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14th International Conference on ICT Innovations 2022. Reshaping the Future Towards a New Normal, ICT Innovations 2022

作者： Sandjakoska, Ljubinka Bogdanova, Ana Madevska Pejov, Ljupcho Faculty of Computer Science and Engineering UIST St. Paul the Apostle Ohrid Macedonia Faculty of Computer Science and Engineering University SS Cyril and Methodius Skopje Macedonia Faculty of Natural Sciences and Mathematics University SS Cyril and Methodius Skopje Macedonia Department of Chemistry Bioscience and Environmental Engineering Faculty of Science and Technology University of Stavanger Stavanger Norway

ISBN: (纸本)9783031227912

In the last decade, the research community has implemented various applications of deep learning concepts to solve quite advanced tasks in chemistry, ranging from computational chemistry to materials and drug design and even chemical synthesis problems at both laboratory and industrial – grades. Because of the advantages as a high-performance prediction tool in molecular simulations, deep learning is becoming far more than just a temporary trend. Instead, it is foreseen as a tool that will be essential to employ throughout tackling a range of different issues in chemical sciences in the nearest future. In this paper, we propose a novel methodology for regularization of deep neural networks used in chemo-informatics. The methodology consists of four blocks: Class of initial conditions;Orthogonalization, Activation and Standardization. Three graph-based architectures are developed: deep tensor neural network, directed acyclic graph and convolutional graph model. Graph-based models are more convenient for modeling molecules since the molecules and their features are often naturally represented by graphs. Several experiments are obtained on datasets from MoleculeNet aggregator: QM7, QM8, QM9, ToxCast, Tox21, ClinTox, BBBP and SIDER, for predicting geometric, energetic, electronic and thermodynamic properties on small molecules. The obtained results outperform some of the published references and give directions for further improvement. As a particular example, in one of the architectures, we have reduced mean absolute error by more than 12 times compared to conventional regression models, and more than 3 times in comparison to deep networks where the proposed methodology is not implemented. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Molecules

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Interactive Visual Schedule with Avatar Buddies for Activity Planning in Autistic Children

Interactive Visual Schedule with Avatar Buddies for Activity...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Hu, Yuqi Yao, Cheng Sun, Xu Liu, Bingjian Gao, Yuan Xue, Mengru Lyu, Hui Ningbo Innovation Center Zhejiang University China Faculty of Science and Engineering University of Nottingham Ningbo China China College of Computer Science and Technology Zhejiang University China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo China China Faculty of Humanities and Arts Macau University of Science and Technology China

ISBN: (纸本)9781643685069

Autism spectrum disorder can greatly affect the well-being of autistic children and their family members. Visual aids, such as schedules, have been commonly used in this community for their benefits in multiple aspects. Utilizing digital technology, we presented the design of an interactive visual schedule system with avatar buddies for activity planning in autistic children. 12 pairs of participants were recruited, USE questionnaire and interviews were used for validation. The user study showed that the digital interactive system presented advantages in the perspective of usefulness, particularly in enhancing children's control over activity planning and assisting in activity completion, compared with traditional visual schedules. It demonstrated the feasibility of integrating digital technology into the field of health and highlighted the matching between the flexibility of digital technology and the personalized needs of special groups. © 2024 The Authors.

关键词： autistic children avatars buddy systems digital interactions visual aids

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EHLM: Empirical Design of Novel Road Curve and Lane Identification Scheme using Effective Hybrid Learning Methodology

EHLM: Empirical Design of Novel Road Curve and Lane Identifi...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Mercy, S. Sudha Ramya, G. Sankar, R. Saravanan, L. Krishnamoorthi, K. R.M.K. Engineering College Department of Computer Science and Design Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur Chennai India S.A. Engineering College Department of MCA Chennai India PERI Institute of Technology Department of ECE Chennai India Sona College of Technology Department of EEE Salem India

ISBN: (纸本)9798350319200

In this study, we present a novel Road Curve and Lane Identification Scheme that harnesses the power of an Effective Hybrid Learning Methodology (EHLM). This advanced approach combines Convolutional Neural Networks (CNN), Mask R-CNN, and ResNet, creating a formidable framework for road curve detection and lane identification in complex driving scenarios. The EHLM framework offers a versatile solution that excels in detecting road curves and accurately identifying lanes, crucial components of autonomous driving systems and advanced driver assistance. It leverages the strengths of each architecture, from CNN's feature extraction capabilities to Mask R-CNN's precise instance segmentation and ResNet's deep learning prowess. This study provides a comprehensive overview of the EHLM approach, showcasing its efficacy in real-world road scenarios. Through extensive experimentation and evaluation, we demonstrate the superiority of our methodology, achieving precise road curve detection and lane identification. Our research contributes to the development of safer and more efficient autonomous vehicles, ultimately enhancing road safety and transportation *** our evaluation, we have considered several models, including CNN, DCNN, MRCNN, CNN-LSTM, ANN, and our Proposed Model. Among these contenders, the Proposed Model stands out prominently in terms of accuracy, achieving an impressive 97.23%. This indicates its remarkable ability to correctly classify and recognize the target elements within the dataset. © 2023 IEEE.

