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Smart Energy Management System Using Non-intrusive Load Monitoring

SN Computer science 2022年第2期3卷 117页

作者： Deshpande, Riya Hire, Shubhankar Mohammed, Zakee Ahmed Electronics and Telecommunication Pune Institute of Computer Technology Maharashtra Pune India Artificial Intelligence and Data Science Chhatrapati Shahu College of Engineering Maharashtra Aurangabad India

Energy Management is a problem faced by many around the world. The ever-rising demand for energy is putting a strain on the worldwide resources. Additionally, during the pandemic, it was observed that there was a lot of discrepancy in the electricity bills. To do our part in addressing the issue, the combination of Internet of Things (IoT) and Machine Learning (ML) has been used in creating a solution which will help measure, monitor and visualize daily energy consumption of a household. Additionally, using the concept of Non-Intrusive Load Monitoring (NILM), a single hardware setup can be used to measure the energy consumption of each appliance in the household. This hardware setup with the use of certain ML algorithms like Factorial Hidden Markov Model (FHMM) and Combinatorial Optimization (CO) disaggregates the combined household energy readings to device specific values. These values then get sent to a cloud database and are presented to the user through a Dashboard like visual interface. Therefore, the system in whole offers a combined solution to the user with minimal setup and cost to give a generic idea based on the energy usage, pattern, consumption. Such monitoring and systems can help efficient and responsible energy usage and can go a long way in ensuring sustainability. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Energy management Internet of things Machine learning Non-intrusive load monitoring

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Enhancing Residential Security with AI-Powered Intrusion Detection Systems

Enhancing Residential Security with AI-Powered Intrusion Det...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Jeneetha Jebanazer, J. Prabha, B.Vishnu Yasotha, B. Jaisudha, J. Senthilkumar, C. Pandi, V.Samuthira Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Jeppiaar Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (纸本)9798350313987

Using Intrusion Detection Systems (IDS) powered by artificial intelligence is presented in the proposed work as a novel method for enhancing residential security. The overarching goal of the study is to design, develop, and evaluate a system that employs artificial intelligence techniques for real-time detection and prevention of unauthorized access in response to the rising demand for such measures. Using anomaly detection, neural networks, and decision trees, which are all examples of machine learning algorithms that benefit from the incorporation of data from multiple sensors, the proposed system guarantees the accurate identification of suspicious activities. Proposed work examines large datasets and compares them to conventional security measures to demonstrate the system's superior performance and prospective impact on reducing home intrusions. Proposed work contributes to the field of residential security by proposing a dependable, adaptable, and intelligent method for protecting homes against the ever-changing types of infiltration threats that exist today. © 2023 IEEE.

关键词： Anomaly Detection artificial intelligence Convolutional Neural Network Home Protection Intrusion Detection Sensor Integration

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A Multiresolution Method for Non-Contact Heart Rate Estimation Using Facial Video Frames 7

A Multiresolution Method for Non-Contact Heart Rate Estimati...

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7th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2022

作者： Das, Mousumi Choudhary, Tilendra Bhuyan, M.K. Sharma, L.N. Pallab Jyoti Dutta, H. Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Department of Electronics and Electrical Engineering Assam Guwahati India

ISBN: (纸本)9781665496483

In recent years, camera-based non-contact heart rate (HR) measurement technology has grown immensely. The system captures the reflection of light from the facial tissues and lead to the formation of a remote photoplethysmogram (rPPG) signal that can be used to measure physiological parameters for cardiac health assessment. Due to environmental interferences, extraction of a reliable rPPG signal is a challenging task and thus, requires a robust denoising algorithm. In this paper, a discrete wavelet transform (DWT)-based multiresolution method is used to remove the noises from the video frames caused due to illumination variation and motion artifacts. Subsequently, rPPG signal is extracted and HR is measured from two region of interests (ROIs), facial and forehead regions. The study evaluates the performance of the proposed method on each of the RGB color channels from both the ROIs. The performance results for the COHFACE dataset show that the proposed method works well for the estimation of HR values. Furthermore, they reveal that the forehead region on the green channel is more suitable for HR measurement. © 2022 IEEE.

关键词： Discrete wavelet transforms

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The Colony Predation Algorithm

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Journal of Bionic engineering 2021年第3期18卷 674-710页

作者： Jiaze Tu Huiling Chen Mingjing Wang Amir H.Gandomi College of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Faculty of Engineering and Information Technology University of Technology SydneyUltimoNSW 2007Australia

This paper proposes a new stochastic optimizer called the Colony Predation Algorithm(CPA)based on the corporate predation of animals in *** utilizes a mathematical mapping following the strategies used by animal hunting groups,such as dispersing prey,encircling prey,supporting the most likely successful hunter,and seeking another ***,the proposed CPA introduces new features of a unique mathematical model that uses a success rate to adjust the strategy and simulate hunting animals'selective abandonment *** paper also presents a new way to deal with cross-border situations,whereby the optimal position value of a cross-border situation replaces the cross-border value to improve the algorithm's exploitation *** proposed CPA was compared with state-of-the-art metaheuristics on a comprehensive set of benchmark functions for performance verification and on five classical engineering design problems to evaluate the algorithm's efficacy in optimizing engineering *** results show that the proposed algorithm exhibits competitive,superior performance in different search landscapes over the other ***,the source code of the CPA will be publicly available after publication.

