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A Novel Collaborative Framework for Cloud-Based Cognitive Services 4

A Novel Collaborative Framework for Cloud-Based Cognitive Se...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sharma, Vinod Rathore, Rachna Maheshwari, Divya Pandey, Abhishek Juneja, Ashwani Kumar Chauhan, Yuvraj Singh Slp University Dept of Cse Gwalior India Jiwaji University Dept. of Mathematics Gwalior India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Vidyapeeth University Dept. of Journalism & Mc Lingayas Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Cognitive Radio (CR) is transforming wireless communications by optimizing spectrum usage. CR users can access the spectrum only when primary users are inactive, requiring reliable spectrum sensing. Cooperative spectrum sensing, where data from multiple nodes is shared with a central hub, has emerged as the most effective method, surpassing local detection in dealing with challenges like noise uncertainty and channel instability. This study explores two fusion algorithms for merging local spectrum data: hard decision fusion (HDF) and soft decision fusion (SDF). While SDF offers better detection capabilities, it demands excessive bandwidth. In spectrum allocation, a complex NP-hard problem, optimization methods, especially swarm intelligence inspired by honey bee behavior, have been researched. To address spectrum assignment issues, a novel hybrid Deep Recurrent Neural Network (DRNN) approach is proposed. This hybrid model, incorporating 2-opt and additional search techniques, enhances spectrum utilization by overcoming slow convergence and local optima challenges. Empirical results demonstrate its superiority in optimizing spectrum allocation. © 2024 IEEE.

关键词： Swarm intelligence

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Vehicle To Vehicle and Vehicle To Ground Communication - Speech Encryption Algorithm

Vehicle To Vehicle and Vehicle To Ground Communication - Spe...

引用

2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Al-Hazaimeh, Obaida M. Al-Smadi, Ma'moun A. Al-Balqa Applied University Dept. of Computer Science Irbid Jordan Al-Balqa Applied University Dept. of Electrical Engineering Irbid Jordan

ISBN: (纸本)9798350322972

Vehicle to vehicle (V2V) communication has many applications in intelligent transportation system (ITS) like smart grid systems, fleet management and traffic light control. It aims to interconnect several components like road infrastructures, wireless networks, sensors, and vehicles. Therefore, it can be utilized to reduce congestion, accidents and fuel consumption. This paper propose a speech encryption system based on a one-dimensional logistic map for vehicle to vehicle communication (V2V) communication in intelligent transportation systems (ITS) to improve road safety and efficiency, where encryption and security play a major role. This study employs a symmetric key cryptography technique for chaotic ciphering. In this approach, a random key table lookup criterion was paired with a one-dimensional chaotic map in order to encrypt speech samples with a high level of security and acceptable speed. XOR encryption and speech row shuffling are used in the proposed technique. Using the difference between the old and new row locations, a combination of variable rotation and inversion was performed to each shuffled row in order to increase the level of security. As a consequence of the testing, the proposed approach was shown to be both efficient and usable. Combining key table and logistic map illustrates the advantages of both high-level security and large random key space. The encrypted speech produced is suitable for practical usage especially in vehicle to vehicle and vehicle to base transmission. © 2023 IEEE.

关键词： Roads and streets

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HealthLink: A Cutting-Edge System for Secure Healthcare Data with IoT, Blockchain, and IPFS 3

HealthLink: A Cutting-Edge System for Secure Healthcare Data...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Vijayakumar, Kallur V. Chethan, A.S. Muneshwara, M.S. Swetha, M.S. Anand, R. Shivakumara, T. Bmsit&m Dept. of Mathematics India Bmsit&m Dept. of Computer Science & Engineering India Bmsit&m Dept. of Information Science & Engineering India Bmsit&m Dept. of Mca India

