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Information Technologies and Communications (GCITC), Global Conference on

作者： E. Chandralekha Jemin V.M T. Anitha V. Deepa V. Usha Ravikumar S Dept. of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Dept. of CSE R.M.K College of Engineering Chennai India Department of Artificial Intelligence and Data Science Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Artifical Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

The economic impact of the infectious bovine virus known as Lumpy Skin Disease (LSD) is substantial. Because of the poxvirus that causes LSD, cattle suffer from decreased meat and milk output, higher veterinary bills, and trade restrictions on cattle-related products. The biggest difficulty in managing LSD epidemics is identifying them at an early stage. Because they are laborious and time-consuming, traditional procedures result in delayed responses. Our research paper provides a comprehensive evaluation of ensemble machine learning models for LSD prediction to help overcome these obstacles. Machine learning models have been shown to be valuable in the field of epidemiology due to their ability to successfully analyse complex data. In this research, a number of ensemble models that have been pre-trained on parameters including location, climate, and disease outbreak history. We also use data preparation methods to equalize data and standardize inputs, for a more reliable study. In order to better understand the patterns of LSD spread, data visualization tools like heatmaps and box plots are used. Our studies' overarching goal is to improve disease management measures by making use of ensemble machine learning models and data analysis strategies, which will have positive effects on cattle health and the economic security of the livestock industry.

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Forecasting Model of Wind Speed and Direction by Convolutional Neural Network - Deep Convolutional Long Short Term Memory

Forecasting Model of Wind Speed and Direction by Convolution...

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Information Technology International Seminar (ITIS)

作者： Anggraini Puspita Sari Dwi Arman Prasetya Takashi Yasuno Andreas Nugroho Sihananto Muhammad Muharrom Al Haromainy Wahyu S.J. Saputra Informatics Dept. Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Data Science Dept. Universitas Pembangunan Nasional “Veteran” Jawa Timur Surabaya Indonesia Electrical and Electronic Engineering Dept. Tokushima University Tokushima Japan

ISBN: (纸本)9798350398205

This paper serves forecast wind speed and direction using a convolutional neural network - deep convolutional long short term memory (CNN-DConvLSTM). The forecasting model merges merit between CNN and ConvLSTM to enhance forecasts accuracy of wind speed and direction. The input of the forecasting model is image data on two dimensions (2D) - coordinate, which expresses the time sequential image of the wind vector. The forecasting model consists of encoding and forecasting networks that forecast one hour later. The actual observed data was taken from the Automated Meteorological data Acquisition System (AMeDAS)in Japan. The forecasting model performance was evaluated to mean absolute error (MAE) between forecasted data and observed data. For confirming effectiveness, the forecasting model was compared to the persistent model and fully connected - long short term memory (FC-LSTM). The proposed forecasting model by CNN-DConvLSTM for one-year data can improve by 25% for wind speed and 19.36% for wind direction over the persistent model which is a more significant accuracy than the LSTM model. It indicates that the CNN-DConvLSTM model is the strongest forecast model.

关键词： Seminars Wind speed Two dimensional displays Predictive models Wind power generation data models Convolutional neural networks

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Examination of Phishing Attempts on Web-Based Business Applications and Their Preventions

Examination of Phishing Attempts on Web-Based Business Appli...

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Communication, Computer sciences and engineering (IC3SE), International Conference on

作者： Shahbaz Shaukat Faisal Rehman Gunjan Chhabra Keshav Kaushik Inam Ullah Khan Hanan Sharef Muhammad Zahid Ali Irfan Ghaffar Muhammad Naeem Arshad Dept. of Computer Science & Information Technology Lahore leads University Lahore Pakistan Dept. Statistics & Data Science University of Mianwali Mianwali & National University of Sciences & Tech. NUST Islamabad Pakistan Department of CSE Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Electronic Engineering SEAS Isra University Islamabad Pakistan

