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Security in the World of Internet of Things (IoT): Overcoming Challenges

Security in the World of Internet of Things (IoT): Overcomin...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Gannavaram, Venkat Tulasi Krishna Desu, Sravya Jujjuru, Venkata Rohith Renikunta, Sri Vatsa Venkata Tejaswi Gajula, Arun Kumar Reddy, Ganta Raghotham School of Electrical Computer and Energy Engineering Arizona State University Tempe United States School of Computing and Augmented Intelligence Arizona State University Tempe United States Department of Electronics and Communication Engineering Kakatiya Institute of Technology and Science Warangal India

ISBN: (纸本)9798350355338

Internet of Things (IoT) is turning out to be more prominent in our everyday existence, with smart home appliances and industrial control systems serving as examples. But the proliferation of IoT devices and internet connectivity increased the potential of security breaches. Botnet attacks, injection attacks, Domain Name Systems (DNS) spoofing attacks, firmware assaults, zero-day attacks, brute-force attacks, and cryptographic attacks may be used against IoT devices. These assaults may result in the loss of data, illegal access, and even physical damage to infrastructure and people. There are various potential courses of action. Machine Learning subpart of Artificial Intelligence have the capacity for finding and prevent dangers in real time. With blockchain technology, an IoT network can have a secure and unbreakable a record of every exchange and data transfers. Hardware-based security techniques, such as secure boot and trusted execution environments, may protect IoT devices against attacks that seek to compromise the underlying hardware or firmware. Standardizing security procedures may ensure that all devices can work together safely and achieve a given degree of security. The use of privacy-enhancing technology, IoT data may be kept confidential and sensitive information disguised. © 2024 IEEE.

关键词： Firmware

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique

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Procedia computer science 2025年 258卷 4105-4116页

作者： Nilesh Bhaskarrao Bahadure Sudhanshu Gonge Jagdish Chandra Patni Bhoomi Shah Nagrajan Raju Prasenjeet D. Patil Department of Computer Science and Engineering School of Technology GSFC University Vadodara Gujarat India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Thanjavur Tamil Nadu India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality.

关键词： Berkeley wavelet transformation enhancement magnetic resonance imaging (MRI) segmentation morphology

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Cack—a counter based authenticated ack to mitigate misbehaving nodes from manets

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Recent Advances in computer science and Communications 2021年第3期14卷 837-847页

作者： Atheeq, C. Rabbani, M. Munir A. Department of Electrical and Computer Engineering B S Abdur Rahman Crescent Institute of Science and Technology Chennai India

Background: The evolution of wireless network from wired network presents a world-wide pattern in the previous couple of decades. All the mobile nodes in MANET act as router as well as host at the same time send and receive messages directly to one another until they are in the communication range and use multiple hops if the nodes are outside the communication range. The self-organizing property of nodes in MANET made it prominent among the principal applications like military and or emergency rescue sites. In spite of, the openness and dynamic nature of mobile nodes, MANET suffers from malicious nodes. Studies show that the existing mechanism lacks with cost effective and reduced overhead in the network. Objective: It is vital to design a system that detects the malicious nodes to guard MANET from attackers. With the enhancements of the innovation and cost effective and minimum overhead, our visualization presents a tremendous expansion of MANETs into modern applications. Methods: In this article, we present our proposed model Counter based authenticated acknowledgement uniquely developed for MANETs that uses chebyshev polynomials and digested acknowledgment message for detection of misbehaving nodes in MANET. Results: Implementation shows that the proposed model outperforms in terms of reduced overhead, delay and packet delivery by mitigating attacks. Conclusion: Finally we conclude that we design an effective intrusion detection system that can be adaptable in MANET applications. © 2021 Bentham science Publishers.