ISBN: (纸本)9798350354348

In today's rapidly evolving healthcare landscape, the integration of cutting-edge technologies such as IoT and wearable devices has led to significant advancements, particularly in the realm of remote patient monitoring. Despite these advancements, the traditional client/server architecture prevalent in current implementations presents substantial challenges related to security and privacy, leaving healthcare systems vulnerable to various attacks. Consequently, stringent regulatory and security measures are imperative to safeguard health data. To address these challenges and meet regulatory requirements, transitioning to a distributed architecture is essential. Blockchain technology, renowned for its distributed nature and robust security features, offers a promising solution to mitigate the security risks inherent in IoT-based systems. Motivated by these considerations, this study introduces HealthLink - a secure healthcare framework that seamlessly integrates IoT and Blockchain *** is designed to facilitate remote patient monitoring, particularly for chronic diseases that require continuous oversight. The framework prioritizes security, scalability, and processing efficiency. Security measures include the use of re-encryption proxies combined with Blockchain for hash data storage, while access control is managed through smart contracts. To enhance Blockchain scalability, an off-chain IPFS-based database is employed for data storage, and the Ethereum Blockchain-based proof of authority is leveraged to expedite data storage processes. As a practical demonstration, we apply the HealthLink system to diabetes management, presenting execution results through system interfaces. Experimental findings highlight substantial enhancements in the security of healthcare systems compared to conventional methodologies, underscoring the potential of HealthLink to revolutionize remote patient monitoring and data security in healthcare. © 2024 IEEE.

关键词： Remote patient monitoring

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Multi-Label Classification of CS Papers Using Natural Language Processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep Learning LSTM Machine learning multi-label classification

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Statistical and Machine Learning Perspectives on Exoplanets Prediction and Classification 1

Statistical and Machine Learning Perspectives on Exoplanets ...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Priya, M. Yagnasri Shendre, Sanjivani P. Kamble, Samrudhi S. Sridhar, Swetha Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India

ISBN: (纸本)9798350376517

Exoplanets are the planets orbiting stars out of our solar system. The exploration of such planets has become a growing field in astronomy but the analysis of the data related to such planets involves many challenges when traditional methods are used as this data is often vast and is obtained with the help of telescopes and space missions. This study proposes a methodology that aims to classify exoplanets, taken from datasets from the Kepler Objects of Interest (KOI) database and the NASA Exoplanet archive, as confirmed, candidate, or false positives. This approach makes use of modern analytical techniques, such as statistical analysis, probability theory, hypothesis testing, and machine learning (ML) algorithms, to facilitate the detection and classification of exoplanets. Clustering methods such as BIRCH and K-Means are also performed on the Planetary data. This study contributes to improving our knowledge and understanding of exoplanetary systems, thereby highlighting the relevance of modern analytical tools in the field of astronomy. © 2024 IEEE.

关键词： NASA

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Enhanced Image Analysis for Detecting Malaria Infection in Blood Samples 7

Enhanced Image Analysis for Detecting Malaria Infection in B...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Karthikeyan Anitha, K. Kashyap, Acchute Amrita School of Computing Dept. of Computer Science Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Engineering Dept. of Mathematics Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798331527822

The methods of detecting malaria infected cells from microscopy images are discussed in detail in this paper. Malaria disease being a world health problem today, there is need to develop better diagnosing technique because of the high death rate that has been plunged to this disease. Having used a dataset of 27,558 images as infected and uninfected, this study employs Convolutional Neural Networks(CNN), Fuzzy and VGG19 models for semiautomated classification with higher speeds and accuracies as opposed to other methods. It consists of mandatory phases, which predetermine preparing the data set for training, including resizing, normalization, and data augmentation. Among to feature selection, SelectKBest method is used in order to enhance input features of the model. Initial experiments with the CNN model again revealed high accuracy greater than 90%, thereby confirming the use of the model in real-life diagnosis of malaria. In addition, the study emphasises the importance of feature extraction and the use of machine learning in the diagnosis function. The outcomes support the adoption and use of automated detection techniques as the human factor contributes greatly to errors in diagnosis, practices should embrace such technologies leading to the fight against malaria. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards the Diagnosis of Heart Disease Using an Ensemble Learning Approach 22

Towards the Diagnosis of Heart Disease Using an Ensemble Lea...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Zhang, Zheng Xu, Liangliang Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Heart disease is one of the leading causes of death worldwide. Understanding the presence of heart disease is crucial for timely intervention and effective management. However, it is still a challenging task to accurately diagnose and treat heart disease. In this paper, we propose an ensemble machine learning model that combines the predictive power of three state-of-the-art machine learning algorithms: BERT (Bidirectional Encoder Representations from Transformers), FT-transformer, and XG-Boost. Through extensive training and evaluation, we assess the performance of our model using established metrics such as accuracy, precision, recall, F1-score, and receiver operating char-acteristic (ROC) curve analysis. The results obtained demonstrate the effectiveness and validity of our machine learning system in predicting heart disease accurately. This paper presents a promising approach that can assist healthcare professionals in making informed decisions and improving patient outcomes, enhances our understanding of heart disease patterns, and contributes to the development of effective diagnostic and treatment approaches. © 2023 IEEE.

