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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： D Venkata Lakshmi R Shyama S Anila Srichandana Abbineni Sajid Abd Al Aqeel Al-Hilali School of Computer Science and Engineering VIT AP University Andhra Pradesh India Department of Computer science and Engineering Adi Shankara Institute of Engineering and Technology Kalady Ernakulam Kerala Department of ET-CSE (DATA SCIENCE) CVR College of Engineering Hyderabad Khidhir Abdullah College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The danger environment for the Internet of Things (IoT) has grown as a result of the widespread use of internet-connected devices, and malware detection is becoming increasingly difficult. Feature extraction, selection, and classification phases of machine learning turn it into a potent weapon against many dangers in Internet of Things environments. Particularly, in order to decrease feature size and speed up detection, feature selection is critical. Three approaches are usually used by researchers: feature reduction, feature selection, or hybrid approaches. This study examines feature selection and feature reduction for IoT malware identification using deep learning (DL) and machine learning (ML) techniques. According to the findings, feature selection by itself can lead to an accuracy rate of $94 \%$. This demonstrates that when it comes to recognizing IoT malware, feature selection works better than feature reduction.

关键词： Deep learning Accuracy Weapons Feature extraction Malware Internet of Things

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Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

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computer Systems science & Engineering 2023年第10期47卷 873-889页

作者： Mohamed Yacin Sikkandar K.Hemalatha M.Subashree S.Srinivasan Seifedine Kadry Jungeun Kim Keejun Han Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Biomedical Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Institute of Biomedical Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Recently,COVID-19 has posed a challenging threat to researchers,scientists,healthcare professionals,and administrations over the globe,from its diagnosis to its *** researchers are making persistent efforts to derive probable solutions formanaging the pandemic in their *** of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography(CT)*** the same time,the recent advances in machine learning(ML)and deep learning(DL)models show promising results in medical ***,the convolutional neural network(CNN)model can be applied to identifying abnormalities on chest *** the epidemic of COVID-19,much research is led on processing the data compared with DL techniques,particularly *** study develops an improved fruit fly optimization with a deep learning-enabled fusion(IFFO-DLEF)model for COVID-19 detection and *** major intention of the IFFO-DLEF model is to investigate the presence or absence of *** do so,the presented IFFODLEF model applies image pre-processing at the initial *** addition,the ensemble of three DL models such as DenseNet169,EfficientNet,and ResNet50,are used for feature ***,the IFFO algorithm with a multilayer perceptron(MLP)classification model is utilized to identify and classify *** parameter optimization of the MLP approach utilizing the IFFO technique helps in accomplishing enhanced classification *** experimental result analysis of the IFFO-DLEF model carried out on the CXR image database portrayed the better performance of the presented IFFO-DLEF model over recent approaches.

关键词： COVID-19 computer vision deep learning image classification fusion model

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Automatic Diagnosis and Grading of Diabetic Retinopathy using Bat Optimization Algorithm-Refined Deep Residual Network

Automatic Diagnosis and Grading of Diabetic Retinopathy usin...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： B. Haritha Lakshmi Venkata Ramana Kaneti R S Soundariya Sanjib Kumar Nayak Rajesh N IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of Computer Technology Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering (Data Science) CMR Technical Campus Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

Diabetic Retinopathy (DR) is one of the most severe sight-threatening disorders resulting from diabetes, and can eventually lead to blindness and visual impairment. Early detection and medical therapy can assist in controlling and preventing DR progression. However, manual grading is extremely difficult and time-consuming because of the retina’s complicated structure. In this research, the Bat Optimization Algorithm- Refined Deep Residual Network (BOA-RDRN) is proposed for automatic diagnosis and grading of DR. Initially, the DIARETDB0 dataset is employed for the proposed method, and then pre-processing is performed for image denoising which eliminates noise. The Attention-based Fusion Network (AFU-Net) is used to segment the lesion region. The Grey-Level Co-occurrence Matrix (GLCM) and Local Ternary Pattern (LTP) are used to extract the features and BOA is employed to find an optimal subset of features in retinal images. Finally, the RDRN is utilized to classify the anomalies as normal or abnormal. The BOA-RDRN achieves an accuracy of 99.42% compared to the existing methods such as Tunicate Swarm Spider Monkey optimization-based Refined Deep Residual Network (TSSMO-RDRN), Weighted Kernel Fuzzy C-Means Clustering and Dilation-Based Function (WKFM-DBF), Improved Harris Hawk Optimization-based Convolution Neural Network (IHHO-CNN), and Enhanced Fuzzy C-Means Clustering-Resnet 152 respectively.