关键词： Chebyshev polynomials

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Enhancing Autonomous Vehicle Navigation with Real-Time Object Detection Using Convolutional Neural Networks 7

Enhancing Autonomous Vehicle Navigation with Real-Time Objec...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Alsalami, Zaid Boob, Nandini Shirish Badhoutiya, Arti Mounika, T. Jayanthi, Muthuswamy Chaturvedi, Prateek Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India New Horizon College of Engineering Department of Electronics and Communication Engineering Bangalore India Amity University Amity School of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

Autonomous vehicles' (AVs) capacity to identify and categorise things in real-time is crucial to their development for the sake of efficient and safe navigation. Because of its exceptional capability to learn spatial feature hierarchies automatically, Convolutional Neural Networks (CNNs) have become an indispensable tool for object identification in intricate visual recognition problems. This research offers a thorough analysis of convolutional neural networks (CNNs) used for autonomous vehicle object identification in real-time, with an emphasis on improving detection accuracy and speed. We compare the processing speed, detection accuracy, and computational efficiency of several CNN designs, such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN. In addition, to address the demanding realtime needs of autonomous driving systems, we suggest CNN design improvements that use hardware acceleration methods like GPU and TPU optimisations. The experimental findings show that our suggested method significantly reduces detection latency while maintaining accuracy, which makes it suitable for use in real-world AV settings. Autonomous vehicle technology advances thanks to this study's results, which aid in building scalable and reliable object identification algorithms. © 2024 IEEE.

关键词： Convolutional neural networks

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On the Analysis of a BERT-based Domain-Specific Question Answering Models for Indian Legal System 6

On the Analysis of a BERT-based Domain-Specific Question Ans...

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6th International Symposium on Advanced electrical and Communication Technologies, ISAECT 2024

作者： Shah, Nisarg Thakkar, Hiren Kumar Mewada, Hiren School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat 382007 India Prince Mohammad Bin Fahd University Electrical Engineering Department P.O. Box 1664 Al Khobar31952 Saudi Arabia

ISBN: (纸本)9798331529987

The Indian legal system is too large and complex which includes the largest Constitution in the world, thousands of laws, central laws, thousands of acts and many bilateral, regional, and multilateral treaties and agreements with different countries. This makes it difficult to understand and memorize the contents for legal professionals as well as the general public. Some individuals and government officials take advantage of this fact by doing unlawful acts and avoid punishments due to the unawareness in public. So, we are developing a closed domain extractive model for question answering on the Indian legal system. The motivation behind developing a Question Answering System (QAS) on Indian laws is to enhance the accessibility of legal information for both legal professionals and the general public. PDFs of legal acts were collected from the Government website from which Question Answering dataset was created. Dataset contains automatic system generated questions using T5 question generated model and then manual cleaning such as removing wrong questions, minor changes were done. Models used for QA are DistilBERT, RoBERTa, BERT Large. BERT Large outperforms the other models with cosine similarity of 0.944, BLEU score of 0.781, Exact match(EM) with 0.72. Question answering model provides short and precise answers of the questions with context from which the answer was extracted to avoid giving wrong information. © 2024 IEEE.

关键词： Economic and social effects

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Advancing Functional Near-Infrared Spectroscopy (fNIRS) with Transformers: A PRISMA-driven systematic review

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IEEE Access 2025年 13卷 97794-97817页

作者： Abdollahpour, Neda Kakaraparthi, Abhinav Valluru, Chakradhar Sai Sertac Artan, N. New York Institute of Technology Department of Electrical and Computer Engineering New York NY United States New York Institute of Technology Department of Computer Science New York NY United States

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging modality in various domains including cognitive neuroscience, brain-computer interfaces (BCIs), clinical diagnostics, mental workload assessment, and neurorehabilitation. Despite its growing applications, processing fNIRS signals presents unique challenges. The advent of deep learning, particularly transformers, has taken on an increasingly pivotal role in addressing these complexities. As transformers have revolutionized multiple fields, their application in fNIRS signal processing has gained significant traction recently. To support ongoing research, this study systematically reviews the advancements and applications of transformer models in fNIRS research, following PRISMA guidelines. A comprehensive analysis was conducted on 30 studies (N = 895), offering a structured evaluation of current trends, methodologies and outcomes. This review paper highlights transformer architectures in fNIRS-based studies, with a particular focus on advanced modifications in key stages such as the embedding and attention mechanisms. Multimodal studies analyzing fNIRS with other modalities, such as Electroencephalography (EEG), using transformers are also reviewed. Finally, the paper examines open-access and private datasets used in the literature and offers guidelines for future research, identifying open problems that warrant further exploration. © 2013 IEEE.