关键词： Autoencoder BERT Heart Disease Machine Learning Transformer XG-Boost

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Preprocessing Pathology Reports for Vision-Language Model Development

Preprocessing Pathology Reports for Vision-Language Model De...

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2024 MICCAI Workshop on Computational Pathology, MICCAI COMPAYL 2024

作者： Lucassen, Ruben T. van de Luijtgaarden, Tijn Moonemans, Sander P.J. Blokx, Willeke A.M. Veta, Mitko Dept. of Pathology University Medical Center Utrecht Netherlands Dept. of Biomedical Engineering Eindhoven University of Technology Netherlands Dept. of Mathematics and Computer Science Eindhoven University of Technology Netherlands

Pathology reports are increasingly being used for development of vision-language models. Because the reports often include information that cannot directly be derived from paired images, careful selection of information is required to prevent hallucinations in tasks like report generation. In this paper, we present a language model for subsentence segmentation based on the information content, as part of a preprocessing workflow for 27,500 pathology reports of cutaneous melanocytic lesions. After initial clean up, the reports were first translated from Dutch to English and then segmented by separate language models. Both models were developed using an iterative approach, in which the development dataset was expanded with manually corrected model predictions for previously unannotated reports before finetuning the next version of the models. Over the course of eight iterations, the development dataset was in the end scaled up to 1,500 translated and annotated reports. On the independent test set of 3,597 sentences from 150 reports, 219 translation errors (6,1%) of different severities were counted. The subsentence segmentation model achieved a strong predictive performance on the test set with a macro average F1-score of 0.921 (95% CI, 0.890-0.940) and a weighted average F1-score of 0.952 (95% CI, 0.944-0.960) over 13 different classes. The remaining 25,850 unannotated reports were translated and segmented using the final models to complete the dataset preprocessing. Differences in word count and class distribution between section types of the reports were explored in preparation for future vision-language modeling. The presented methodology is generic and can, therefore, easily be extended to multiple or different pathology domains beyond melanocytic skin lesions. Code and trained model parameters are made publicly available. ©2024 Lucassen et al.

关键词： Visual languages

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Safety and Security Dependencies for Gridshield

Safety and Security Dependencies for Gridshield

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Soltani, Reza Ozceylan, Baver Lopuhaä-Zwakenberg, Milan Kolb, Christina Hoogsteen, Gerwin University of Twente Dept. of Electrical Engineering Mathematics and Computer Science Enschede Netherlands University of Twente Dept. of Behavioural Management and Social Sciences Enschede Netherlands

ISBN: (纸本)9789531842976

Present day cyber-physical systems, such as the Smart Grid, lead to the integration of multiple sub-systems into one single intertwined system. Such systems are characterized by many inter-dependencies between these sub-systems. This makes it complex to correctly assess the impact of new defence mechanisms with respect to the safety and security of the system as a whole. Existing formalisms such as fault and attack trees cannot describe the full system *** paper presents a novel integrated model, namely the Attack-Fault-Defence Tree (AFDT), and tools to analyse such cyber-physical systems. The presented visual representation allows experts from various disciplines to discuss system dependencies together. In addition, we also present how minimum cut sets can be derived to formally quantify how the safety and security of the overall system is enhanced with the implementation of new defences. The presented AFDT is applied to the Gridshield concept, a novel defence mechanism to prevent grid overloading in power grids due to simultaneous charging of electric vehicles. © 2024 IEEE.

关键词： Cyber attacks

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Identifying Melanoma Skin Disease Using Convolutional Neural Network DenseNet-121 2

Identifying Melanoma Skin Disease Using Convolutional Neural...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kaushik, Priyanka Rathore, Rachna Kumar, Aditya Kanishka Goshi, Ganpat Sharma, Priyanshu CSE-APEX Chandigarh India Jiwaji University Dept. of Mathematics Gwalior India Madhav University Dept. of Computer Science & Engineering Abu Road India

ISBN: (纸本)9798350360523

Advancements in melanoma detection are imperative for early intervention and improved patient outcomes. This study explores the transformative potential of DenseNet-121 within CNNs for melanoma identification. Leveraging a dataset from the ISIC 2019 challenge, the model achieved a remarkable 90% accuracy, surpassing traditional models. DenseNet-121 demonstrated adaptability across diverse lesion variations, showcasing its superiority. The study's implications extend to real-time applications, collaborative efforts, and ethical considerations, heralding a significant stride in reshaping melanoma diagnostic standards. © 2024 IEEE.

关键词： Dermatology

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