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Application of Mobile Health Technology for Predicting attention-deficit/hyperactivity disorder (ADHD) for adolescent

Application of Mobile Health Technology for Predicting atten...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Zearah, Sajad Ali Alsalhy, Mohammed Jameel Taha, Ahmed Alatba, Sura Rahim Fouad, Laith Jawad, Israa Abed Scientific Research Center Al-ayen University Thi-Qar Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Medical Instruments Engineering Techniques Al-farahidi University Baghdad Iraq Computer Technologies Engineering Al-Turath University College Baghdad Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Computer Engineering Techniques Al-Nisour University College Baghdad Iraq

ISBN: (纸本)9798350303452

Many adults and children are affected by the distressing condition known as attention-deficit/hyperactivity disorder (ADHD). The widespread availability of mobile phones in today's society facilitates the development of novel approaches to various problems. In recent years, mHealth has emerged due to the widespread use of mobile devices in the medical field. Hence, this article proposes the Interactive mHealth Framework (ImHealthF) to aid adults and children with ADHD. There are four different types of people affected by the illness represented in the application: children, parents, doctors, and educators. Parents can recognize the signs of this disease in their children, and playing games of numerous types may help improve adolescents' brainpower. Using an example, this study highlights the importance of the initiative's security features for mobile health. To protect the privacy of its users and the integrity of their data during transmission, the developed tool makes extensive use of encryption. Support and accurate treatment for Attention Deficit Hyperactivity Disorder (ADHD) need to extend beyond the titration period and provide more to patients and their families than simple monitoring via consequence trials, and this is where mHealth comes in. © 2023 IEEE.

关键词： mHealth

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Human-computer Interaction Assisted Economic Growth and Financial Status of Urban Cities using Artificial Intelligence 1

Human-Computer Interaction Assisted Economic Growth and Fina...

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1st IEEE International Conference on Emerging Research in computational science, ICERCS 2023

作者： Dawood, Mustafa Nazar Brayyich, Mohammed Majed, Safa Zearah, Sajad Ali Ali, Aqeel Ali, Ahmed Al-Turath University College Computer Technologies Engineering Baghdad Iraq National University of Science and Technology Department of Medical Devices Engineering Technologies Nasiriyah Dhi Qar Iraq Al-Kunooze University College Technical Computer Engineering Department Basrah Iraq Al-Ayen University Scientific Research Center Thi-Qar Iraq Al-Farahidi University Medical Instruments Engineering Techniques Baghdad Iraq Al-Maamoun University College Computer Technology Engineering Baghdad Iraq

ISBN: (纸本)9798350359756

The rate of population increase in an urban region is an important Economic Growth and Financial factor (EGF). Urbanization helps a nation's economy develop and flourish. Urbanization refers to the spread of a city or suburb into its environs. The growth of cities indicates a country's economic well-being since cities are major financial hubs. Employment opportunities increase, and the local economy benefits as a metropolitan area expands. Demand for products and services rises due to urbanization, prompting a reallocation of resources (land, labor, and capital) from agriculture to other sectors of the economy. Cities have increasing needs, exacerbated by poor air and water quality, limited water supply, waste-disposal worries, and excessive energy usage. Economic expansion is impossible without technological progress, which makes it possible to mass-produce better goods at reduced prices. Effective city planning will be crucial for addressing these, and other challenges as the world's urban centers grow. Artificial Intelligence (AI) has already had a wide-ranging impact on our culture, and its influence on our daily lives and the economy is only growing. The research suggests that an AI-EGF might be a valuable tool for increasing the prosperity of urban areas. Some people believe that AI can help the economy develop and increase productivity. Analysis of large data sets may be performed effectively to raise productivity and standard of living. When a new product, market, or industry is created, consumer demand increases and new revenue opportunities arise. Recent years have seen a rise in the urgency of concerns about the impact of AI on organizations and consumers. The result of artificial intelligence (AI) on businesses and workers' financial security is a topic of intense interest. The experiments show that the suggested strategy is 37% more efficient on average. © 2023 IEEE.