关键词： BCI Brain-computer Interface Deep Learning Emotion Recognition fNIRS functional Near Infrared Spectroscopy Mental Arithmetic Motor Imagery Systematic review Transformer architecture Transformer models

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The Rise of Generative Artificial Intelligence in Healthcare 12

The Rise of Generative Artificial Intelligence in Healthcare

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Kuzlu, Murat Xiao, Zhenxin Sarp, Salih Catak, Ferhat Ozgur Gurler, Necip Guler, Ozgur Old Dominion University Electrical Engineering Technology NorfolkVA United States The College of William and Mary Computational Operations Research WilliamsburgVA United States Virginia Commonwealth University Electrical and Computer Engineering RichmondVA United States University of Stavanger Department of Electrical Engineering and Computer Science Rogaland Norway EKare Inc. FairfaxVA United States

ISBN: (纸本)9798350322910

Generative Artificial Intelligence (GAI) is transforming various fields, including finance, education, marketing, and healthcare. Especially in healthcare, GAI has the potential to revolutionize various aspects, such as medical imaging, drug development, patient care, and treatment planning. Key stakeholders who stand to benefit from these advancements include hospitals, clinics, pharmaceutical companies, medical device manufacturers, and research institutions. However, the implementation of GAI in healthcare presents several challenges, such as ensuring data privacy and security, addressing ethical considerations, maintaining quality and accuracy, adhering to regulatory compliance, and integrating with existing systems. This paper examines the current state of GAI in healthcare, discusses its potential benefits and challenges, and highlights future directions that must be addressed to fully harness the power of GAI in improving patient outcomes and healthcare systems. © 2023 IEEE.

关键词： Artificial intelligence

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Decentralized Microgrid Energy Management: A Cooperative Game Approach for Peer-to-Peer Energy Trading

Decentralized Microgrid Energy Management: A Cooperative Gam...

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International Conference on Green Energy and Applications (ICGEA)

作者： Son Tran Thanh Anh Nguyen-Tuan Hung Ta-Xuan Tuyen Nguyen Duc Hirotaka Takano Dept. Electrical Engineering School of EEE Hanoi Uni. of Science and Technology Hanoi Vietnam Dept. Electrical Electronic and Computer of Engineering Faculty of Engineering Gifu Univerisity Science and Technology Gifu Japan

ISBN: (数字)9798331530778

ISBN: (纸本)9798331530785

This article introduces an innovative platform for microgrids (MGs) that enables active energy trading both with the main power grid and directly among MGs. By facilitating Peer-to-Peer (P2P) energy trading, the proposed platform ensures mutually beneficial outcomes, particularly in the presence of advanced energy resources such as Battery Energy Storage Systems (BESSs) and Demand Response Programs (DRPs). From the perspective of power systems, P2P energy trading enhances local energy balancing and promotes self-sufficiency within MG networks. Unlike traditional noncooperative game models, where participants act independently, this study adopts a cooperative game approach that aligns with the autonomous and self-interested nature of MGs. Numerical simulations validate the effectiveness of the platform, demonstrating that MGs can engage in energy trading while achieving economic benefits. Fur-thermore, the platform determines optimal pricing and energy trading volumes, ensuring that participants gain measurable eco-nomic advantages in the P2P market. Additionally, technologies such as BESSs and DRPs significantly enhance energy sharing among MGs, thereby improving overall system efficiency and sustainability.