关键词： Artificial Neural Network (ANN) Convolutional Neural Network Effective Hybrid Learning Methodology (EHLM) Lane Identification Scheme

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Mechanical System Vulnerabilities: Advanced Fusion Machine Learning Model for Detecting Ethereum Attack Patterns in IIoT-5.0 Environments 1

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7th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Sulakhe, Vishal N. Goyal, S.B. Gadekar, Amit Wasnik, Manisha Agarkar, Pankaj Rajawat, Anand Singh Mechanical Engineering Department Sandip University Nashik422213 India Faculty of Information Technology City University Petaling Jaya46100 Malaysia Sandip Institute of Technology and Research Centre Nashik India Ajeenkya D Y Patil School of Engineering Charoli Lohegaon Maharashtra Pune412105 India School of Computer Science and Engineering Sandip University Nashik422213 India

ISBN: (数字)9783031733185

ISBN: (纸本)9783031733178

It is of the utmost significance to keep the reliability and confidentiality of these systems in check in light of the coming advent of Industry 5.0 as well as the growing penetration of the Industrial Internet of Things (IIoT) into complicated mechanical systems. The broad adoption of blockchain technology, and in particular Ethereum, brings with it a new set of opportunities as well as hazards that have an impact on the complicated dynamic that exists between machines, data, and decentralized applications. This research investigates an advanced fusion machine learning model with the intention of locating dangerous patterns and likely attack vectors that target Ethereum transactions and contracts within IIoT-5.0 systems. The results of this investigation are intended to be used in future research. The detection of these patterns and vectors is the primary focus of the model. In an era in which there is a transition taking place in the industrial sector, this research presents a novel technique for improving cybersecurity. Examining the specific flaws that may exist in mechanical systems after they have been integrated with blockchain solutions is the strategy that will be utilized. Our model shows tremendous promise in recognizing abnormalities and potential dangers by utilizing a combination of supervised learning techniques and unsupervised learning approaches. These techniques allow for the model to develop without being directly monitored by a human. This paves the way for preventative safety precautions to be included in the design of the future generation of industrial systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ethereum

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Efficient Design of a Single-Balanced Diode Mixer for 5G Communication Systems 5

Efficient Design of a Single-Balanced Diode Mixer for 5G Com...

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5th International Conference on Computing, Electronics and Communications engineering. iCCECE 2022

作者： Sellidj, Abdelmounim Gaoua, Said Yagoub, Mustapha C.E. University of Science and Technology H. Boumedienne Faculty of Electronics and Computer Science Algiers Algeria University of Ottawa School of Electrical Engineering and Computer Science Ottawa Canada

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

This work discusses the design of a broadband single-balanced down converter mixer, operating in the 5G NR FR2 bands of 24.25-29.5 GHz. With an IF frequency of 250 MHz, the mixer presents excellent port-To-port isolation over almost all the targeted band, i.e., 94.6 dB, 72.6 dB, and 25 dB for LO-IF, RF-IF, and RF-LO ports, respectively. It also exhibits low conversion loss (7.49 dB) and noise figure (with a maximum of 6.38 dB and 6.29 dB for double-and single-sideband, respectively). Furthermore, it displays a good linearity with a 1-dB compression point of 10.5 dB as well as input/output third order intermodulation products (IIP3/OIP3) of 18.68 dBm and 11.03 dBm, respectively. Such performance made the proposed design a suitable candidate for 5G applications. © 2022 IEEE.

关键词： Mixers (machinery)

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Multi-objective de novo Drug Design using Bidirectional Recurrent Neural Networks and Nondominated Sorting 2

Multi-objective de novo Drug Design using Bidirectional Recu...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Dineshkumar, R. Sunil Kumar, V. Reddy, R. Archana Shnain, Ammar Hameed Sachinkumar Saveetha University Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Department Of Ece Chennai India Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Navodaya Institute Of Technology Department Of Cse Raichur India

ISBN: (纸本)9798350381665

Drug Design (DD) is a significant field for pharmaceutical businesses study. Deep Learning (DL) approaches are evolving as an auspicious solution to employ de novo drug design, such as to develop drug-like molecules personalized to particular requirements. In DD, the design as well as production of safe and effective compounds is a long-term, complex, as well as difficult process. Hence, this research proposes the Bidirectional Recurrent Neural Network (BiRNN) for the design of multi-objective de novo drug. Initially, the CheMBL and KIBA datasets are utilized to estimate the effectiveness of the proposed method. Then, the data are proprocessed by utilizing Fragment similarity calculations to design various ligands sets and particular target collections. The proposed BiRNN attains the accuracy of 0.9742, and 0.0632 on CheMBL dataset, as well as Mean Square Error (MSE) of 0.213 and 0.024 on KIBA dataset respectively. These obtained results are better and effective when compared to the existing methods like Transformer-Decoder Generator, A2C Reinforcement Learning (RL) and Recurrent Neural Network (RNN). © 2024 IEEE.

