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IEEE International Conference on Biomedical engineering, computer and Information Technology for Health

作者： Md.Tofael Ahmed Bhuiyan Md Nyem Hasan Bhuiyan Khandaker Mohammad Mohi Uddin Md Abdul Based Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka International University Dhaka Bangladesh

ISBN: (数字)9798331534356

ISBN: (纸本)9798331534363

Chronic kidney cancer (CKC) is a real-world example of a crucial class of frontiers that are behind basic medical diagnostics and have a significant worldwide impact. For this reason, we need to evaluate more sophisticated prediction algorithms. In order to balance the previously proven CKC detection, this research has presented a novel strategy that combines ensemble approaches with robust machine learning. We employ nine alternative algorithms three from each of Random Forest, Decision Tree, and Light Gradient Boosting Machine in addition to the standard predictive models for this specific stacking ensemble. We obtained an accuracy of 99.19% after preprocessing the data and using collaboration for feature selection. Our model performs with excellent predictive power, similar to Random Forests, but with the ease of de-biasing and good interpretability of Decision Trees. Additionally, as a mixed category, the ensemble performs better than any one model on all performance criteria (precision, recall, and F1 score). In order to enhance individual risk prediction of CKC and intervention execution, this adaptable framework is therefore ideal for use in clinical settings. Let’s put it this way: our study is a significant step toward improving CKC diagnosis, and it provides a foundation for the broader application of ensemble models of learning in healthcare, which might revolutionize patient care in all specializations.

关键词： Accuracy Machine learning algorithms Stacking Predictive models Prediction algorithms Chronic kidney disease Decision trees Random forests Medical diagnostic imaging Cancer

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Implementing Secure Key Management and Distribution in Cloud Environment 2

Implementing Secure Key Management and Distribution in Cloud...

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2nd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2023

作者： Lamsal, Aman Paul, Aniket Buchade, Amar Nalavade, Jagannath Bidwe, Ranjeet MIT Art Design and Technology University Dept. of Computer Science and Engineering Pune India Vishwakarma Institute of Information Technology Pune Savitribai Phule Pune University Pune India Department of Computer Engineering Pune India

ISBN: (纸本)9798350333763

Key management is considered as an essential aspect of securing cloud-based systems and data. As more organizations move their operations to cloud, the need for secure and efficient key management solutions becomes increasingly important. En-suring security of cloud computing is essential as many users often store their sensitive information with the cloud providers. Cloud service providers offer Key Management Services (KMS) to manage cryptographic keys for their customers, but there are still several challenges related to key management in cloud environments, including key storage, access control, and key rotation. Our research project aims to address these challenges by developing a secure and efficient key management solution for cloud-based systems. We propose a system that uses a combination of various cloud services and encryption algorithms to generate, store, and manage cryptographic keys securely. Our system provides granular access controls, and strong encryption to ensure the confidentiality and integrity of the keys. We believe that our proposed solution will help cloud service providers and organizations to manage their cryptographic keys more efficiently and securely, reducing the risk of unauthorized access and data breaches. This research paper provides a method to effectively manage, store and share the secret key between the parties using the concept of Threshold Cryptography. © 2023 IEEE.

关键词： Access control

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Accelerating supersingular isogeny Diffie-Hellman (SIDH) cryptosystem for the security of resource-constrained IoT devices with FPGA 3

Accelerating supersingular isogeny Diffie-Hellman (SIDH) cry...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Khadra, Shaimaa Abo Ismail, Nabil A. Attiya, Gamal M. Abdulrahman, Salah Eldin S. E. High Institute of Engineering and Technology Dept. Mechatronics Engineerng Al-Mahalla-El-Kobra Egypt Menoufia University Faculty of Electronic En. Dept. Computer Science & Engineering Menouf Egypt