关键词： Biological system modeling Volume measurement Games Microgrids Pricing Particle measurements Peer-to-peer computing Sustainable development Optimization Resilience

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DeepWeb: A Web Application for Advanced Lung Cancer Identification Using Novel CNN Model

DeepWeb: A Web Application for Advanced Lung Cancer Identifi...

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International Conference on Innovations in science, engineering and technology (ICISET)

作者： Kazi Rifah Noor Shafinul Islam Nowshin Tasnim Mohammad Nurul Huda Department of Computer Science and Engineering United International University Dhaka Bangladesh Department of Computer Science and Engineering University of Science and Technology Chittagong Chittagong Bangladesh Department of Electrical and Computer Engineering North South University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh

ISBN: (数字)9798350355499

ISBN: (纸本)9798350355505

Lung Cancer is one of the deadliest forms of cancer that affects people which increases mortality rates worldwide. Identifying anomalies in medical photographs at the very early stage is crucial for both quantitative image study and patient care. So, in our research, we present a fully automated web application to predict lung cancer early on CT scan images using a robust deep learning model called Convolutional Neural Network (CNN). We also performed transfer learning models i.e. ResNet50 and VGG16 on the same dataset. Still, these models could not achieve higher accuracy than CNN as it showed an excellent performance achieving 99% accuracy and 97% of F1score value. The proposed model is established in three phases: image augmentation is done initially as preprocessing steps on the Kaggle IQ-OTH lung cancer dataset, followed by lung cancer classification using the CNN model, and finally, this model is used to create a web application. Our web application developed using Streamlit, can successfully predict normal, benign, and malignant classes of lung cancer showing the confidence score of each image inserted by the user. This prompt user experience with our web application based on the robust Convolutional Neural Network will assist medical professionals in enhancing the automated early screening and diagnosis of lung cancer as well as raising the patient survival rate effectively.

关键词： Deep learning Accuracy Computed tomography Transfer learning Lung cancer Mortality User experience Convolutional neural networks Medical diagnostic imaging Residual neural networks

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Wind Energy Forecasts with Supervised Machine Learning Techniques

Wind Energy Forecasts with Supervised Machine Learning Techn...

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International Conference on Green Energy, Computing and Intelligent technology 2024, GEn-CITy 2024

作者： Hussain, Mariam Ahammad, Kawsar Noor, Moshiur R. Sharmin, Nusrat Shafiul, Sumayea B. Islam, Shakarul Research of Integrated Green and Holistic Training Center Bogura Bangladesh Dept. of Electrical and Electronic Engineering Eastern University Dhaka Bangladesh Dept. of Computer Science and Engineering University of Science and Technology Chittagong Bangladesh Dept. of Epidemiology Bangladesh University of Health Sciences Dhaka Bangladesh

ISBN: (纸本)9781837242689

United Nations set clean and affordable energy (7th) sustainable development goals. Among renewables, wind energy requires frequent observations. Due to limited finance in developing countries like Bangladesh, accurate and wide observations are one of the major challenges in operation and maintenance for energy harvests. Thus, this paper developed energy forecast models with supervised machine learning (ML) algorithms: support vector regressor (SVR) and random forest (RF) and compared with linear regression (LR) model. With global wind atlas (GWA) datasets (heights: 10 to 200m), training and testing performed with SVR, RF, and LR models. Model performances evaluated with Pearson correlation, coefficient of determinations, mean absolute percentage (MAPE), and root mean square errors (RMSE). During training and testing, ML estimations outperformed LR model as MAPE indicated ≤15% and ≥50% respectively for global and local-scale datasets. For an agricultural land with limited observations, ML predictions were performed for Bogura city with NASA-POWER dataset representing an MAPE of 4.84% (SVR) and 0.91% (RF). At Bogura on average, the final output showed annual energy production of 189 Kilowatt where probability for wind speeds about 70% (3 to 6 m/s at 50m). In conclusion, ML model accuracy is efficient for successful energy operations in Bangladesh and other developing countries with limited observations. © The Institution of engineering & technology 2024.

关键词： Wind forecasting

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