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Efficient 2D Simulators for Deep-Reinforcement-Learning-based Training of Navigation Approaches

Efficient 2D Simulators for Deep-Reinforcement-Learning-base...

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International Conference on Ubiquitous Robots (UR)

作者： Huajian Zeng Linh Kästner Jens Lambrecht The Chair of Robotics Artificial Intelligence and Real-time Systems Technical University Munich (TUM) Germany The Chair of Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Institute of Technology Berlin Germany

In recent years, Deep Reinforcement Learning (DRL) has emerged as a competitive approach for mobile robot navigation. However, training DRL agents often comes at the cost of difficult and tedious training procedures in which powerful hardware is required to conduct oftentimes long training runs. Especially, for complex environments, this proves to be a major bottleneck for widespread adoption of DRL approaches into industries. In this paper we integrate an efficient 2D simulator into the Arena-Rosnav framework of our previous work as an alternative simulation platform to train and develop DRL agents. Therefore, we utilized the provided API to integrate necessary components into the ecosystem of Arena-Rosnav. We evaluated our simulator by training a DRL agent within that platform and compared the training and navigational performance against the baseline 2D simulator Flatland, which is the default simulating platform of Arena-Rosnav. Results demonstrate that using our Arena2D simulator results in substantially faster training times and in some scenarios better agents. This proves to be an important step towards resource-efficient DRL training, which accelerates training times and improve the development cycle of DRL agents for navigation tasks. We made our simulator openly available at https://***/Arena-Rosnav/arena2d.

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Chinese Emergency Event Extraction Based on Contrastive Learning with Event Semantic Features

Chinese Emergency Event Extraction Based on Contrastive Lear...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Xinyi Sheng Jinguang Gu Youcheng Yan Fangfang Xu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan China The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China

Extracting emergency events from a large amount of unstructured information is essential for improving early warning and emergency response. Existing event extraction methods for specialist fields often rely on well-designed syntactic dependencies or external knowledge graphs, which can provide high accuracy in their specialist fields. However, these approaches may have poor generalization capabilities and are difficult to apply directly to the emergency field. In addition, most existing methods ignore the interdependencies between event triggers and event arguments, which may be crucial semantic features for event extraction. To solve these problems, we propose a Chinese emergent event extraction method based on contrastive learning with event semantic features(CNEmergEE). Firstly, the proposed approach automatically expands the pre-trained model’s vocabulary on a domain corpus. Then, continuing learning on the same corpus allows the model to complete domain adaptation. Finally, we use contrast learning to enable models to learn semantic representations of events. Experimental results on the CEC (Chinese Emergency Corpus) dataset show that our approach achieves better improvement.

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AI-Driven Threat Intelligence in Cloud Computing Detecting and Responding to Cyber Attacks

AI-Driven Threat Intelligence in Cloud Computing Detecting a...

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Intelligent Algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： A. Beatrice Dorothy B. Madhavidevi Balusamy Nachiappan G. Manikandan Pramod Kumar Patjoshi M. Sindhuja Department of Data Science St. Joseph’s College (Autonomous) Tiruchirappalli India Department of Computer Science and Engineerin Institute of Aeronautical Engineering Hyderabad India Technical Lead Prologis Denver CO United States Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India School of Management Centurion University of Technology and Management Bhubaneswar India Department of Computer Science and Engineering St. Martins Engineering College Secunderabad India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

This research work tackles flaws in typical cloud threat intelligence systems, which often depend on static rules and signature-based detection approaches that are unable to react to changing cyber threats. These constraints highlight the need for a more proactive approach, leading the development of a novel system that incorporates AI-driven approaches. The work goes beyond static rule-based models and proposes an AI-driven method to addressing inadequacies in current systems. Combining anomaly detection and machine learning (ML) techniques enables the system to adapt to changing security threats. The first phases involve gathering and analyzing data from numerous cloud sources to improve the system's capacity to spot problems. Supervised learning with Random Forest classifies known hazards, while unsupervised learning with Isolation Forest detects new abnormalities. Real-time monitoring and response considerably improve the system's threat detection rates (95%), anomaly detection (93%), and other performance indicators. The proposed system surpasses the existing system by 95% accuracy, 93% precision, and 96% recall. These findings demonstrate how effectively the framework enables cloud safety and its capacity to enhance overall digital safety and proactively prevent assaults.