ISBN: (纸本)9798350323511

It is imperative to note that post-quantum cryptography, such as supersingular isogeny Diffie-Hellman (SIDH), is essential for ensuring that Internet of Things (IoT) devices have a restricted amount of resources. The primary challenges in assuring the security of IoT devices are addressed by this work's analysis of SIDH implementations designed for field programmable gate array (FPGA) architecture. Extensive efforts towards implementing the architecture for a rapid and constant-time FPGA implementation of SIDH. This quantum-resistant cryptographic primitive is a crucial component for adhering to NIST's PQC standardization. Our goal with this paper is to demonstrate how FPGA architectures can enhance the parallelism of SIDH, making it a more practical option for securing resource-limited IoT devices. Our focus is on providing a reliable speed record for SIDH, which is crucial for ensuring the security of IoT devices. The design for isogeny computation was built over p434 in Xilinx Virtex 6 and produced 45ms for public key generation in addition to 35ms for secret key generation. © 2023 IEEE.

关键词： Internet of things

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Design and Development of a Smart Agriculture (SA) System with Machine learning-Based IoT Architecture 2

Design and Development of a Smart Agriculture (SA) System wi...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Paul, Robi Nazir, Junayed Bin Paul, Arnob Ahammad, Arif Rochester Institute of Technology Dept. of Electrical and Computer Engineering Technology RochesterNY14623 United States Shahjalal University of Science and Technology Dept. of Electrical and Electronic Engineering Sylhet3114 Bangladesh

ISBN: (纸本)9798350358667

Agriculture has always been a crucial part of the economy, but urbanization has started to reduce our fertile land. This rapid reduction has increased the infestations and diseases, bottlenecking production rates and threatening food safety. However, recent advancements in the IoT have posed many potential solutions for this emerging problem. Incorporating IoT-based infrastructures like a smart-farming system can reduce production costs and increase productivity and overall quality while maintaining environmental balance. This study aims to create a smart agriculture system using IoT that can maintain suitable harvesting conditions and classify diseases through a transfer learning-based approach using Deep Convolutional Neural Network (DCNN). Thereon necessary suggestions are passed to the user through the Android app. The system can successfully detect and suggest diagnosis to six classes, including healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight, with an average accuracy of 96.08%. The precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616 using the non-normalized augmented dataset. The work is expected to offer a roadmap for the future generation of Smart Agriculture systems by summarizing the cultivation system's disease detection, prevention, and overall optimization process. © 2023 IEEE.

关键词： Convolutional neural networks

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An Adaptive Frame-based Age-aware Access Scheme for Time-Critical Satellite-IoT 14

An Adaptive Frame-based Age-aware Access Scheme for Time-Cri...

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14th International Conference on Information and Communication Technology Convergence, ICTC 2023

作者： Xie, Huiyang Cao, Zhenyu Song, Shilun Jiang, Junhui Jeon, Sang-Woon Jin, Hu Hanyang University Dept. Electrical and Electronic Engineering Ansan Korea Republic of Harbin Engineering University College of Computer Science and Technology Harbin China

ISBN: (纸本)9798350313277

Low earth orbit (LEO) satellite constellation provides a possible solution for remote real-time data gathering applications and timely updates of information can be efficiently transmitted through dynamic inter-satellite links. Recently, the freshness of status updates is characterized by age of information (AoI), a novel metric proposed to capture the freshness of status updates. In this paper, we propose a novel adaptive frame-based age-aware access (AFAA) scheme that prioritizes the transmission of devices experiencing consecutive frame failures. The proposed AAFA framework incorporates a new concept of control-AoI to differentiate the transmission probability of each device. In particular, higher control-AoI is given to the devices that manifest higher priority transmission requirements. Since obtaining the system control-AoI, which is the summation of all devices' control-AoI values, is challenging due to the lack of information about distributed devices, we enhance our AAFA scheme by integrating an estimation mechanism for the system control-AoI. Through comprehensive simulations, we show the effectiveness of this introduced estimation algorithm. The findings underscore that the AAFA scheme not only reduces average AoI but also achieves nearly optimal system throughput, which is appealing to LEO satellite IoT. © 2023 IEEE.

