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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raman, Ramakrishnan Meenakshi, R. Latha, R. Rawandale, Chandrashekhar J. Jayaprakash, S. Srinivasan, C. Maharashtra Pune India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The system automated robotic for waste sorting and recycling by identifying and categorizing various forms of waste. Traditional sorting of waste takes time and is prone to human mistakes. This solution uses IoT and machine learning to overcome these restrictions and improve waste management efficiency. The system begins with waste-collecting area sensors and cameras. The sensors record waste item weight, size, and color, while the cameras record images. The Raspberry Pi, a small image-processing single-board computer, processes sensor and camera data. Raspberry Pi transfers processed data to the cloud for analysis and decision-making. The Support vector machine (SVM) method classifies waste in the cloud using machine learning. A dataset of waste categories trains the SVM algorithm to discriminate recyclable from non-recyclable materials. The system controls robotic arms or conveyor belts to segregate waste after classification. The robotic arms effectively classify garbage into containers. Real-time video management ensures fast, precise waste sorting, enhancing recycling efficiency. The system can gather data from many garbage-collecting regions concurrently. The cloud architecture enables real-time data processing and analysis. SVM-based machine learning techniques improve waste categorization accuracy, improving trash sorting. Waste sorting and recycling using the real-time video management system is novel. IoT, machine learning, and cloud computing facilitate robotic waste segregation. The method might improve waste management and environmental conservation. Optimizing waste management operations, reducing manual labor, and promoting sustainable methods. © 2023 IEEE.

关键词： Cloud Internet of Things Recycling Sensors SVM Algorithm Waste Sorting

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AMBO:All Members-Based Optimizer for Solving Optimization Problems

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computers, Materials & Continua 2022年第2期70卷 2905-2921页

作者： Fatemeh Ahmadi Zeidabadi Sajjad Amiri Doumari Mohammad Dehghani Zeinab Montazeri Pavel Trojovsky Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Electrical and Electronics Engineering Shiraz University of TechnologyShirazIran Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

There are many optimization problems in different branches of science that should be solved using an appropriate *** optimization algorithms are one of the most efficient approaches to solve this type of *** this paper,a new optimization algorithm called All Members-Based Optimizer(AMBO)is introduced to solve various optimization *** main idea in designing the proposedAMBOalgorithm is to use more information from the population members of the algorithm instead of just a few specific members(such as best member and worst member)to update the population ***,in AMBO,any member of the population can play a role in updating the population *** theory of AMBO is described and then mathematically modeled for implementation on optimization *** performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions,which belong to three different categories:unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO,eight other well-known algorithms have been also *** optimization results demonstrate the ability of AMBO to solve various optimization ***,comparison and analysis of the results show that AMBO is superior andmore competitive than the other mentioned algorithms in providing suitable solution.

关键词： Algorithm all members optimization optimization algorithm optimization problem population-based algorithm

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Adversarial learning for Mirai botnet detection based on long short-term memory and XGBoost

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 153-160页

作者： Vajrobol, Vajratiya Gupta, Brij B. Gaurav, Akshat Chuang, Huan-Ming Institute of Informatics and Communication University of Delhi India International Center for AI and Cyber Security Research and Innovations. Asia University Taiwan Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Ronin Institute MontclairNJ United States Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

In today's world, where digital threats are on the rise, one particularly concerning threat is the Mirai botnet. This malware is designed to infect and command a collection of Internet of Things (IoT) devices. The use of Mirai attacks has intensified in recent times, thus threatening the smooth operation of numerous devices that are connected to a network. Such attacks carry adverse consequences that include interference with services or the leakage of confidential information. To fight this growing threat, smart and flexible detection techniques are required to counter the new methods cyber attackers use. The aim of this research is to develop a resilient defense against Mirai botnet attacks. The Long Short Term Memory term (LSTM) and XGBoost combined have the best performance of 97.7% accuracy score. With this combination, the aim is to strengthen our cyber defenses against sophisticated and dynamically operating Mirai botnets to further enhance the security of our digital world. © 2024

关键词： Botnet

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Design of a Device to Ease and Improve the Diagnosis of Popliteal Artery Entrapment Syndrome

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Journal of engineering and science in Medical Diagnostics and Therapy 2024年第4期7卷 041006页

作者： Rutherford, Emma Xu, Wenhao Vu, Kiet Lozano, Claudia Lee, Alexandra Dzenutis, Paige Dowlatshahi, Arriyan S. Pennes, Anthony Connor, Jay Slocum, Alexander Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering & Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Harvard Medical School BostonMA02215 United States Orthopedic Surgery Mount Auburn Hospital CambridgeMA02139 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02139-4301 United States

