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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Harshada Gole Subodh Daronde Deparment of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Biomedical Enginering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

With the advent of bioinformatics, cancer research has entered a new age wherein vast amounts of complex biological data can be interpreted to create powerful treatments. This approach has exceptionally improved the identification of biomarkers, deciphering hidden patterns contained within data, and developing personalized treatment modalities by incorporating bioinformatics-based strategies with artificial intelligence and machine learning. Advances in bioinformatics will soon give insight into the cancer process at cellular levels and offer a better understanding of tumor heterogeneity and evolution. Increased reliance on bioinformatics has directly affected the development of newer immunotherapy forms. In the last year, there have been 13 new FDA-approved immunotherapy treatments for 10 different drugs and 11 types of cancers specific to each one. The future of bioinformatics in cancer research is bright, and the field is likely to be further revolutionized with the continued progress in artificial intelligence, machine learning, and single-cell analysis techniques

关键词： Protocols Precision medicine Immunotherapy Machine learning Proteomics Biomarkers Bioinformatics Prognostics and health management Cancer Tumors

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Effective Density-Based Concept Drift Detection for Evolving Data Streams 24th

Effective Density-Based Concept Drift Detection for Evolving...

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24th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2023

作者： Cui, Zelin Tian, Hui Shen, Hong Institute of Information Security Engineering Chinese Academy of Sciences Beijing China School of Information and Communication Technology Griffith University Brisbane Australia Faculty of Applied Sciences Macao Polytechnic University China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9789819982103

Concept drift is a common phenomenon appearing in evolving data streams of a wide range of applications including credit card fraud protection, weather forecast, network monitoring, etc. For online data streams it is difficult to determine a proper size of the sliding window for detection of concept drift, making the existing dataset-distance based algorithms not effective in application. In this paper, we propose a novel framework of Density-based Concept Drift Detection (DCDD) for detecting concept drifts in data streams using density-based clustering on a variable-size sliding window through dynamically adjusting the size of the sliding window. Our DCDD uses XGBoost (eXtreme Gradient Boosting) to predict the amount of data in the same concept and adjusts the size of the sliding window dynamically based on the collected information about concept drifting. To detect concept drift between two datasets, DCDD calculates the distance between the datasets using a new detection formula that considers the attribute of time as the weight for old data and calculates the distance between the data in the current sliding window and all data in the current concept rather than between two adjacent windows as used in the exiting work DCDA [2]. This yields an observable improvement on the detection accuracy and a significant improvement on the detection efficiency. Experimental results have shown that our framework detects the concept drift more accurately and efficiently than the existing work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss

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Mathematical Biosciences and engineering 2024年第4期21卷 5521-5535页

作者： Ran, Zhongnan Jiang, Mingfeng Li, Yang Wang, Zhefeng Wu, Yongquan Ke, Wei Xia, Ling School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Department of Cardiology Beijing Anzhen Hospital Capital Medical University No. 2 Anzhen Road Chaoyang District Beijing100029 China School of Applied Sciences Macao Polytechnic Institute China Department of Biomedical Engineering Zhejiang University Hangzhou310027 China

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the prevention and detection of arrhythmia diseases. Due to the similarities in Normal beat (N) and Supraventricular Premature Beat (S) categories and imbalance of ECG categories, arrhythmia classification cannot achieve satisfactory classification results under the inter-patient assessment paradigm. In this paper, a multi-path parallel deep convolutional neural network was proposed for arrhythmia classification. Furthermore, a global average RR interval was introduced to address the issue of similarities between N vs. S categories, and a weighted loss function was developed to solve the imbalance problem using the dynamically adjusted weights based on the proportion of each class in the input batch. The MIT-BIH arrhythmia dataset was used to validate the classification performances of the proposed method. Experimental results under the intra-patient evaluation paradigm and inter-patient evaluation paradigm showed that the proposed method could achieve better classification results than other methods. Among them, the accuracy, average sensitivity, average precision, and average specificity under the intra-patient paradigm were 98.73%, 94.89%, 89.38%, and 98.24%, respectively. The accuracy, average sensitivity, average precision, and average specificity under the inter-patient paradigm were 91.22%, 89.91%, 68.23%, and 95.23%, respectively. © 2024 the Author(s).