关键词： Mean square error

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Entropy Based Feature Fusion Using Deep Learning for Waste Object Detection and Classification Model

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computer Systems science & engineering 2023年第12期47卷 2953-2969页

作者： Ehab Bahaudien Ashary Sahar Jambi Rehab B.Ashari Mahmoud Ragab Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser CityCairo11884Egypt

Object Detection is the task of localization and classification of objects in a video or *** recent times,because of its widespread applications,it has obtained more *** the modern world,waste pollution is one significant environmental *** prominence of recycling is known very well for both ecological and economic reasons,and the industry needs higher *** object detection utilizing deep learning(DL)involves training a machine-learning method to classify and detect various types of waste in videos or *** technology is utilized for several purposes recycling and sorting waste,enhancing waste management and reducing environmental *** studies of automatic waste detection are difficult to compare because of the need for benchmarks and broadly accepted standards concerning the employed data ***,this study designs an Entropy-based Feature Fusion using Deep Learning forWasteObject Detection and Classification(EFFDL-WODC)*** presented EFFDL-WODC system inherits the concepts of feature fusion and DL techniques for the effectual recognition and classification of various kinds of waste *** the presented EFFDL-WODC system,two major procedures can be contained,such as waste object detection and waste object *** object detection,the EFFDL-WODC technique uses a YOLOv7 object detector with a fusionbased backbone *** addition,entropy feature fusion-based models such as VGG-16,SqueezeNet,and NASNetmodels are ***,the EFFDL-WODC technique uses a graph convolutional network(GCN)model performed for the classification of detected waste *** performance validation of the EFFDL-WODC approach was validated on the benchmark *** comprehensive comparative results demonstrated the improved performance of the EFFDL-WODC technique over recent approaches.

关键词： Object detection object classification waste management deep learning feature fusion

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IoT Security Frameworks: A Comparative Review with a Focus on Privacy 2

IoT Security Frameworks: A Comparative Review with a Focus o...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Hasnawi, Abduljaleel Niu, Yitong Tawfeq, Jamal F. Ranjan Pradhan, Manas Salahat, Mohammed Ghazal, Taher M. Ministry Of Higher Education And Scientific Research Baghdad Iraq College Of Information Technology Imam Ja'afar Al-Sadiq University Department Of Communication Technology Engineering Baghdad Iraq School Of Aeronautical Engineering AnYang University Anyang China Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions. © 2024 IEEE.

关键词： Internet of things

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Performing Aerobatic Maneuver with Imitation Learning 23rd

Performing Aerobatic Maneuver with Imitation Learning

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23rd International Conference on Computational science, ICCS 2023

作者： Freitas, Henrique Camacho, Rui Silva, Daniel Castro Department of Informatics Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias s/n. Porto4200-465 Portugal LIAAD/INESC TEC - Artificial Intelligence and Decision Support Laboratory/Institute for Systems and Computer Engineering Technology and Science Porto Portugal LIACC - Artificial Intelligence and Computer Science Laboratory Porto Portugal

ISBN: (纸本)9783031359941

The work reported in this article addresses the challenge of building models for non-trivial aerobatic aircraft maneuvers in an automated fashion. It is built using a Behavioural Cloning approach where human pilots provide a set of example maneuvers used by a Machine Learning algorithm to induce a control model for each maneuver. The best examples for each maneuver were selected using a set of objective evaluation metrics. Using those example sets, robust models were induced that could replicate (and in some cases outperform) the human pilots that provided the examples (the clean-up effect). Complete complex maneuvers were performed using a meta-controller capable of sequencing the basic ones learned by imitation. This endeavor was rewarded by the results that show several Machine Learning models capable of performing highly complex aircraft maneuvers. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloning

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Brain Drug Effect Detection on Impulsivity with Ensemble Bagged Trees Method 7

Brain Drug Effect Detection on Impulsivity with Ensemble Bag...

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7th IEEE International Conference of computer science and Information technology, ICOSNIKOM 2023

作者： Rahajoe, Ani Dijah Wibowo, Alvito Dwinovan Agussalim Aisyah, Siti Sitanggang, Delima Turnip, Arjon Universitas Pembangunan Nasional Department of Computer Science Indonesia Universitas Padjadjaran Depertment of Electrical Engineering Sumedang Indonesia Universitas Pembangunan Nasional Department of Computer Science Indonesia Universitas Prima Indonesia Faculty of Sciences and Technology Medan Indonesia Universitas Padjadjaran Department of Electrical Engineering Sumedang Indonesia

ISBN: (纸本)9798350360752

The rise of drug trafficking and abuse is something that must be addressed immediately. The impact caused is quite damaging because it can interfere with brain performance, and cause addiction to death. Methods for drug detection using medical methods have been widely practiced, but using non-medical technology is still quite rare. In this study, drug detection is performed using brain waves obtained from electroencephalography signal with the proposed Ensemble Bagged Trees (EBT) classifier. Experimental trials were conducted on 40 subjects in one of the prisons in West Java. Extraction and classification results using EBT with 93.8% accuracy were obtained. These results indicate the potential of the proposed technology to be used for drug detection. © 2023 IEEE.

关键词： Electroencephalography

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