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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Ismail, Leila Materwala, Huned Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios. © 2024 Elsevier B.V.. All rights reserved.

关键词： Internet of things

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Optimized Crop Detection Using IoT and Deep Neural Networks 5

Optimized Crop Detection Using IoT and Deep Neural Networks

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Choudhury, Tanupriya Rohini, A. Mahdi, Hussain Falih Uttarakhnad Dehradun248007 India Graphic Era Hill University Dehradun248002 India Dept. of Computer Science & Engg Visakhapatnam531162 India University of Diyala Department of Computer and Software Engineering Baquba Iraq

ISBN: (纸本)9798350337525

The advancement of agriculture in food production is an increasing concern. It is one of the major challenges in contemporary farming. The development methods of soil farming have increased the fabrication of crops and food. It emphasizes the vital role of sustainability in soil. Different technologies were used to grow and flourish in soil to supply the nutrition's. It buffers and delicate the plant roots from fluctuations in temperature. Hydroponically grown fruits can produce rich nutrition's like traditional grown fruits. Proposed research has focused on the IoT in Hydroponics for Growing Tomato Plants. The Nutrient Film Technique (NFT) is needed for the present farmers for monitor and managing the crop system. For managing and monitoring the crop to execute the method scientifically and take the decision about the crops in time. The integration of IoT has given the solutions to solve the contemporary problems of calcium deficiency, lack of airflow around plants and so on. This proposal has developed a smart hydroponic system with technology enabled by IoT system. © 2023 IEEE.

关键词： Crops

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Design and Implementation of Intelligent Dustbin with Garbage Gas Detection for Hygienic Environment based on IoT

Design and Implementation of Intelligent Dustbin with Garbag...

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2022 International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2022

作者： Ahmed, Marzia Shaha, Rony Sarker, Kaushik Mahi, Rifat Bin Kashem, Mohammod Abul Daffodil International University Department of Software Engineering Dhaka Bangladesh Universiti Malaysia Pahang Faculty of Electrical and Electronics Engineering Pahang 26600 Malaysia Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Duet Department of Computer Science and Engineering Gazipur Bangladesh

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Rapid population expansion necessitated increased resource use in everyday living. As a result, the pace of trash gen-eration has increased dramatically, affecting the environment's hygiene system and other health concerns. Waste overflows in public spaces, and improved management is necessary. The purpose of this study is to develop a model of an intelligent trashcan for usage in smart cities. Additionally, to identify dangerous gases emitted by dustbins for subsequent management operations, as well as to monitor the amount of trash in the waste bin and warn the municipality through SMS. This system includes two ultrasonic sonar sensors for measuring trash level, a GSM module for sending SMS, three gas sensors for detecting harmful garbage gas, an infrared sensor for counting garbage droplets, and an Arduino Uno for managing all activities. The system notifies you whether the bin is full or empty and can also be controlled by voice command. Additionally, released gas may be monitored to determine the severity of the impairment and to notify the appropriate authorities. Most significantly, it will identify a failed trash drop in the bin and alert the user through alarm for truly considering the reduction of spilled garbage surrounding bins while using the system. © 2022 IEEE.

关键词： Global system for mobile communications

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Ethics of AI: A Systematic Literature Review of Principles and Challenges 22

Ethics of AI: A Systematic Literature Review of Principles a...

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26th ACM International Conference on Evaluation and Assessment in software engineering, EASE 2022

作者： Khan, Arif Ali Badshah, Sher Liang, Peng Waseem, Muhammad Khan, Bilal Ahmad, Aakash Fahmideh, Mahdi Niazi, Mahmood Akbar, Muhammad Azeem M3S Empirical Software Engineering Research Unit University of Oulu Finland Faculty of Computer Science Dalhousie University Canada School of Computer Science Wuhan University China Department of Computer Science University of Loralai Pakistan College of Computer Science and Engineering University of Hail Saudi Arabia School of Business University of Southern Queensland Australia Department of Information and Computer Science King Fahd University of Petroleum and Minerals Finland Software Engineering Department Lappeenranta-Lahti University of Technology Finland