关键词： Diseases

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LBC-IoT: Lightweight Block Cipher for IoT Constraint Devices

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computers, Materials & Continua 2021年第6期67卷 3563-3579页

作者： Rabie A.Ramadan Bassam W.Aboshosha Kusum Yadav Ibrahim M.Alseadoon Munawar J.Kashout Mohamed Elhoseny College of Computer Science and Engineering University of Ha’ilHa’il81481Saudi Arabia Department of Computer Engineering Faculty of EngineeringCairo UniversityCairo12613Egypt Department of Computer Engineering Higher Institute of EngineeringElshorouk AcademyCairoEgypt Faculty of Computers and Information Mansoura UniversityEgypt College of Computer Information Technology American University in the EmiratesUnited Arab Emirates

With the new era of the Internet of Things(IoT)technology,many devices with limited resources are *** devices are susceptible to a signicant number of new malware and other risks emerging *** of the most appropriate methods for securing those IoT applications is cryptographic algorithms,as cryptography masks information by eliminating the risk of collecting any meaningful information *** ensures that all data communications are private,accurate,authenticated,authorized,or *** conventional cryptographic algorithms have been developed specically for devices with limited resources;however,it turns out that such algorithms are not ideal for IoT restricted devices with their current ***,lightweight block ciphers are gaining popularity to meet the requirements of low-power and constrained devices.A new ultra-lightweight secret-key block-enciphering algorithm named“LBC-IoT”is proposed in this *** proposed block length is 32-bit supporting key lengths of 80-bit,and it is mainly based on the Feistel ***-efcient cryptographic features in“LBC-IoT”include the use of simple functions(shift,XOR)and small rigid substitution boxes(4-bit-S-boxes).Besides,it is immune to different types of attacks such as linear,differential,and side-channel as well as exible in terms of ***,LBC-IoT achieves reasonable performance in both hardware and software compared to other recent ***-IoT’s hardware implementation results are very promising(smallest ever area“548”GE)and competitive with today’s leading lightweight ***-IoT is also ideally suited for ultra-restricted devices such as RFID tags.

关键词： Security internet of things cryptographic algorithms block cipher lightweight algorithms

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network 4

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Guduri, Naga Venkata Ramakrishna Charaan, R.M. Dilip Kalaiyarasi, R. Samuthira Pandi, V. Reddy, D.Laxma Selvaprabhu, T. University of Technology and Applied Sciences Faculty of Engineering Al Musannah Oman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Dhanalakshmi College of Engineering Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types. © 2024 IEEE.

关键词： Spatio-temporal data

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A low-cost buoyant force measurement device for monitoring sugar concentration in water solution

A low-cost buoyant force measurement device for monitoring s...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Luka Kaučić Ivana Kovačević Ivan Aleksi Tomislav Keser Josip Zidar Snježana Rimac-Drlje Department of Computer Engineering and Automation Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia Department of Communications Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

Monitoring sugar concentration during fermentation is crucial for producing high-quality alcoholic beverages. Traditional methods for measuring sugar concentration can be costly and time-consuming, especially for small-scale producers. In this study, we developed a low-cost buoyant force measurement device for monitoring sugar concentration in water solutions. The device consists of a buoyant object fully submerged in the solution and connected to a load-cell sensor. As the sugar concentration in the liquid increases, the buoyant force on the object increases, and the load-cell measures this force. Proposed device is calibrated using solutions of known sugar concentrations and its high accuracy and precision is presented. The device is tested in controlled environment to ensure accurate tracking changes in sugar concentration over time. Proposed device can be a valuable tool for small-scale producers looking to optimize their fermentation processes while minimizing costs.

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Development of Smart Beehive Frame for Multi-Parameter Monitoring 33

Development of Smart Beehive Frame for Multi-Parameter Monit...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Milovanovic, M. Pejic, J. Pejic, P. University of Niš Faculty of Electronic Engineering Department of Electronics Niš18000 Serbia University of Niš Faculty of Sciences and Mathematics Department of Computer Science Niš18000 Serbia Belgrade Metropolitan University Faculty of Information Technology Serbia

ISBN: (纸本)9798350347760

Honeybees are essential for pollination in agriculture. This paper presents a smart beehive frame for multi-parameter monitoring. The system is based on opto elements that emit IR light to the honeycomb and measure the reflection. The results showed that this kind of system can be used in practice to decrease the number of the visual on-site observations of the bee colonies. © 2023 IEEE.