关键词： Decision making

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages Information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Climate-Based AI-Powered Precision Irrigation: Sustainably Smart Agriculture Frameworks for Maximum Crop Yields

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Remote Sensing in Earth Systems sciences 2024年第1期8卷 161-172页

作者： Dhanke, Jyoti A. Srivastava, Diksha Menaga, D. Raj, Roop Kumar, Kambala Vijaya Jangir, Pradeep Mani, P. Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Biotechnology and Chemical Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan K.Ramakrishnan College of Technology Trichy India

A digital transformation is necessary for agriculture to achieve sustainability and increase productivity. With the use of IoT, high-tech detectors and helicopters can monitor crop health and environmental factors in real time, transmitting crucial data to cloud-based systems. Due to their robust computational and storage capabilities, these systems are capable of handling large datasets and carrying out intricate data analytics. Even before the COVID-19 pandemic, the world’s food supply was precariously balanced and insufficient. The present agricultural system prioritizes short-term gains above the preservation of the earth and its ecosystems. To construct agricultural systems that can meet the needs of ten billion individuals in the next 30 years, significant structural and automated changes are required. However, with the support of intelligent technology and the progress of artificial intelligence in agriculture, these obstacles might be overcome. Integration of data, processing images, and neural networks are examples of artificial intelligence technologies that have allowed producers to make better choices using up-to-date information and prediction models. The study emphasizes the need to address the ethical and practical concerns associated with climate-based AI in agriculture to promote its equitable deployment. The results of this research contribute to the ongoing debate about artificial intelligence’s possible applications in farming. New agricultural technologies provide optimism for a future that is environmentally friendly and will be able to feed the growing global population. The integration of renewable energy sources is one of the areas where artificial intelligence is seen to be able to assist in reaching global sustainability goals. Artificial intelligence (AI) has the potential to revitalize both newly developed and existing agricultural land by modifying, deploying, and implementing automated systems and tools. We take stock of where AI is at

关键词： Artificial intelligence Climatic conditions Cloud computing Deep learning Energy Sustainable precision irrigation

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Design and Implementation of Key-Value Database for Ship Virtual Test Platform Based on Distributed System

Design and Implementation of Key-Value Database for Ship Vir...

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作者： Qingyu Meng Kejia Zhang Haiwei Pan Maocai Yuan Baoying Ma College of Computer Science and Technology Harbin Engineering University China Ship Scientific Research Center

The virtual test platform is a vital tool for ship simulation and ***, the numerical pool ship virtual test platform is a complex system that comprises multiple heterogeneous data types, such as relational data, files, text,images, and animations. The analysis, evaluation, and decision-making processes heavily depend on data, which continue to increase in size and complexity. As a result, there is an increasing need for a distributed database system to manage these data. In this paper, we propose a Key-Value database based on a distributed system that can operate on any type of data, regardless of its size or type. This database architecture supports class column storage and load balancing and optimizes the efficiency of I/O bandwidth and CPU resource utilization. Moreover, it is specifically designed to handle the storage and access of large files. Additionally, we propose a multimodal data fusion mechanism that can connect various descriptions of the same substance, enabling the fusion and retrieval of heterogeneous multimodal data to facilitate data analysis. Our approach focuses on indexing and storage, and we compare our solution with Redis, MongoDB, and MySQL through experiments. We demonstrate the performance, scalability, and reliability of our proposed database system while also analysing its architecture's defects and providing optimization solutions and future research directions. In conclusion, our database system provides an efficient and reliable solution for the data management of the virtual test platform of numerical pool ships.

