate that the ResNet50 model achieves superior classification accuracies of 95.80% and 86.02% for two and three-level stress classification, respectively, outperforming existing state-of-the-art methods. This study is the first to utilize STFT-generated spectrograms and transfer learning for perceived stress classification, highlighting the efficacy of deep learning techniques in quantifying perceived mental stress through non-invasive EEG recordings. Our results indicate that the proposed method can significantly enhance the accuracy of stress classification frameworks, offering potential improvements in mental health assessment and intervention strategies. © 2013 IEEE.

关键词： Noninvasive medical procedures

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Mapping Explicit and Implicit Discourse Relations between the RST-DT and the PDTB 3.0

Mapping Explicit and Implicit Discourse Relations between th...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Costa, Nelson Filipe Sheikh, Nadia Kosseim, Leila Laboratory Department of Computer Science and Software Engineering Concordia University MontréalQC Canada

ISBN: (纸本)9789544520922

In this paper we propose a first empirical mapping between the RST-DT and the PDTB 3.0. We provide an original algorithm which allows the mapping of 6,510 (80.0%) explicit and implicit discourse relations between the overlapping articles of the RST-DT and PDTB 3.0 discourse annotated corpora. Results of the mapping show that while it is easier to align segments of implicit discourse relations, the mapping obtained between the aligned explicit discourse relations is more unambiguous. © 2023 Incoma Ltd. All rights reserved.

关键词： Mapping

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A Novel Secure Scan Design Based on Delayed Physical Unclonable Function

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computers, Materials & Continua 2023年第3期74卷 6605-6622页

作者： Weizheng Wang Xingxing Gong Xiangqi Wang Gwang-jun Kim Fayez Alqahtani Amr Tolba School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China College of Information Science and Engineering Hunan Women’s UniversityChangsha410138China College of Mathematics and Computational Science Hunan First Normal UniversityChangsha410138China Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia

The advanced integrated circuits have been widely used in various situations including the Internet of Things,wireless communication,*** its manufacturing process exists unreliability,so cryptographic chips must be rigorously *** to scan testing provides high test coverage,it is applied to the testing of cryptographic integrated ***,while providing good controllability and observability,it also provides attackers with a backdoor to steal *** the text,a novel protection scheme is put forward to resist scan-based attacks,in which we first use the responses generated by a strong physical unclonable function circuit to solidify fuseantifuse structures in a non-linear shift register(NLSR),then determine the scan input code according to the configuration of the fuse-antifuse structures and the styles of connection between the NLSR cells and the scan *** the key is right,the chip can be tested normally;otherwise,the data in the scan chain cannot be propagated normally,it is also impossible for illegal users to derive the desired scan *** proposed technique not only enhances the security of cryptographic chips,but also incurs acceptable overhead.

关键词： Cryptographic chips scan testing scan-based attacks hardware security PUF

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Increasing e-Health systems security and availability by using noSQL databases 23

Increasing e-Health systems security and availability by usi...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Contasel, Cristian Palacean, Alexandru-Viorel Turcanu, Dinu Stoica, Victor-Valentin National University of Science and Technology POLITEHNICA Bucharest Department of Computer Science and Engineering Bucharest Romania Technical University of Moldova Department of Software Engineering and Automatics Chişinǎu Moldova

ISBN: (纸本)9798331540388

As e-Health systems become more widely used starting with COVID-19 pandemic, the amount of data they collect increases significantly. The volume, diversity, and unpredictability of patient data necessitate distinct storage types for long-Term preservation. As a result, one of the primary problems with e-Health systems is making patient data accessible and combined into different styles so that AI algorithms can analyze it. This paper presents an improved persistence system that utilizes NoSQL databases such as Couchbase. The aim of this system is to improve overall system performance while reducing security risks associated with data availability, confidentiality, and integrity. © 2024 IEEE.

关键词： Electronic health record

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