关键词： Age of Information frame-adaptive online estimation random access

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Embedding Climate Dynamics and Prediction with Deep Learning for Wind Power Forecasting: Short-Term to Long-Term Perspective

Embedding Climate Dynamics and Prediction with Deep Learning...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kim, Hana Lee, Yong Oh Ok, Changsoo Kim, Dongkyun Baek, Seungyup Hongik University Dept. of Computer Engineering Seoul Korea Republic of Hongik University Dept. of Industrial and Data Engineering Seoul Korea Republic of Hongik University Dept. of Civil Engineering Seoul Korea Republic of Vgen Inc. Seoul Korea Republic of

ISBN: (纸本)9798350324457

Wind power generation plays an increasingly significant role in the global shift towards renewable energy sources for climate mitigation. However, its susceptibility to climate variability underscores the critical importance of accurate energy generation prediction for ensuring a stable energy supply. In this paper, we analyze the correlation between climate data and energy generation data, extracting essential factors based on this analysis. We propose a Convolutional Neural Network-based model capable of four-hour short-term forecasting by representing these factors as embedding matrices. Furthermore, we combine this model with a Long Short Term Memory model to extend the forecasting period to 24 hours, validating its performance in the day-ahead market bidding context. Using empirical data from South Korea, our short-term forecasting model achieved an accuracy of 76%, while the long-term model demonstrated 85% accuracy, highlighting its potential for practical applications in wind energy generation and market operations. © 2023 IEEE.

关键词： Climate Dynamics Data Representation Deep Learning Long-Term Prediction Short-Term Prediction Temporal Analysis Wind Power Forecasting

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Early Detection of Alzheimer's Disease using Neuro Imaging and Deep Learning Techniques 7

Early Detection of Alzheimer's Disease using Neuro Imaging a...

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7th SLAAI - International Conference on Artificial Intelligence, SLAAI-ICAI 2023

作者： Ahamed, A.U.F. Hamdhee, A. Aashiq, M.N.M. Hirshan, R. Kumara, W.G.C.W. South Eastern University of Sri Lanka Dept. of Electrical and Telecommunication Engineering Oluvil Sri Lanka South Eastern University of Sri Lanka Dept. of Computer Science and Engineering Oluvil Sri Lanka

ISBN: (纸本)9798350319262

Accurately diagnosing the stages of Alzheimer's disease (AD) using MRI images poses a challenge for medical practitioners, as even experienced professionals can make mistakes in diagnosing the stage. Traditional solutions heavily rely on various MRI pre-processing techniques to improve diagnostic accuracy. In recent years, deep learning methods, particularly Convolutional Neural Networks (CNNs), have shown promise in AD diagnosis through classification approaches. However, there is still a need to bridge the research gap in utilizing deep learning techniques for object detection in Alzheimer's disease diagnosis. In this research, comprehensive comparisons are conducted and various techniques to address this gap are explored, aiming to identify the most effective approach for solving this diagnostic challenge. We introduce hyperparameter tuning in our CNN model to identify the best parameters for achieving optimal accuracy by considering execution time. Next, we compare the accuracy and loss metrics of various CNN architectures' respective execution times. Additionally, we examine the impact of dataset augmentation techniques and the number of data samples on accuracy, loss, and execution times. Our proposed CNN model incorporates the latest techniques, featuring the EfficientNetV2S architecture. Hence, our work provides a benchmark for deep learning-based Alzheimer's disease stage detection, categorizing individuals into four stages Mild Demented, Moderate Demented, Non Demented, and Very Mild Demented. The utilization of the dataset using MRI data from the ADNI, with its extensive 2D slices for each stage, enables the extraction of meaningful features and patterns associated with Alzheimer's disease pathology. The outcomes of this study contribute to the advancement of diagnostic tools and therapeutic interventions, enhancing the understanding and management of Alzheimer's disease. © 2023 IEEE.

