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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data

Exploring Deep Insights from Vast Data: An Overview of Deep ...

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International Conference on Electrical engineering and computer science (ICECOS)

作者： Sarhad Baez Hasan Shahab Wahhab Kareem Didar Dlshad Hamad Ameen Computer Science Department Soran University Faculty of Science Soran Iraq Department of Information System Engineering Department of Computer Engineering Erbil Polytechnic University College of Engineering Knowledge University Erbil Iraq

ISBN: (数字)9798350368253

ISBN: (纸本)9798350368260

Big data has emerged very fast, and this has brought both opportunities and problems that are related to the application of deep learning. This paper explores how deep learning can be implemented using big data and in particular the Apache Mahout library. Unlike most other machine learning tools, Mahout can perform distributed computation, especially while using the MapReduce frameworks including Apache Hadoop and Apache Spark. However, this study does not only focus on the explanation of the need for Mahout's integration and employment but also carries out a high-fidelity argument on how deep learning algorithms can be incorporated in the Mahout framework to optimize the manipulation of large and complicated datasets. Mahout-based contributions include the comparative analysis of key deep learning paradigms including distributed processing, parallel processing and transfer learning. This paper also includes examples of the Mahout deep learning integration use and its application in real contexts. This article describes the challenges that currently organizations face in handling big data and indeed, the potential threats these challenges pose to the success of deep learning techniques when used in organizations’ big data processing and analysis.

关键词： Deep learning Scalability Transfer learning Cluster computing Organizations Big Data Parallel processing Sparks Security Research and development

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Advances in Medical Image Processing for Liver Tumour Recognition: A Comprehensive Survey

Advances in Medical Image Processing for Liver Tumour Recogn...

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computer, communication and Control (IC4), International Conference on

作者： B. Deepa C. N. Marimuthu S. Geetha S. Maheswari Faculty of Information and Communication Engineering Anna University Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

The last several decades have seen a lot of activity in the medical image processing domain especially when it comes to the segmentation of liver and liver tumours. The survival rate of liver cancer is rather low when compared to some other types of cancer. Nevertheless, early detection greatly improves the prognosis of liver cancer patients. As a consequence, multiple researchers have developed automated deep-learning (DL) systems for forecasting the development of cancer cells using various medical imaging modalities. A summary of some previously published studies on liver cancer diagnosis is currently lacking in review publications. But new mechanisms and architecture in the prognosis of liver cancer were beyond the possibility of these investigations. This review emphases on the topologies of DL for liver cancer diagnosis. The survey that follows examines the datasets used, describes existing DL-based designs, assesses the benefits and drawbacks of earlier studies, and discusses image processing techniques. Furthermore, a thorough examination of several imaging modalities, performance metrics and results, challenges and future research goals are provided. The comparison study of different methods reveals that HFCNN has the highest accuracy, at 97.22%.

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Generating synthetic mixed-type tabular data by decoding samples from a latent-space: a case study in healthcare

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Procedia computer science 2024年 246卷 2254-2263页

作者： Jarosław Drapała Jerzy Świątek Department of Computer Science and Systems Engineering Faculty of Information and Communication Technology Wrocław University of Science and Technology Wrocław 50-370 Poland

Medical data are subject to privacy regulations, which severely limit AI specialists who wish to construct decision support systems for medicine. Large amounts of this data are tabular, indicating that they are organized into a table format, where patient records are represented in rows and measured variables in columns. Furthermore, the variables come in different types—some are numerical, while others are categorical. In this work, we introduce a novel method for constructing generators of synthetic tabular data with mixed types. The key point of our approach is the explicit utilization of a latent space to represent the original data. A case study using real medical data is presented.