ISBN: (纸本)9781450396134

Ethics in AI becomes a global topic of interest for both policymakers and academic researchers. In the last few years, various research organizations, lawyers, think tankers, and regulatory bodies get involved in developing AI ethics guidelines and principles. However, there is still debate about the implications of these principles. We conducted a systematic literature review (SLR) study to investigate the agreement on the significance of AI principles and identify the challenging factors that could negatively impact the adoption of AI ethics principles. The results reveal that the global convergence set consists of 22 ethical principles and 15 challenges. Transparency, privacy, accountability and fairness are identified as the most common AI ethics principles. Similarly, lack of ethical knowledge and vague principles are reported as the significant challenges for considering ethics in AI. The findings of this study are the preliminary inputs for proposing a maturity model that assesses the ethical capabilities of AI systems and provides best practices for further improvements. © 2022 ACM.

关键词： Challenges AI Ethics Machine Ethics Systematic Literature Review Principles

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Real Estate Management via a Decentralized Blockchain Platform

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computers, Materials & Continua 2021年第2期66卷 1813-1822页

作者： Iftikhar Ahmad Mohammed AAlqarni Abdulwahab Ali Almazroi Laiba Alam Department of Computer Science and Information Technology University of Engineering and TechnologyPeshawarPakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia

Blockchain technology is one of the key technological breakthroughs of the last *** has the ability to revolutionize numerous aspects of society,including financial systems,healthcare,e-government and many *** such area that is able to reap the benefits of blockchain technology is the real estate *** many other industries,real estate faces major administrative problems such as high transaction fees,a lack of transparency,fraud and the effects of a middleman including undue influence and *** enables supporting technologies to overcome the obstacles inherent within the real estate investment *** technologies include smart contracts,immutable record management and time-stamped *** utilize these key properties of blockchain technology in our work by proposing a system that has the ability to record real estate transactions in a private blockchain,using smart *** immutability of the blockchain ledger and transactions can provide a safe space for the real estate *** technology can also assist the authentication process by hastening background *** digital keys are provided to parties that are involved in a contract,thus minimizing the risk of *** also discuss the rationale behind the advantages of using a blockchain in this manner,and how we selected the consensus mechanism for our proposed system.

关键词： Blockchain real estate smart contracts

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Detecting Deepfakes: Using CNN to Identify Manipulated Visual Media 8

Detecting Deepfakes: Using CNN to Identify Manipulated Visua...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sunanda, N. Shailaja, K. Babu, Jampani Satish Vamsi Krishna, K. Lakshmanpratap, N. Motupalli, Ravi Kanth and Ai&ds Hyderabad India Vasavi College of Engineering & Technology Department of Cse Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Vaddeswaram India Prowess Software Services Hyderabad India Mallareddy Engineering College Department of Ece Hyderabad India Vnr Vignana Jyothi Institute of Engineering & Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350376425

Deepfake detection is a rapidly evolving field with significant implications for the integrity of visual media. This review explores techniques, challenges, and future directions in deepfake detection. Traditional image forensics techniques, deep learning models, and multi-modal fusion approaches are employed to differentiate real content from deepfakes. Addressing the challenges of evolving deepfake algorithms and the need for real-time detection, future research should focus on novel architectures, training strategies, temporal and contextual information, and ethical considerations. By advancing deepfake detection methods, researchers can contribute to combating the proliferation of synthetic content and preserving the authenticity of visual media in the digital age. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault Detection On Heat Pump Operational Data Using Machine Learning Algorithms 11

Fault Detection On Heat Pump Operational Data Using Machine ...