Popliteal artery entrapment syndrome (PAES) is a condition affecting blood flow in the back of the leg, which can have a significant impact on the patient’s life. It is often misdiagnosed due to a lack of standardized procedures and quantifiable diagnostic criteria. To facilitate easier and more accurate diagnosis of this syndrome, we have designed and built an initial prototype of a device that provides adjustable resistance during plantar-flexion of the patient’s ankle while quantifying force generated and ankle range of motion. The device will be used while the physician observes blood flow through the popliteal artery in the back of the knee using an ultrasound. The device provides a convenient and ergonomic solution for patients and physicians, which helps improve the accuracy of PAES diagnosis and allows physicians to further study this condition. Copyright © 2024 by ASME.

关键词： Diagnosis

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A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis:Challenges and Motivations

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computers, Materials & Continua 2023年第5期75卷 2833-2858页

作者： Norah M.Alshahrani M.L.Mat Kiah B.B.Zaidan A.H.Alamoodi Abdu Saif Department of Computer System&Technology Faculty of Computer Science&ITUniversity of MalayaLembah Pantai50603Kuala LumpurMalaysia Department of Computer Science Applied CollageKing Khalid UniversityP.O.Box 62569AbhaSaudi Arabia Future Technology Research Center National Yunlin University of Science and Technology123 University RoadSection 3DouliouYunlin64002Taiwan Department of Computing Faculty of ArtsComputing and Creative Industry(FSKIK)Universiti Pendidikan Sultan IdrisTanjung MalimMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity of MalayaKuala Lumpur50603Malaysia

A smart contract is a digital program of transaction protocol(rules of contract)based on the consensus architecture of *** contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications.A smart contract is the central aspect of a blockchain that facilitates blockchain as a platformoutside the cryptocurrency *** development of blockchain technology,with a focus on smart contracts,has advanced significantly in recent ***,research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical *** paper extensively reviews smart contracts based on multi-criteria analysis,challenges and ***,implementing blockchain in multicriteria research is required to increase the efficiency of interaction between users via supporting information exchange with high *** blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence,detecting malfunctioning,helping users with performance issues,reaching a consensus,deploying distributed solutions and allocating plans,tasks and joint *** smart contract with decision-making performance,planning and execution improves the implementation based on efficiency,sustainability and ***,the uncertainty and supply chain performance lead to improved users’confidence in offering new solutions in exchange for problems in smart *** includes code analysis and performance,while development performance can be under development.

关键词： Blockchain smart contract MCDM MADM

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An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

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Enhancing Sovereign Allocation of Resources in Cloud Milieus Using a Causal Dilated Geometric Algebra Approach for Dynamic Scalability 2

Enhancing Sovereign Allocation of Resources in Cloud Milieus...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Katru, Chandrasekhar Rao Srinivasan, Sathish Kumar, Mohan Yadwad, Sunita Satish, G. Maranan, Ramya Lead Software Engineer Indian LandSC29707 United States Machine Learning & AI Development Oracle Cloud Infrastructure Division San FranciscoCA United States Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru India Parul institute of Engineering and Technology Parul University Computer science and Engineering Faculty of Engineering and Technology Gujarat India Sree Dattha Institute of Engineering and Science Department of Electrical and Electronics Engineering Telangana Sheriguda India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Cloud environments utilize availability and usage of resources as crucial components in order to produce scalability, reliability and low cost. Such resource capabilities require autonomous systems that can handle these resources dynamically in spite of fluctuating workloads and service delivery requirements. Static models of resource management suffer from scalability and inability to react on the fly and accurately predict the demand thus causing Service Level Agreement (SLA) breaches and affecting the Quality of Service (QoS) as well as energy consumption profile. Advanced and elaborative cloud workloads require an intelligent system that can analyse the temporal and spatial patterns without impacting the overall system's performance significantly. Inspired by these challenges, this research proposes a Causal Dilated Geometric Algebra Convolutional Transformer Network (CATNet) along with the Prairie-Dog Optimizer (P-DogO), referred to as CAT-DogNet to learn and predict the dynamic scalability. CATNet integrates Causal Dilated Convolutional Networks in time domain for temporal structure capturing and Geometric Algebra Transformer for spatial resource coding. P-DogO adjusts weight parameters and losses for resource allocation, enhancing them depending on a work environment. A set of comprehensive assessments demonstrate a 0.85% SLA violation rate, average response time of less than 10ms, and overall 12.3% less energy consumption than baseline methods. The system had favourable predictability rates over 99.4% for the resource demand estimate. CAT-DogN et presents a sound and agnostic architecture for autonomous resource allocation in cloud environment, which indeed extend the state of art by considering flexibility, power consumption and QoS compliance. © 2025 IEEE.