关键词： Multidimensional scaling Kernel density estimation Random forest regression Support Vector Machine

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Evaluation of time-series database on microservice architecture for health monitoring system

Evaluation of time-series database on microservice architect...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Simanjuntak, Eko Surantha, Nico Isa, Sani Muhamad Bina Nusantara University Binus Graduate Program-Master of Computer Science Computer Science Department Jakarta11480 Indonesia Tokyo City University Electronic and Communication Engineering Faculty of Engineering Department of Electrical Tokyo Japan

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

Recently, the research on daily health monitoring using a wearable sensor has been continually evolving. In the future, when this system is actually implemented, a vast amount of data transmission will be conducted from multiple sensors to the cloud server. A system capable of transmitting, storing, processing, and visualizing massive volumes of data is necessary for health monitoring. This research aims to develop a health monitoring system that can accommodate a high amount of data with high performance. The proposed techniques include numerous modifications to system architecture and technology. In the system architecture, a multi-database is proposed to enhance the reliability of the system. As an enhancement to the technology aspect, a time series database and MQTT server are proposed. As the result, the proposed system achieves significantly better performance in terms of throughput, database-writing speed, and database-reading speed compared to the existing system. © 2022 IEEE.

关键词： Database systems

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Effects of Alternative Scatterplot Designs on Belief 25

Effects of Alternative Scatterplot Designs on Belief

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2025 CHI Conference on Human Factors in Computing systems, CHI 2025

作者： Strain, Gabriel Stewart, Andrew J. Jay, Caroline Rutherford, Charlotte Warren, Paul A. Department of Computer Science Faculty of Science and Engineering University of Manchester Manchester United Kingdom Division of Psychology Communication and Human Neuroscience Schoolof Health Sciences Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom

ISBN: (纸本)9798400713941

Viewers tend to underestimate correlation in positively correlated scatterplots. However, systematically changing the size and opacity of scatterplot points can bias estimates upwards, correcting for this underestimation. Here, we examine whether the application of these visualisation techniques goes beyond a simple perceptual effect and could actually influence beliefs about information from trusted news sources. We present a fully-reproducible study in which we demonstrate that scatterplot manipulations that are able to correct for the correlation underestimation bias can also induce stronger levels of belief change compared to conventional scatterplots presenting identical data. Consequently, we show that novel visualisation techniques can be used to drive belief change, and suggest future directions for extending this work with regards to altering attitudes and behaviours. © 2025 Copyright held by the owner/author(s).

关键词： Visualization

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Emotion Unleashed: Real-Time FER in Video via Advanced Deep Learning Models 1

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9th Future Technologies Conference, FTC 2024

作者： Thakkar, Bhaumikkumar Bhatt, Chintan Patel, Parth Patel, Mire Bruno, Alessandro Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University GJ Gandhinagar382007 India Department of Business Law Economics Consumer Behaviour ‘Carlo A. Ricciardi’ Faculty of Communication IULM University Milan20143 Italy

ISBN: (数字)9783031731105

ISBN: (纸本)9783031731099

While conversing, showing emotions through facial expressions comes naturally to human beings. Face expression detection is tackled by computer vision to serve psychology, human-computer interaction, and security applications. In this work, we present the software we have designed and implemented to read the emotions on people’s faces. The system allows users to discern emotions on people’s faces using artificial intelligence. It is a challenging task that has piqued the interest of scholars worldwide in facial expression analysis in recent years. Regarding some technical aspects of the proposed method, we rely on Deep learning methods, which have proved robust in various scenarios, including facial emotion recognition. This paper introduces a Deep Learning-based approach that relies on a Convolutional Neural Network (CNN) to infer knowledge from facial images and distinguish features. These features are then used to assign a label to an emotion. Deep Learning algorithms, such as DeepFace, NASNet Mobile, EfficientNet V2, and Inception V2, are investigated on FER tasks. By analyzing data from the FER-13 and Face databases, our model has learned to recognize emotions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Battery Monitoring system on IoT-Based Smart Micro Grid

Battery Monitoring System on IoT-Based Smart Micro Grid

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Electronic and Electrical engineering and Intelligent system (ICE3IS), International Conference on

作者： Nur Iksan Djoko Adi Widodo Djuniadi Djuniadi Riana Defi Mahadji Putri Agus Suryanto Erika Devi Udayanti Department of Electrical Engineering Faculty of Engineering Universitas Negeri Semarang Indonesia Department of Information System Faculty of Computer Science Universitas Dian Nuswantoro Indonesia