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11th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2024

作者： Rahman, Md Mahbubur Malekian, Reza Akerstroem, Vilhelm Malmö University Department of Computer Science and Media Technology Malmö20506 Sweden Malmö University Internet of Things and People Research Centre Malmö20506 Sweden Engineering Software Robert Bosch GmbH Lund Sweden

ISBN: (纸本)9798350366501

Heat pumps, being complex systems, are susceptible to various malfunctions. By harnessing contemporary IoT technologies, these devices continuously transmit data which enables monitoring, maintenance, and efficiency. This study focuses on identifying compressor short duration cycles as faults through supervised machine learning algorithms such as XGBoost, Random Forest, SVM, and k-NN. Data preprocessing and labeling were conducted using extensive logged data from heat pump systems, addressing issues like high dimensionality, data sparsity, and temporal dependencies. The methodology included feature engineering, interpolation of missing data, and downsampling for compressor short duration cycles. Supervised machine learning models were applied to classify these short duration cycles. Among the models, XGBoost achieved the highest accuracy and F1-scores, effectively distinguishing between normal and fault conditions. The findings highlight the potential of machine learning to enhance predictive maintenance and operational efficiency in heat pumps. © 2024 IEEE.

关键词： Predictive maintenance

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An Approach for Auto Generation of Labeling Functions for software engineering Chatbots

arXiv

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arXiv 2024年

作者： Alor, Ebube Abdellatif, Ahmad Khatoonabadi, SayedHassan Shihab, Emad Lab The Department of Computer Science & Software Engineering Concordia University MontrealQC Canada The Department of Electrical & Software Engineering University of Calgary CalgaryAB Canada

software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are the Natural Language Understanding platforms (NLUs), which enable them to comprehend and respond to user queries. Before deploying NLUs, there is a need to train them with labeled data. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets. This challenge arises because training SE chatbots requires specialized vocabulary and phrases not found in typical language datasets. Consequently, chatbot developers often resort to manually annotating user queries to gather the data necessary for training effective chatbots, a process that is both time-consuming and resource-intensive. Previous studies propose approaches to support chatbot practitioners in annotating users’ posed queries. However, these approaches require human intervention to generate rules, called labeling functions (LFs), that identify and categorize user queries based on specific patterns in the data. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate the effectiveness of our approach by applying it to the queries of four diverse SE datasets (namely AskGit, MSA, Ask Ubuntu, and Stack Overflow) and measure the performance improvement gained from training the NLU on the queries labeled by the generated LFs. We find that the generated LFs effectively label data with AUC scores of up to 85.3%, and NLU’s performance improvement of up to 27.2% across the studied datasets. Furthermore, our results show that the number of LFs used to generate LFs affects the labeling performance. We believe that our approach can save time and resources in labeling users’ queries, allowing practitioners to focus on core chatbot functionalities rather than on manually labeling queries. Copyright © 2024, The Authors. All rights reserved.

关键词： software engineering

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The classification of medical and botanical data through majority voting using artificial neural network

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International Journal of Information Technology (Singapore) 2023年第6期15卷 3271-3283页

作者： Tripathi, Kshitij Khan, Fayaz Ahmed Khanday, Akib Mohi Ud Din Nisa, Khair U. L. Department of Computer Applications The Maharaja SayajiRao University of Baroda Vadodara 390002 India Department of Information Technology S.P. College Cluster University Srinagar 190001 India Department of Computer Science and Software Engineering-CIT United Arab Emirates University Al Ain United Arab Emirates

Data classification has many approaches in data mining and machine learning. The artificial neural network (ANN) is applied to classify the data that might belong to various domains like chemical, botanical, medical, spatial, textual, and image. In this work, an ANN technique is applied to the 7 Life sciences (botanical and medical) data sets extracted from public data repositories. Various optimization approaches like exhaustive validation, cross-validation, and multiple seeding are used to discover the most optimized networks for the given datasets. Finally, voting predicts the class where the whole dataset is used as a test set instead of folds. The results obtained by the proposed approach outperform other approaches on all the datasets. Cleveland’s heart, Statlog heart, Dermatology, Hepatitis, Seeds, Abalone and Vertebral Column data sets (all of UCI) after applying the voting showed the accuracy of 94.61%, 93.7%, 99.73%, 96.77%, 99.05%, 89.37% and 90.32% respectively. In the future deep neural network may be used to improve the results. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial neural network Classification Optimization Validation

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Games for Scrum Team Collaboration in the Global software Development Environment: A Literature Review 7

Games for Scrum Team Collaboration in the Global Software De...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

software development companies commonly use Global software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： software design

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