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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dar, Muzaffar Ahmad Pushparaj, Jagalingam Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset. © 2024 IEEE.

关键词： Speech recognition

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Task Offloading in Internet of Medical Things Systems: A Hybrid Fog-Cloud Approach with Actor-Critic Decision Making 3rd

Optimizing Task Offloading in Internet of Medical Things Sys...

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3rd International Conference on Advanced Computing and Applications, ICACA 2024

作者： Sutradhar, Shrabani Bose, Rajesh Majumder, Sudipta Mondal, Haraprasad Dibrugarh University Institute of Engineering and Technology Dibrugarh University Assam Dibrugarh786001 India Department of Computational Science Brainware University West Bengal Kolkata India Department of Computer Science & Engineering JIS University West Bengal Kolkata India

ISBN: (纸本)9789819747986

In response to the escalating demands for advanced processing capabilities in healthcare applications, we introduce a ground-breaking solution, the Hybrid Fog-Cloud Offloading (HFCO) approach, for Internet of Medical Things (IoMT) devices. This method optimally offloads complex tasks to cloud servers or nearby fog nodes, mitigating the inherent limitations of IoMT devices. The offloading decision is intelligently made by IoMT devices, considering task requirements and collaborating with nearby fog nodes. The challenge of selecting the most suitable fog node is addressed through an autonomous decision-making mechanism employing a Markov Decision Process (MDP) and the Actor-Critic algorithm. Our proposed strategy surpasses current state-of-the-art approaches, evident in numerical simulations showcasing reduced latency, heightened efficiency, load balancing, and improved scalability, resilience, and security. By seamlessly integrating cloud resources, the HFCO approach empowers IoMT applications, ensuring minimal latency, efficient task execution, and a robust, secure healthcare infrastructure. This research pioneers a dynamic solution to enhance the operational efficacy of IoMT devices in healthcare settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Markov processes

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Digital Twin of a Vibration System for Active Geomonitoring of the Environment 16

Digital Twin of a Vibration System for Active Geomonitoring ...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Khairetdinov, Marat S. Yakimenko, Alexander A. Nishlyaev, Evgenii V. Novosibirsk State Technical University Institute of Computational Mathematics and Mathematical Geophysics of SB RAS Novosibirsk Russia Department of Computer Science Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9798350330885

The problem of constructing a digital twin of a vibration geophysical system for active geophysical monitoring of the environment is considered. The system is used to probe complex media in the "lithosphere-atmosphere-hydrosphere" complex in order to monitor the dynamically changing geophysical characteristics of media in time and space. First of all, this concerns seismic-volcano-hazardous zones, landslide areas, areas of quarry and nuclear explosions, etc. in order to develop methods for predicting catastrophic phenomena - earthquakes, volcanic eruptions, etc. An example of such a seismically active zone is the Baikal Rift Zone. The functioning of the virtual part of the digital twin of a vibration geophysical system is based on multifactor equations for the propagation and processing of vibration oscillations in complex environments. Such equations determine the main components of the digital twin, the connections between them and the simulation modes that underlie the digital twin. The implementation of the virtual digital model is provided using the Manufacturing and Transportation Simulation System simulation modeling system. The system is a visually interactive processor-oriented simulation modeling system for technical systems and technological processes. © 2023 IEEE.

关键词： Volcanoes

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Simulation and Emulation Tools for Fog Computing

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Recent Advances in computer science and Communications 2022年第3期15卷 315-322页

作者： Singh, Simar P. Kumar, Rajesh Sharma, Anju Reddy, S. Raji Vashisht, Priyanka Landran Mohali India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda India Department of Mathematics & Humanities Mahatma Gandhi Institute of Technology Hyderabad India Computer Science and Engineering Department The NorthCap University Gurugram India