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

The use of batteries as an energy storage medium has a very important role in the installation of renewable energy power plants, such as photovoltaics to overcome intermittency in photovoltaics and to maintain stable power quality. In this study, the installation of solar plants that was used as the research object was a smart micro grid that integrates photovoltaic electrical energy sources with the Utility electricity network for residential scale. The battery monitoring system is used to manage the performance of existing batteries on the electricity grid through analysis of the battery's electrical values such as current, voltage, power and energy, so that it can function optimally in storing electrical energy and supplying electricity to users. Internet of things (IoT) technology is used to provide easy transfer of data obtained through the process of data acquisition to data visualization provided to be accessed by users in real time. This research aims to develop an IoT-based battery monitoring system. The development carried out focuses on the data acquisition process, the data transfer process using IoT technology, the process of analyzing and visualizing data on the IoT cloud and remote access to users.

关键词： Photovoltaic systems Cloud computing Electricity Data acquisition Data visualization Data transfer Batteries Smart grids Internet of Things Monitoring

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A Comprehensive Analysis of Identifying Patients with Chronic Kidney Disease

A Comprehensive Analysis of Identifying Patients with Chroni...

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computer, communication and Control (IC4), International Conference on

作者： S. Geetha C. N. Marimuthu S. Maheswari B. Deepa Faculty of Information and Communication Engineering Anna University Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

Chronic Kidney Disease (CKD) is a prevalent and progressive health condition affecting millions of individuals worldwide. Early identification and management of CKD are critical to mitigate its adverse effects on patients’ health and reduce the burden on healthcare systems. This study presents a comprehensive analysis of various methodologies and approaches for identifying patients with Chronic Kidney Disease. The research encompasses a detailed review of traditional and emerging diagnostic techniques, by exploring the sensitivity, specificity, and accuracy of these methods, considering their applicability in diverse patient populations and healthcare settings. Furthermore, this analysis delves into the role of artificial intelligence (AI) and machine learning in enhancing CKD detection and risk prediction. The synthesis of this comprehensive analysis provides valuable insights into the current landscape of CKD identification and offers recommendations for optimizing diagnostic strategies.

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Temporal Preferences-Based Utility Control for Smart Homes

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Intelligent Automation & Soft Computing 2023年第5期36卷 1699-1714页

作者： Salman Naseer Raheela Saleem Muhammad Mudasar Ghafoor Shahzada Khurram Shafiq Ahmad Abdelaty Edrees Sayed Muhammad Shafiq Jin-Ghoo Choi Department of Information Technology University of the PunjabGujranwala Campus52250Pakistan Department of Computer Science Gift UniversityGujranwalaPunjab52250 Pakistan University of the Punjab Jehlum CampusJehlum49600Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The residential sector contributes a large part of the energy to the global energy *** date,housing demand has mostly been uncontrollable and inelastic to grid *** the performance of a home energy manage-ment system requires the creation of various proﬁles of real-world residential demand,as residential demand is complex and includes multiple factors such as occupancy,climate,user preferences,and appliance *** Peak Ratio(A2P)is one of the most important parameters when managing an efﬁcient and cost-effective energy *** the household level,the larger relative magni-tudes of certain energy devices make managing this ratio critical,albeit difﬁ*** Demand Response(DR)and Demand Side Management(DSM)systems have been proposed to reduce this ratio to *** main ways to achieve this are economic incentives,user comfort modeling and control,or *** this study,we propose a unique opportunistic social time approach called the Time Utility Based Control Feature(TUBCF),which uses the concept of a utility function from economics to model and control consumer *** propose a DR model for residential customers to reduce Peak-to-Average Ratio(PAR)and improve customer satisfaction by eliminating Appliance Wait Time(WTA)during peak *** PAR reduction and WTA,we propose a system architecture and mathematical *** proposed model automatically schedules devices based on their temporal preferences and considers six households with different device types and operational *** results show that using this strategy can reduce A2P by 80%and improve user comfort during peak hours.

关键词： Temporal preferences utility control ﬁxed priority demand shaping

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