关键词： Infrastructure as a service (IaaS)

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Implementation and Evaluation of a Deep Neural Network for Spam Detection: An Empirical Study of Accuracy and Efficiency

Implementation and Evaluation of a Deep Neural Network fo...

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International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies, IAT 2023

作者： Varga, Luka Livada, Časlav Baumgartner, Alfonzo Šojo, Robert Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University in Osijek Kneza Trpimira 2B Osijek31000 Croatia

ISBN: (纸本)9783031430558

The problem of spam emails is a widespread issue that creates a lot of inconvenience for individuals and organizations. According to statistics, approximately 84% of emails received on a daily basis are recognized as spam. This paper aims to present a solution to this problem by proposing the use of a neural network capable of identifying and classifying potential spam emails. The neural network was developed using Python, TensorFlow, Keras, Google Colaboratory, and Jupyter. These tools were chosen because they are widely used and well-suited for the task of creating a deep learning model. The results of the network were found to be satisfactory, with an accuracy rate of approximately 99%. This is comparable to the results achieved by large companies such as Google and Yahoo! who are known to use similar methods to combat spam. Overall, this paper demonstrates that neural networks can be a powerful tool for addressing the problem of spam emails and that the proposed solution has the potential to improve the efficiency and effectiveness of spam filtering for individuals and organizations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Electronic mail

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A secure IoT limited Devices by applying Multi-Round Lightweight Cryptography 25

A secure IoT limited Devices by applying Multi-Round Lightwe...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al-Mousa, Mohammad Rasmi Abdulqawi, Mohammad Badarneh, Hazem Jihad Al-Shaikh, Ala'a Asassfeh, Mahmoud Al-Sherideh, Ala'a Saeb Samara, Ghassan Elrashidi, Ali Alazaidah, Raed Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of Information Technology Zarqa Jordan

ISBN: (纸本)9798331540012

The Internet of Things (IoT) is considered the base factor, that helps in developing many sectors. Additionally, IoT provides novel applications and services, i.e. smart homes, connected cars, and industrial automation. However, this development aggravates the security and privacy issues. This is because the connected devices may be vulnerable to cyber attacks in addition to generated data. Accordingly, it is of utmost importance to deploy appropriate security measures to alleviate these attacks. To overcome the mentioned problem, this work employs lightweight cryptography on limited processing capabilities of IoT devices i.e. limited memory, computing power, and battery life. These limited capabilities obstruct employing cryptography methods that require nontrivial computational resources. This work presents a multiround lightweight cryptography approach for IoT devices, which defines the security and protection of generated and transferred data. Multi-round cryptography mitigates the required device resources while ensuring robust protection and maintaining the stability of IoT devices. © 2024 IEEE.

关键词： Cyber attacks

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Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9217-9232页

作者： Faragallah, Osama S. El-Hoseny, Heba M. El-Sayed, Hala S. Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Computer Science The Higher Future Institute for Specialized Technological Studies El Shorouk Egypt Department of Electrical Engineering Faculty of Engineering Menoufia University Shebin El-Kom32511 Egypt

In computer vision segmentation field, super pixel identity has become an important index in the recently segmentation algorithms especially in medical images. Simple Linear Iterative Clustering (SLIC) algorithm is one of the most popular super pixel methods as it has a great robustness, less sensitive to the image type and benefit to the boundary recall in different kinds of image processing. Recently, COVID-19 severity increased with the lack of an effective treatment or vaccine. As the Corona virus spreads in an unknown manner, th-ere is a strong need for segmenting the lungs infected regions for fast tracking and early detection, no matter how small. This may consider difficult to be achieved with traditional segmentation techniques. From this perspective, this paper presents an efficient modified central force optimization (MCFO)-based SLIC segmentation algorithm to discuss chest CT images for detecting the positive COVID-19 cases. The proposed MCFO-based SLIC segmentation algorithm performance is evaluated and compared with the thresholding segmentation algorithm using different evaluation metrics such as accuracy, boundary recall, F-measure, similarity index, MCC, Dice, and Jaccard. The outcomes demonstrated that the proposed MCFO-based SLIC segmentation algorithm has achieved better detection for the small infected regions in CT lung scans than the thresholding segmentation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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