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

Because of developments in the online sector, the occurrence of data breaches in web-based business systems is rising quickly. The attackers employ a range of different methods, including Distributed-Denial-of-Service attacks, Hacking Current Sessions, Reusing of Usernames and Passwords, Injections of Structured Query Language, and Phishing Attacks. Phishing attacks are widely used in the modern age. Phishing attacks are frauds attempt for tricking users into giving up sensitive information. Based on the intruder's intended target, such digital threats can damage specific people or whole industries. Researchers in this field offer a variety of phishing identification methods to stop such threats. Throughout this review paper, we categorize modern phishing identification methods into five main groups, which includes framework based on deep learning algorithms and artificial intelligence methodology based on search engines, methodology based on scanned URLs, methodology based on Whitelisting-Blacklisting Using Pictorial Resemblance as a Basis. Additionally, a statistical assessment of every individual category, giving its advantages and disadvantages. Additionally, this article offers users advice on how to prevent cyber-attacks.

关键词： Industries Structured Query Language Reviews Phishing Prevention and mitigation Passwords Search engines

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Summarization of Investment Reports Using Pre-trained Model

arXiv

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arXiv 2024年

作者： Sakaji, Hiroki Kobayashi, Ryotaro Izumi, Kiyoshi Mitsugi, Hiroyuki Kuramoto, Wataru School of Engineering The University of Tokyo Tokyo Japan Data-driven Science Dept. Frontier Technologies Research Center Daiwa Institute of Research Ltd. Tokyo Japan Fund Management Division Daiwa Asset Management Co.Ltd. Tokyo Japan

In this paper, we attempt to summarize monthly reports as investment reports. Fund managers have a wide range of tasks, one of which is the preparation of investment reports. In addition to preparing monthly reports on fund management, fund managers prepare management reports that summarize these monthly reports every six months or once a year. The preparation of fund reports is a labor-intensive and time-consuming task. Therefore, in this paper, we tackle investment summarization from monthly reports using transformer-based models. There are two main types of summarization methods: extractive summarization and abstractive summarization, and this study constructs both methods and examines which is more useful in summarizing investment reports. © 2024, CC BY-NC-ND.

关键词： Decentralized finance

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Multi-Class Clinical Text Annotation and Classification Using Bert-Based Active Learning

SSRN

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SSRN 2022年

作者： Afzal, Muhammad Hussain, Jamil Abbas, Asim Hussain, Maqbool Dept. of Software Sejong University Seoul05006 Korea Republic of Dept. of Data Science Sejong University Seoul05006 Korea Republic of Dept. of Computer Science ST. JHON’S University United States School of Computing and Engineering University of Derby United Kingdom

data-driven approaches require labeled data for autonomous decision-making, but medical data is recorded in structured and unstructured form mostly with no label specified. Manual labeling is an arduous task, and variations in the notes at different levels, including section, sentence, and terms, add to the challenge. Creating well-structured and accurate data to use as a training dataset for a machine learning (ML) model increases the chance of getting meaningful results from the ML models. This paper proposes a multi-class annotation and classification framework that uses four classes: subjective, objective, assessment, and plan, adopted from a well-known medical protocol called SOAP. The SOAP classes capture a clinical context and help reduce misinterpretations when a concept of a similar type is used in different sections of the same clinical document. To illustrate the effectiveness of the proposed methodology, we conduct a set of experiments on clinical notes acquired from a public dataset (i2b2). We observe that our proposed model has eased the onerous job of annotation and achieved a better performance than competitors. The encouraging results of this research demonstrate the potential of combining active learning, transfer learning, and deep learning for automatic annotation to get accurate text classification. © 2022, The Authors. All rights reserved.

关键词： Text processing

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A Study on the Generation of Spectrogram for the Detection of Venous Needle Dislodgement by Image Recognition Using Machine Learning

A Study on the Generation of Spectrogram for the Detection o...