关键词： Measurement Privacy Supervised learning Real-time systems Threat assessment Protection Anomaly detection Unsupervised learning Streams Random forests

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Demonstration and Analysis of a 100-Mbps Real-Time Daytime OWC System

Demonstration and Analysis of a 100-Mbps Real-Time Daytime O...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shixuan Wang Weibo Zheng Shulei Wang Yuanzhe Qu Shidong Yuan Yongchun Yuan Wentao Li Junjie Zhang Qianwu Zhang Key laboratory of Optics Fiber and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Engineering No. 1 Shanghai Institute of Technical Physics Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Shanghai China Teralink Optical Corporation (Shanghai) Shanghai China

We propose a daytime OWC system based on VCSEL and APD with misalignment tolerance of 18 degrees. FPGA-Based real-time experimental results show that 140m @100Mbps transmission distance can be achieved with 47300 lux ... 详细信息

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

关键词： Ultraviolet sources Asia Avalanche photodiodes Real-time systems Optical fiber communication Vertical cavity surface emitting lasers Photonics

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Intelligent traffic prediction system using hybrid convolutional neural networks for smart cities

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Multimedia Tools and Applications 2024年 1-19页

作者： J, Jeba Sonia G, Arun Kumar kumar, ERajesh Raju, K. Narasimha Sudha, V. Kshirsagar, Pravin R. Tirth, Vineet Rajaram, A. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Chennai603203 India Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida201301 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India Andhra Pradesh Visakhapatnam530048 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Electronics & amp Telecommunication Engineering J D College of Engineering & ampManagement Maharashtra Nagpur India Mechanical Engineering Department Centre for Engineering and Technology Innovations College of Engineering King Khalid University Asir Abha61421 Saudi Arabia Department of Electronics and Communication Engineering EGS Pillay Engineering College Nagapattinam India

Intelligent Traffic Management Systems (ITMS) play a crucial role in advancing smart city applications by addressing the rapid increase in road traffic due to the growing number of vehicles. Effective real-time traffic prediction is essential to avoid congestion, yet existing systems often struggle with high delays and insufficient accuracy. This paper introduces an innovative ITMS based on a Hybrid Convolutional Neural Network (HCNN) to overcome these challenges. The proposed system integrates numerous smart devices, including smart vehicles, with an edge computing framework where the HCNN model is deployed. The HCNN combines Convolutional Neural networks (CNN) and Long Short-Term Memory (LSTM) networks to enhance prediction accuracy and reduce latency. Experimental results demonstrate that the HCNN model achieves up to 95% prediction accuracy while reducing delays by up to 50% compared to conventional methods. These improvements are significant for smart city applications, ensuring timely and precise traffic management. The study validates the effectiveness of the HCNN model in improving traffic prediction and proposes its potential for broader smart city implementation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Position: explain to question not to justify 24

Position: explain to question not to justify

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Proceedings of the 41st International Conference on Machine Learning

作者： Przemyslaw Biecek Wojciech Samek MI2.AI University of Warsaw Poland and Warsaw University of Technology Poland Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Germany and Department of Electrical Engineering and Computer Science Technical University of Berlin Germany and BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

Explainable Artificial Intelligence (XAI) is a young but very promising field of research. Unfortunately, the progress in this field is currently slowed down by divergent and incompatible goals. We separate various threads tangled within the area of XAI into two complementary cultures of human/value-oriented explanations (BLUE XAI) and model/validation-oriented explanations (RED XAI). This position paper argues that the area of RED XAI is currently under-explored, i.e., more methods for explainability are desperately needed to question models (e.g., extract knowledge from well-performing models as well as spotting and fixing bugs in faulty models), and the area of RED XAI hides great opportunities and potential for important research necessary to ensure the safety of AI systems. We conclude this paper by presenting promising challenges in this area.

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A Semiconductor Manufacturing Final Test Yield Classification Using Random Forest

A Semiconductor Manufacturing Final Test Yield Classificatio...

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data science and Information System (ICDSIS), IEEE International Conference on

作者： R. Dineshkumar Srinivas Aluvala Suma Srinath Zaid Alsalami V T Krishnaprasath Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Semi-conductor manufacturing companies are growing rapidly by forming collaborative design and wafer fabrication. The yield prediction techniques use production data and provide improved operational efficiency and decrease production costs. Random forest classification method is proposed for semiconductor manufacturing Final Test (FT) yield prediction. data pre-processing methods include Gaussian mixture Model, one hot encoder and Label encoder whereas improved grid search algorithm is used for feature selection. The selected features are then given to the implemented classification Random forest (RF) model. The RF method was evaluated by conducting experiments for three different products which are provided by Silicon Laboratories. The RF method outperformed in handling both numerical and categorical production data and is evaluated by the metrics such as accuracy, precision, recall and F-measure. The developed method achieved 0.985 of accuracy when compared with the existing methods such as LSTM-AM and SH-DNN.

关键词： Measurement Accuracy Laboratories Production Semiconductor device manufacture Feature extraction Prediction algorithms

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