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7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021

作者： Lestari, Nur Indah Bekhit, Mahmoud Mohamed, Mohamed Ali Fathalla, Ahmed Salah, Ahmad 2 Messiter Street CampsieNSW2194 Australia Department of Mathematics Faculty of Science Suez Canal University Ismailia41522 Egypt School of Electrical and Data Engineering University of Technology Sydney Sydney2007 Australia 2P Perfect Presentation Riyadh105523 Saudi Arabia Faculty of Computers and Informatics Zagaizg University Zagazig44519 Egypt

ISBN: (纸本)9783030959869

Nowadays, IoT monitoring systems are ubiquitous. These systems utilized sensors to measure the temperature indoors or outdoor. These sensors can be temporarily unavailable for several reasons, such as power outages. Thus, the server that collects the temperatures should find an alternative for predicting the temperature during the downtime of temperature sensors. In this context, there are several machine learning models for predicting temperature. This work is motivated to study the performance gap of predicting outdoor and indoor temperatures. In the proposed study, we utilized a deep learning recurrent neural network called Gated Recurrent Units (GRUs) and four machine learning models, namely, random forest (RF), decision trees (DT), support vector machines (SVM), and linear regression (LR) for predicting the temperature during the downtimes of the temperature sensors. Then, we evaluated the proposed models on a realistic dataset. The results show that predicting the indoor temperature is more predictable than the outdoor temperature. Moreover, the results revealed that the SVM model was the most accurate model for this task. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Temperature sensors

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A Novel Dual Prediction Scheme for data Communication Reduction in IoT-Based Monitoring Systems 7th

A Novel Dual Prediction Scheme for Data Communication Reduct...

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7th EAI International Conference on the Internet of Things as a Service, IoTaaS 2021

作者： Fathalla, Ahmed Salah, Ahmad Mohamed, Mohamed Ali Lestari, Nur Indah Bekhit, Mahmoud Department of Mathematics Faculty of Science Suez Canal University Ismailia41522 Egypt Faculty of Computers and Informatics Zagaizg University Zagazig44519 Egypt 2P Perfect Presentation Riyadh105523 Saudi Arabia 2 Messiter Street CampsieNSW2194 Australia School of Electrical and Data Engineering University of Technology Sydney Sydney2007 Australia

ISBN: (纸本)9783030959869

Internet of things (IoT) based monitoring systems became commonplace. These systems are built upon a large number of devices and sensors. The data collection task of a large number of sensors and devices in an IoT system includes a massive number of data communications. The more the number of devices, the critical is the network bottleneck. In this context, the dual prediction scheme was proposed as a solution for mitigating the large size of communication volumes. The dual prediction scheme consists of a model for predicting future measurements based on historical data. This model is duplicated on both sides, the edge side (i.e., sensor) and the data collection device (i.e., cluster head). The literature includes several works which proposed many dual prediction schemes based on several techniques such as filters and moving average. The literature does not include utilizing the ensemble learning models. This motivates this work to investigate the gradient boosting regression model’s performance compared to the existing solutions. The proposed and state-of-the-art models are evaluated on a realistic dataset. The obtained results show that the proposed model outperforms the existing dual prediction schemes in terms of communication reduction. © 2022, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Internet of things

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Real-Time Sequential MOGA-GA-PSO Based Optimization for Toxin-Free Composting

Real-Time Sequential MOGA-GA-PSO Based Optimization for Toxi...

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Machine Intelligence and Smart Innovation (ICMISI), International Conference on

作者： Riham Abdallah ZeinEldin Amr Khamis Mahmoud Yasser F. Hassan R&D Dept. Industry Service Complex AASTMT Alexandria Egypt Faculty of Computers and Data Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350365740

ISBN: (纸本)9798350365757

Composting is a method of recycling food waste, and it is considered a significant aspect of waste management and soil enrichment, but this process may face risks of developing phytotoxicity in resulted compost thereby hindering plant growth. This paper, therefore, introduces a novel multistage sequential optimization algorithm that aims at minimizing the occurrence of phytotoxic effects as well as ensuring safe compost production. Each stage optimizes certain key parameters responsible for risk development and uses suitable techniques adaptively based on phase requirements resulting in minimum levels of phytotoxicity. Additionally, it uses real-time monitoring and indirect indicators to predict potential risks where toxicity score is calculated continuously during the process of composting and acts as an early warning indicator. According to the priority established to plant health and eco-friendly composting, phytotoxicity problems have been addressed in our methodology thereby contributing to sustainable practices.

关键词： Waste management Measurement Technological innovation Toxicology Composting Production Soil

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New Approach of Artificial Intelligence in Digital Forensic Investigation: A Literature Review 1

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International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. Computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Hybrid Iris Recognition: Leveraging HOG Features and Dense Networks for Enhanced Biometric Accuracy

Hybrid Iris Recognition: Leveraging HOG Features and Dense N...

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Future Telecommunications and Artificial Intelligence (IC-FTAI), International Conference on

作者： Esraa Tarek Shawkat Guirguis Magda Madbouly Information Technology Department Institute of Graduate Studies and Research Alexandria University Alexandria Egypt Dean of the Faculty of Computers and Data Science Alexandria University Alexandria Egypt

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

The article describes a new method for iris recognition, based on a hybrid approach combining edge detection, Histogram of Oriented Gradients (HOG) features, and dense neural networks(DNN), enabling improved accuracy and reduced training time. Using Circular Hough Transform (CHT)segmentation and Canny Edge Detection for localization, followed by feature extraction through HOG, the authors train the data using dense networks. The results show a 92.37% accuracy on the CASIA-Iris-Interval dataset and 96.28% on the IITD dataset, outperforming previous techniques. The authors illustrate the proposed method with results from MATLAB simulations on three datasets. The hybrid method allows for a reduction in training time by 10 minutes compared to traditional Convolutional Neural Network (CNN)methods. The effectiveness of the new method is confirmed by accuracy improvements of up to 5 %. These research results supplement existing iris recognition techniques and can be used for real-time biometric applications. The novelty and scientific contribution lie in integrating HOG and dense networks, enhancing both accuracy and computational efficiency in biometric systems.