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International Conference on Software engineering Research, Management and Applications (SERA)

作者： Naofumi Nakaya Mutsuki Koizumi Satoshi Watanabe Naruki Shirahama Takayuki Abe Akihiro Watanabe Fac. of Medical Science Juntendo University Japan Chiba Dept. of Electronic Technology Tokyo Electronics College Tokyo Japan Coll. of Bioresource Sciences Nihon University Kanagawa Japan Fac. of Data Science Shimonoseki City University Yamaguchi Japan Dept. of Clinical Engineering Tokyo Women's Medical University Hospital Tokyo Japan Fac. of Health Sciences Nihon Institute of Medical Science Saitama Japan

ISBN: (数字)9798350391343

ISBN: (纸本)9798350391350

The safety and quality of life for the approximately 350,000 dialysis patients in Japan are critically affected by the risk of unintentional needle dislodgement during hemodialysis, which represents a significant cause of medical accidents. This study introduces an innovative approach to detect needle dislodgement, focusing on the nonhemolytic detection of indwelling needle dislodgement. By analyzing the impedance change through capacitive coupling between the patient and a detection circuit, we developed a prototype system that generates spectrograms based on frequency and phase characteristics from 1 kHz to 1 MHz. These spectrograms capture the changes in electrical characteristics associated with needle dislodgement, with alterations in color indicating changes in gain and phase difference. Initial experiments demonstrated the feasibility of using spectrogram analysis to visually distinguish normal from venous needle dislodgement (VND) conditions. Our results emphasize the potential for applying machine learning algorithms to these spectrograms for accurate and automated detection of VND. This could significantly improve patient safety by enabling the early detection of dislodgement events under various conditions, including partial needle contact or leakage at the puncture site. Future research will focus on expanding the dataset of normal and VND state spectrograms for machine learning training, aiming to validate the effectiveness of this technology in clinical settings.

关键词： Training Visualization Prototypes Machine learning Needles Reliability engineering Safety

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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TARGET SPECIFIC DE NOVO DESIGN OF DRUG CANDIDATE MOLECULES WITH GRAPH TRANSFORMER-BASED GENERATIVE ADVERSARIAL NETWORKS

arXiv

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arXiv 2023年

作者： Ünlü, Atabey Çevrim, Elif Sarıgün, Ahmet Yiğit, Melih Gökay Çelikbilek, Hayriye Bayram, Osman Güvenilir, Heval Ataş Koyaş, Altay Kahraman, Deniz Cansen Olğaç, Abdurrahman Rifaioğlu, Ahmet Banoğlu, Erden Doğan, Tunca Biological Data Science Lab Dept. of Computer Engineering Hacettepe University Turkey Dept. of Bioinformatics Graduate School of Health Sciences Hacettepe University Turkey Dept. of Chemistry Middle East Technical University Turkey Dept. of Physics Middle East Technical University Turkey Dept. of Computer Engineering Middle East Technical University Turkey Dept. Of Artificial Intelligence Engineering Bahcesehir University Turkey Cancer Systems Biology Lab Graduate School of Informatics Middle East Technical University Turkey Dept. of Pharmaceutical Chemistry Faculty of Pharmacy Gazi University Turkey Laboratory of Molecular Modeling Evias Pharmaceutical R&D Ltd Turkey Dept. of Electrical and Electronics Engineering Iskenderun Technical University Turkey Institute for Computational Biomedicine Heidelberg University Germany

Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a high potential for designing de novo molecules. However, for them to be useful in real-life drug development pipelines, these models should be able to design drug-like and target-centric molecules. In this study, we propose an end-to-end generative system, DrugGEN, for the de novo design of drug candidate molecules that interact with intended target proteins. The proposed method represents molecules as graphs and processes them via a generative adversarial network comprising graph transformer layers. The system is trained using a large dataset of drug-like compounds and target-specific bioactive molecules to design effective inhibitory molecules against the AKT1 protein, which is critically important in developing treatments for various types of cancer. We conducted molecular docking and dynamics to assess the target-centric generation performance of the model, as well as attention score visualisation to examine model interpretability. Results indicate that our de novo molecules have a high potential for interacting with the AKT1 protein at the level of its native ligands. Using the open-access DrugGEN codebase, it is possible to easily train models for other druggable proteins, given a dataset of experimentally known bioactive molecules. © 2023, CC BY-NC-SA.