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Anime Sketch Colourization Using Enhanced Pix2pix GAN 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Mudhalwadkar, Nikhil Prashant Mokayed, Hamam Alkhaled, Lama Shivakumara, Palaiahnakote Hum, Yan Chai Lulea Tekniska Universitet Lulea Sweden Centre of Image and Signal Processing Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Mechatronics and Biomedical Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Petaling Jaya Malaysia

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Coloring manga sketches is challenging due to the wide gap between the meaning of color and sketches. Unlike Western comics, Japanese manga sketch does not provide color, rather they are in binary form using a screening technique. These styles differ in how regions are filled, but converting between them is a difficult task that is typically done manually. This paper aims to develop a method for coloring Japanese manga composed of sketches and dialogue. Coloring is considered a challenging and time-consuming task for creators. The proposed method proposes using a pix2pix GAN (Generative Adversarial Network) to color a manga sketch when given as input. Additionally, an alternative approach is suggested, which involves modifying the generator loss function to improve the performance. This paper aims to automate the process of coloring a single sketch, with the potential to extend the research in the future to apply the colors consistently across multiple manga strips. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Utilizing Large Language Models for Psychological Assessment: Enhancing Suicide Risk Detection Through Social Media Analysis 6

Utilizing Large Language Models for Psychological Assessment...

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6th International Conference on Frontier Technologies of Information and computer, ICFTIC 2024

作者： Xu, Zeling Xu, Jieping Luo, Yishi Zhang, Keyu Zhang, Jinyuan Zou, Yuntao Liu, Li Macau University of Science and Technology School of Computer Science and Engineering Faculty of Innovation Engineering Avenida Wai Long Macao Special Administrative Region of China Taipa999078 China Guangdong University of Education School of International Studies Guangdong Guangzhou510812 China Guangdong Medical University Second Clinical Medical College Guangdong Dongguan523109 China Hebei Medical University Basic Medical School Hebei Shijiazhuang050017 China Sichuan Film and Television University School of Television Sichuan Chengdu610037 China

ISBN: (纸本)9798331541750

Mental health issues are increasingly prevalent due to the fast-paced lifestyle and rising work stress. Traditional psychological assessments face limitations due to resource scarcity and inefficiency. This paper presents a novel system using large language models (LLMs) to detect suicidal tendencies through social media analysis. Our system integrates prompt engineering and Retrieval-Augmented Generation (RAG) techniques to enhance detection accuracy. The architecture includes a knowledge retrieval-enhanced component and a judgment component, utilizing a fine-tuned LLM. Experiments with a Kaggle dataset demonstrate high performance, with consistent accuracy, precision, recall, and F1-Score across multiple runs. This system offers a promising solution to improve early psychological assessment and intervention, addressing the mental health resource gap. Future work aims to optimize the model and explore broader applications in psychological research. © 2024 IEEE.

关键词： Risk assessment

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Automated Detection of Defects on Metal Surfaces using Vision Transformers

arXiv

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arXiv 2024年

作者： Alaa, Toqa Kotb, Mostafa Zakaria, Arwa Diab, Mariam Gomaa, Walid Department of Computer Science and Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Engineering Alexandria University Alexandria Egypt

Metal manufacturing often results in the production of defective products, leading to operational challenges. Since traditional manual inspection is time-consuming and resource-intensive, automatic solutions are needed. The study utilizes deep learning techniques to develop a model for detecting metal surface defects using Vision Transformers (ViTs). The proposed model focuses on the classification and localization of defects using a ViT for feature extraction. The architecture branches into two paths: classification and localization. The model must approach high classification accuracy while keeping the Mean Square Error (MSE) and Mean Absolute Error (MAE) as low as possible in the localization process. Experimental results show that it can be utilized in the process of automated defects detection, improve operational efficiency, and reduce errors in metal manufacturing. © 2024, CC BY-NC-ND.

关键词： Distribution transformers

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