关键词： Training Histograms Accuracy Image edge detection Neural networks Transforms Feature extraction Real-time systems Telecommunications Iris recognition

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Indian Sign Language Recognition System for Interrogative Words Using Deep Learning 1

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International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023

作者： Rawat, Sagar Solomon, Dahlak Daniel Kanwar, Kushal Garg, Sonia Kumar, Karan Mijwil, Maad M. Beňova, Eleonora Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

With around 63 million population of deaf/mute society in India, an effective way of communicating with them is needed in order to understand them as deaf/mute people use hand shape, facial expressions, and body movements for communication. Developing an effective, accessible, and reliable way of communication is vital research in progress in machine translation. In this paper, proposed methodology considers isolated dynamic Indian Sign Language (ISL) gestures to recognize human actions on interrogative words. A self-recorded dataset of such gestures is made to contour the unavailability and variation of such dataset. Video frames are extracted from these dynamic gestures which are fed into proposed CNN architecture for training followed by prediction using Single-Frame CNN method on input videos. The proposed method has been tested with different optimizers and has achieved highest accuracy of 99.6% with Adam optimizer. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Image watermarking algorithm based on quaternion and chaotic Lorenz system

Image watermarking algorithm based on quaternion and chaotic...

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2019第十一届数字图像处理国际会议

作者： Jialiang Peng Bassem Abd El-Atty Hany S.Khalifa Ahmed A.Abd El-Latif School of Data Science and Technology Heilongjiang University Mathematics and Computer Science Department Faculty of Science Menoufia University Computer science department Misr Higher Institute of Commerce and Computers Department of Cybersecurity College of Computer Science and Engineering University of Jeddah

Quaternions are hyper-complex numbers applied in three-dimensional space,which are widely used in physics and image *** this paper,we utilize the features of quaternions for constructing a new substitution box(S-box) based on Lorenz *** performance of the proposed S-box is investigated by S-box evaluation *** results demonstrate that the constructed S-box has significant qualities for viable applications in security *** well as,a new image watermarking mechanism is introduced which its embedding procedures based on the suggested *** results demonstrate the efficiency of the presented image watermarking technique.

关键词： image watermarking Lorenz system quaternion substitution boxes quality analysis

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An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

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Computer Systems science & Engineering 2023年第7期46卷 897-911页

作者： Muhammad Zia Ur Rehman Jawad Ahmad Emad Sami Jaha Abdullah Marish Ali Mohammed A.Alzain Faisal Saeed Department of Electrical Engineering HITEC UniversityTaxila47080Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia DAAI Research Group Department of Computing and Data ScienceSchool of Computing and Digital TechnologyBirmingham City University15 Bartholomew RowBirminghamB55JUUK

Breast cancer is one of the leading cancers among *** has the second-highest mortality rate in women after lung *** detection,especially in the early stages,can help increase survival ***,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s ***,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early *** research presented in this paper is focused on the multi-class classification of breast *** deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound *** proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble *** learning models are fine-tuned along with hyperparameter tuning to achieve better ***,entropy-based feature selection is *** cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,*** performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

关键词： Breast cancer image enhancement ensemble model transfer learning feature selection

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Padma Nyoman Crisnapati I Made Darma Susila I Komang Agus Ady Aryanto Jaturapith Krohkaew Ricky Aurelius Nurtanto Diaz I Made Suandana Astika Pande Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective.

关键词： Fuzzy logic PI control Microcontrollers 2-DOF Manuals Manipulators Three-dimensional printing PD control Root mean square Tuning

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A Novel Image Encryption and Decoding Approach Based on DNA Coding and Arnold's Transformation for IoT Applications

A Novel Image Encryption and Decoding Approach Based on DNA ...

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Telecommunications Conference (ITC-Egypt), International

作者： Nawal Shaltout Ahmed A. Abd El-Latif Waleed Al-Adrousy Mohamed Hammad Abdelhamied A. Ateya Samir Elmougy Department of Computer Science Faculty of Computers and Information Mansoura University Mansoura Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Egypt Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom Egypt Department of Electronics and Communications Engineering Zagazig University Zagazig Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Nowadays, the Internet of Things (IoT) is significantly utilized by an extensive range of applications that lead to the enhancement of different aspects of defense and military capabilities. In IoT, a wide range of smart devices that can communicate with each other are connected through it. Therefore, it is essential to protect multimedia data, especially images with an encryption approach that offers a sufficient degree of security in military applications. Hence, this paper introduces an effective scheme for image encryption and decoding relying on Arnold's transformation DNA coding. It proposes a new pseudo-random number generation PRNG using the Sine-Logistic map and Duffing map called SLDM. The encryption method involves three phases: 1- Key generation and PRNG. 2- confusion phase using the Arnold transformation method. 3- diffusion using DNA rules and operations. The results based on the USC-SIPI dataset, show that the suggested scheme is sufficiently resistant to every kind of attack. Furthermore, the suggested method yields NPCR scores of around 99.62% and UACI scores of approximately 33.48%. Due to its low complexity, it has a great deal of potential for safe and real-time picture applications.

关键词： Resistance Image coding Chaotic communication DNA Encryption Decoding Telecommunications

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