关键词： Generative adversarial networks

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Efficient Feature Extraction for Classification of Alzheimer's Abnormalities Using Deep Learning

Efficient Feature Extraction for Classification of Alzheimer...

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Emerging Technology (INCET), International Conference for

作者： Minal A. Zope Rakesh K. Deshmukh Priya Pise Shrikant D. Bhopale Dept. of Computer Engineering AISSMS's Institute of Information Technology Kalinga University Raipur Pune India Kalinga University Raipur India Artificial Intelligence and Data Science Indira College of Engineering and Management Pune India Dr. D. Y. Patil Pratisthan's Colleg of Engineering Salokhenagar Kolhapur India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

Recent machine learning AD predictions employed feature selection and extraction methods. The literature research revealed several attempts to identify AD by using different Deep learning layer and micro-simulation techniques. However, identifying important features that can detect AD extremely early remains a difficult task. Automation, poor classification accuracy, over fitting, etc. are the other challenges while using machine learning algorithms. The recent progression of using deep learning methods has received the significant attention for the automatic AD classification. CNN's strategy, while successful, has certain drawbacks: (1) The CNN model uses local receptive fields, shares weights, and subsamples, (2) deep neural networks need a lot of labeled sample training models to avoid over fitting, which may cause the loss of crucial information like representative features at various frequencies and orientations; and (3) the higher computational overhead is another challenge. Therefore, these challenges motivate us the need of the proposed study. The scope of the proposed study is limited to the classification of AD from the input medical images.

关键词： Deep learning Training Machine learning algorithms Automation Computational modeling Fitting Artificial neural networks

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FedERA: Framework for Federated Learning with Diversified Edge Resource Allocation

FedERA: Framework for Federated Learning with Diversified Ed...

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Federated Learning Technologies and Applications (FLTA), International Conference on

作者： Anupam Borthakur Aditya Kasliwal Asim Manna Dipyan Dewan Debdoot Sheet Centre of Excellence in Artificial Intelligence Indian Institute of Technology Kharagpur West Bengal India Dept. of Data Science and Computer Application Manipal Institute of Technology Manipal Karnataka India Department of Electrical Engineering Indian Institute of Technology Kharagpur West Bengal India

ISBN: (数字)9798350354812

ISBN: (纸本)9798350354829

Federated Learning (FL) is a distributed machine learning (ML) approach that allows multiple devices to train a model while keeping training data private on edge devices. However, existing FL libraries are incapable of: (i) su pporting a wide range of existing FL algorithms, (ii) handling diverse public as well as custom datasets, (iii) discarding unreliable updates from malicious edge devices, (iv) facilitating edge-device training, and (v) implementing model compression to reduce communication overhead. With the rapid progression in AI, there is an increment in the computation power while training the models on edge devices which results in more carbon emission (CE). However, in most of the existing FL frameworks, there is no module for estimating CE. In this paper, we present FedERA, a modular and fully customizable open-source FL framework aiming to address these issues especially also by offering extensive support on heterogeneous edge devices and by incorporating both standalone and distributed training approaches. The integration of new software modules unforeseen in existing FL frameworks not only amplifies the scope of its usability but also fosters environment-friendly FL. We believe that FedERA will be a valuable tool for researchers and practitioners working in FL.

关键词： Training Federated learning Operating systems Computational modeling Training data Carbon dioxide Real-time systems Software Security Usability

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