关键词： Magnetic resonance imaging

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A Room Temperature Compensated Lateral Field Excited Lithium Tantalate Sensor Platform

A Room Temperature Compensated Lateral Field Excited Lithium...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Trusty, Yuri Khmeleva, Ekaterina McGann, Jason Hartz, Jequil Emanetoglu, Nuri Vetelino, John University of Maine Dept. of Electrical and Computer Engineering OronoME United States University of Washington Bothell Dept. of Physics BothellWA United States University of Maine Dept. of Mechanical Engineering OronoME United States

ISBN: (纸本)9798350346459

The quartz crystal monitor (QCM) is a common sensor platform based on the room temperature compensated pure shear mode (PSM) of thickness field excited (TFE) AT-cut quartz. However, with electrodes on both crystal faces, TFE only allows sensing of mechanical property changes. Lateral field excitation (LFE), where both electrodes are on a single face, enables the detection of both mechanical and electrical changes, potentially leading to higher sensitivity. As lithium tantalate (LT) has an LFE PSM and piezoelectric coupling several times greater than that of quartz, LFE LT was chosen as a possible replacement for TFE quartz. A theoretical search of all LT cuts identified those exhibiting a room temperature compensated PSM. A set of orientations ranging from (YXwl) 0°/-85° to 0°/-90° was chosen for experimental verification. The temperature response of each sample was shown to be parabolic, with a roughly linear relationship between crystal cut angle and temperature inflection point/turnaround temperature. Specifically, the (YXwl) 0°/-87° cut with a turnaround temperature at 26.4°C demonstrates that a room temperature PSM in LT can be excited via LFE. Future work focusing on the development of an LT sensing platform could profoundly impact sensor systems in agriculture, homeland security, global warming, and medical applications. © 2023 IEEE.

关键词： Electrodes

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A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting

arXiv

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arXiv 2024年

作者： Chakraborty, Arya Basu, Auhona Dept. of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Dept. of Computer Engineering York University Toronto Canada

The financial domain presents a complex environment for stock market prediction, characterized by volatile patterns and the influence of multifaceted data sources. Traditional models have leveraged either Convolutional Neural Networks (CNN) for spatial feature extraction or Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, with limited integration of external textual data. This paper proposes a novel Two-Level Conv-LSTM Neural Network integrated with a Large Language Model (LLM) for comprehensive stock advising. The model harnesses the strengths of Conv-LSTM for analyzing time-series data and LLM for processing and understanding textual information from financial news, social media, and reports. In the first level, convolutional layers are employed to identify local patterns in historical stock prices and technical indicators, followed by LSTM layers to capture the temporal dynamics. The second level integrates the output with an LLM that analyzes sentiment and contextual information from textual data, providing a holistic view of market conditions. The combined approach aims to improve prediction accuracy and provide contextually rich stock advising. © 2024, CC BY-SA.

关键词： Prediction models

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Human vs AI: Evaluating the Effectiveness of Deep Learning in AI Text Detection

Human vs AI: Evaluating the Effectiveness of Deep Learning i...

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International Conference on computer and Information Technology (ICCIT)

作者： Taohidur Rahman Muhammad Mizanur Rahman Mizan Tanjim Mahmud Swagata Roy Mohammad Kamal Uddin Hifju Huma Khanam Redhi Shanjida Rashid Priya Abubokor Hanip Mohammad Shahadat Hossain Dept. of Computer Science and Engineering International Islamic University Chittagong Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Computer Science and Engineering BGC Trust University Bangladesh Bangladesh Dept. of Computer Science and Engineering Chittagong University of Engineering & Technology Chittagong Bangladesh Washington University of Science and Technology (WUST) Alexandria VA USA Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

This study looks on the effectiveness of fine-tuned DistilBERT and LSTM models for distinguishing between human-written and AI-generated text. We address the growing demand for reliable AI text identification as an outcome of the potential misuse of large language models (LLMs) in instances related to the transmission of misleading data. This approach combines advantage of the beneficial characteristics of both model, LSTMs’ capacity to extract sequential information from text input, and DistilBERT’s pre-trained interpretation of contextualize associations. We build these models employing a set of labels that includes writings created through machines and people indiscriminately. The evaluation metrics exhibit outstanding achievement, with DistilBERT and LSTM achieving 100% classification accuracy (1.00) of the test sample. By stimulating the ethical use of LLMs while minimizing any adverse effects, the study advances the development of dependable methods that can identify text composed with machines with intelligence.

关键词： Measurement Deep learning Ethics Large language models Text detection Writing Natural language processing Reliability Information technology Long short term memory

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