关键词： Colony Predation Algorithm optimization nature-inspired computing meta-heuristic engineering problems

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Maximizing Crop Yields and Water Efficiency through Tailored Irrigation System Design and Crop Selection

Maximizing Crop Yields and Water Efficiency through Tailored...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Musale, Prajakta Jadhav, Balasaheb Bahir, Suvarna A Thopate, Kaushalya Anarase, Dipali Jaiswal, Krishna Vishwakarma Institute of Technology Department of Engineering Sciences and Humanities Pune India Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Trinity Academy of Engineering Department of Computer Engineering India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Wipro Technology India

ISBN: (纸本)9798350308167

Today various water-pump operating systems are available in the market, which allows the user/farmer to operate his water-pump from a distant location using mobile app or on a phone call to the system which is integrated with the water-pump. While studying the existing system we came across a problem that, existing systems does not provide any solution for optimizing the usage of water and power. Optimizing the use of power and water in farming has become a real-time problem these days. To overcome this problem, we have proposed an IOT based system "Advanced Irrigation System". This system provides automation for operating the water pump in the farm by automatically turning ON and OFF the water-pump depending on the type of the crop and its moisture requirements. Also, proposed system provides farmer interface via a mobile application in his regional language, where he can check the moisture, humidity and temperature of his farm. Along with this proposed system has a Google Assistant integrated, using which famer can operate the water-pump remotely over a voice command. © 2023 IEEE.

关键词： Advanced Irrigation System Crop Selection Maximizing Crop Yields power Optimization Water Efficiency water-pump operating systems

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A Hybrid Heuristic Algorithm for Solving COVID-19’s Social Distancing at Universities Campus

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Computer Systems science & engineering 2022年第6期41卷 933-944页

作者： Hassan Al-Tarawneh Khalid Al-Kaabneh Aysh Alhroob Hazem Migdady Issam Alhadid Department of Data sciences and Artificial Intelligence Al-Ahlyyia Amman UniversityAmmanJordan Department of Software Engineering Isra UniversityAmmanJordan Department of CSMIS Oman college of management and technologyMuscatOman Faculty of Information Technology and Systems University of JordanAqabaJordan

Social distancing during COVID-19 has become one of the most important measures in reducing the risks of the spread of the virus. Implementing thesemeasures at universities is crucial and directly related to the physical attendance ofthe populations of students, professors, employees, and other members on campus. This research proposes an automated scheduling approach that can help universities and schools comply with the social distancing regulations by providingassistance in avoiding huge assemblages of people. Furthermore, this paper proposes a novel course timetable-scheduling scheme based on four main ***, a distance of two meters must be maintained between each student inside theclassroom. Second, no classrooms should contain more than 20% of their regularcapacity. Third, there would be no back-to-back classes. Lastly, no lectures shouldbe held simultaneously in adjacent classrooms. The proposed approach wasimplemented using a variable neighborhood search (VNS) approach with an adaptive neighborhood structure (AD-NS) to resolve the problem of scheduling coursetimetables at Al-Ahlyyia Amman University. However, the experimental resultsshow that the proposed techniques outperformed the standard VNS tested on university course timetabling benchmark dataset ITC2007-Track3. Meanwhile, theapproach was tested using datasets collected from the faculty of information technology at Al-Ahlyyia Amman University (Jordan). Where the results showed that,the proposed technique could help educational institutes to resume their regularoperations while complying with the social distancing guidelines.

关键词： COVID-19 social distance variable neighborhood search adaptive neighborhood structure university course timetable

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Blockchain Oriented Hybrid Architecture for Crowdsource Model

Blockchain Oriented Hybrid Architecture for Crowdsource Mode...