Background: Fog computing paradigm has recently emerged and gained increasing attention in the present era of the Internet of Things. The growth of a large number of devices all around, leads to the situation of the flow of packets everywhere on the Internet. To overcome this situation and to provide computations at network edge, fog computing is the need of the present time that enhances traffic management and avoids critical situations of jam, congestion etc. Methods: For research purposes, there are many methods to implement the scenarios of fog computing i.e. real-time implementation, implementation using emulators, implementation using simulators etc. The present study aims to describe the various simulation and emulation tools for implementing fog computing scenarios. Results: The review shows that iFogSim is the simulator that most of the researchers use in their research work. Among emulators, EmuFog is being used at a higher pace than other available emulators. This might be due to ease of implementation and user-friendly nature of these tools and language these tools are based upon. The use of such tools enhance better research experience and leads to improved quality of service parameters (like bandwidth, network, security etc.). Conclusion: There are many fog computing simulators/emulators based on many different platforms that use different programming languages. The paper concludes that the two main simulation and emulation tools in the area of fog computing are iFogSim and EmuFog. Accessibility of these simulation/emulation tools enhance better research experience and leads to improved quality of service parameters along with the ease of their usage. © 2022 Bentham science Publishers.

关键词： Fog computing

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Intrusion Detection System in Mobile Cloud Computing Using Bat Optimization Algorithm-Support Vector Machine 2

Intrusion Detection System in Mobile Cloud Computing Using B...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Alabdeli, Haider Yamsani, Nagendar Anitha, D. Chaithra, K.N. Bindu, G. The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Kattankulathur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India

ISBN: (纸本)9798350317558

Mobile Cloud Computing (MCC) is provided in various industries to acquire cloud-based services by using mobile technology. MCC is employed to minimize mobile device limitations by enabling them to offload computations to remote cloud. It contains significant information about organizations;hence, to avoid this environment from attacks a security solution is required. An Intrusion Detection System (IDS) is a solution to these security problems. The Bat Optimization Algorithm-based Support Vector Machine (BOA-SVM) is developed for IDS by utilizing machine learning in MCC in this research. Firstly, data is acquired by UNSW-NB15 dataset and then min-max normalization is established for normalizing acquired data. GWO is employed for Feature Selection (FS) that determines relevant features for classification. At last, SVM is established to classify IDS effectively and accurately. BOA-SVM attains a better accuracy of 99.78% compared to existing techniques such as Edge-based Hybrid Intrusion Detection Framework (EHIDF), Information Gain-Chi-Square-Particle Swarm Optimization (IG-CS-PSO), and improved Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) respectively. © 2024 IEEE.

关键词： Bat Optimization Algorithm Intrusion Detection System Machine Learning Mobile Cloud Computing Support Vector Machine

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Automated Early Diagnosis of Lung Tumor Based on Deep Learning Algorithms 1

Automated Early Diagnosis of Lung Tumor Based on Deep Learni...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Maheswari, M. Jothi, B. Devi, P. Gayathri Chitradevi, B. Pradeep, Kumar S SRM Institute of Science and Technology Department of Computational Intelligence Chennai India Panimalar Engineering College Department of ECE Chennai India Department of Computer Science Engineering Perambalur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The main goals of this study are to identify the early stages of lung cancer and investigate the degree of accuracy of various machine learning algorithms. A thorough review of the literature revealed that certain classifiers have low accuracy while others have higher accuracy but are challenging to approach nearer to 100%. Low accuracy and high implementation costs as a result of inappropriate DICOM image handling Many various types of images are utilised in medical image processing, however CT scans are typically chosen since they have less noise. Deep learning has been shown to be the most effective technique for processing medical images, identifying and classifying lung nodules, extracting features, and predicting the stage of lung cancer. This system's initial stage employed image processing methods to separate lung sections. Using K Means, the segmentation is carried out. Segmented images are used to extract the features, which are then classified using a variety of machine learning algorithms. The accuracy, sensitivity, and specificity of the proposed techniques, as well as their classification time, are used to assess their performance. © 2023 IEEE.

关键词： computerized tomography

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IoT-Enabled Health Monitoring System for Safeguarding Vital Organs with Cloud-Based Diagnosis and Advanced Algorithms 4

IoT-Enabled Health Monitoring System for Safeguarding Vital ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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