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2023 International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Pachhaiammal Alias Priya, M. Rajulu, N. Sundara Jayanthi, S. Kavitha, G. Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Excel Engineering College Department of Information Technology Tamil Nadu Coimbatore India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350333602

Blockchain has been preserved as one of the strongest innovations to facilitate crowdsourcing as it offers new functionality, such as decentralization and transparency. Alas, certain intrinsic drawbacks of Blockchain, which become the efficiency bottleneck of crowdsourcing schemes, have hardly been tackled by the most current initiatives when implementing Blockchain to clustering. The followings are the few challenges faced by Blockchain: Scalability, the blockchain sector still face difficulties in managing a large number of users at once, Hackers and shadow dealing, Complex to understanding and adopting. In this article, to achieve decentralization and privacy protection, a modern hybrid blockchain crowd sourcing facility is proposed. With its hybrid Blockchain, doubleheaders, and double consensus algorithms, this network integrates stable connectivity between the requestors and the employees. In order to ensure the automated execution of the activities and the security of users' privacy, smart contracts and zero awareness evidence are both used. Finally, tests are performed to validate the effectiveness of the platform's consensus algorithm by matching it with the new one. The proposed hybrid framework proved that it is highly confidential and gives an extraordinary performance in regard to privacy. With the development of big data and medical information control systems, there is a growing interest in the sharing of electronic medical records between businesses for better medical care and advancement. On the other hand, the proliferation of large amounts of data, privacy issues, inherent business confidence issues, and complex legal requirements impede the quick development of healthcare intelligence. © 2023 IEEE.

关键词： Blockchain

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Cloud-Enhanced Transfer Learning Framework for Medical Image Analysis: Advancing Diagnostic Speed and Accuracy in Healthcare 3

Cloud-Enhanced Transfer Learning Framework for Medical Image...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Prabu, S. Chandra, N. Subhash Bhavani, M. Varma, V. K. Chaitanya Soni, Mukesh Mahendra Institute of Technology Department of Electronics and Communication Engineering Namakkal India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalle India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sagi Rama Krishnam Raju Engineering College Department of Mechanical Engineering Bhimavaram India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune India

ISBN: (纸本)9798331508456

When it comes to medical image analysis, problems arise due to the scarce amount of data and computational resources in medical environments. This is because, as earlier stated, cloud settings demand efficient models capable of delivering more reliable diagnostic results without straining the cloud's computing resources. In this work, we presented a transfer learning based on DenseNet-201 as well ResNet-152 architectures with a new large dataset containing 15,000 medical images of different modality. By utilizing the distributed cloud computing and hypermeters precision strategy, the proposed model was found to have 95.8 % of accuracy, 94.2% sensitivity, and 96.3%specificity. It also reduced the processing time by sixty-eight percent over standard methods across the facilities. Five different datasets validated the proposed approach, achieving an external accuracy of 92.4%, indicating improved generalization performance. The enhancement of accuracy to approximately 90% and the reduction in required computation demonstrate its feasibility for extensive clinical applications on the cloud, spanning across hospitals. © 2025 IEEE.

关键词： Distributed cloud

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A Review of Machine Learning and Deep Learning algorithms for Chronic Kidney Disease Prediction 2

A Review of Machine Learning and Deep Learning algorithms fo...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Nagre, Pratik Verma, Prateek Kumar, Praveen Gavankar, Nitin L. Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Maharashtra Wardha442001 India Walchand College of Engineering Department of Computer Science and Engineering Maharashtra Sangli416415 India

ISBN: (纸本)9798331528713

Chronic Kidney Disease (CKD) is a common disease worldwide that is manifested by slow progressive loss of kidney function that can result in complications and kidney failure. That is why conventional approaches to CKD diagnosis and treatment have certain difficulties: late identification of the disease, inaccuracy of the diagnosis, and high costs for the therapy. The new approaches for solving these problems are opened with the help of Machine Learning (ML) and deep learning (DL). These technologies can help existing information sets in large volumes in diagnosing, predicting, and in early detection of diseases to enhance the well-being of the patients. However, the outcome comes with a handful of constraints, including the quality of the data used in developing the models, the ability to interpret results from the generated model, and the complexity of the computation process. The following section describes the various ML and DL algorithms used for CKD, their strengths, weaknesses, and the possibility of implementing these technologies in clinical settings for better CKD outcomes. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Approach for Anomaly Detection using Snort Integrated with Machine Learning

A Novel Approach for Anomaly Detection using Snort Integrate...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Preethi Ponnuru Rakshitha Reddy Lekkala Likhitha Pendyala Pavan Kumar Abhinav Kamani Department of CSE – Cyber Security Data Science and Artificial Intelligence & Data Science VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

In today’s digital world, it is crucial to keep a company’s information safe from cyber threats. With new and more sophisticated network attacks emerging all the time, better security measures and ways to monitor our networks are needed. Intrusion Detection Systems (IDS) can act as digital guards to help protect our networks from these malicious threats. Snort is a widely used open-source IDS, but it can struggle with the ever-changing nature of these threats. Our research focuses on enhancing Snort’s capabilities by integrating it with ML algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Naive Bayes. We also conducted subset testing using the NSL-KDD dataset. Our results indicate that combining these ML algorithms with Snort can improve our ability to detect and stop malicious network activities. This research can be helpful for anyone interested in developing smarter and more effective intrusion detection systems.

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