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Improving social media use for disaster resilience: challenges and strategies

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International Journal of Digital Earth 2023年第1期16卷 3023-3044页

作者： Nina S.N.Lam Michelle Meyer Margaret Reams Seungwon Yang Kisung Lee Lei Zou Volodymyr Mihunov Kejin Wang Ryan Kirby Heng Cai Department of Environmental Sciences Louisiana State UniversityBaton RougeUSA Landscape Architecture and Urban Planning Texas A&M UniversityCollege StationUSA School of Information Studies Center for Computation and TechnologyLouisiana State UniversityBaton RougeUSA Division of Computer Science and Engineering Louisiana State UniversityBaton RougeUSA Department of Geography Texas A&M UniversityCollege StationUSA Escambia County Natural Resources Management PensacolaFloridaUSA

This paper develops a social media-disaster resilience analysis framework by categorizing types of social media use and their challenges to better understand and assess its role in disaster resilience research and *** framework is derived primarily from several case studies of Twitter use in three hurricane events in the United States-Hurricanes Isaac,Sandy,and *** paper first outlines four major contributions of social media data for disaster resilience research and management,which include serving as an effective communication platform,providing ground truth information for emergency response and rescue operations,providing information on people's sentiments,and allowing predictive ***,there are four_key challenges to its uses,which include,easy spreading of false information,social and geographical disparities of Twitter use,technical issues on processing and analyzing big and noisy data,especially on improving the locational accuracy of the tweets,and algorithm bias in Al and other types of ***,the paper proposes twenty strategies that the four sectors of the social media community-organizations,individuals,social media companies,and researchers-could take to improve social media use to increase disaster resilience.

关键词： Twitter use challenges and strategies disaster resilience social and geographical disparities emergency rescue Hurricane Isaac Hurricane Sandy Hurricane Harvey

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Deep Learning Approaches for Environmental Monitoring in Smart Cities 9

Deep Learning Approaches for Environmental Monitoring in Sma...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Latha Soundarraj, Prem Jamadar, Vahid Mansur Rappai, Sherin Alkhafaji, Mohamed Ayad Shareef, Ashraf Mohammed Zearah, Sajad Ali School of Business & Management - Mba Christ University Maharashtra Pune412112 India Dr. DaulatraoAher College of Engineering Department of Mechanical Engineering Satara Maharashtra Karad415124 India Department of Computer Science Kothanur K. Narayanapura Karnataka Bangalore560077 India College of Engineering Technology National University of Science And Technology DhiQar Iraq National University of Science And Technology DhiQar Iraq Al-ayen University Scientific Research Center Thi-Qar Iraq

ISBN: (纸本)9798350365092

It introduces a novel integrated environmental monitoring system capable of doing on-the-go measurements. In metropolitan settings, air pollution is one of the most serious environmental threats to human health. The widespread use of automobiles, emissions from manufacturing processes, and the use of fossil fuels for propulsion and power generation have all contributed to this issue. Air quality predictions in smart cities may now be made using deep learning methods, thanks to the widespread adoption of these tools and their continued rapid growth. Particulate Matter (PM) with a width of less than 2.5 m (PM2.5) is one of the most perilous kinds of air pollution. To anticipate the hourly gauge of PM2.5 focus in Delhi, India, we utilized verifiable information of poisons, meteorological information, and PM2.5 fixation in the adjoining stations to make a spatial-worldly element for our CNN-LSTM-based deep learning arrangement. According to our experiments, our 'hybrid CNN-LSTM multivariate' method outperforms all of the above conventional models and allows for more precise predictions. © 2024 IEEE.

关键词： Air quality

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Optimum sensors allocation for drones multi-target tracking under complex environment using improved prairie dog optimization

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Neural Computing and Applications 2024年第18期36卷 10501-10525页

作者： Zitar, Raed Abu Alhadhrami, Esra Abualigah, Laith Barbaresco, Frederic Seghrouchni, Amal ElFallah Thales/SCAI Sorbonne University Abu Dhabi United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Research Center University of Tabuk Tabuk71491 Saudi Arabia Thales Land and Air Systems Paris France Mohammed VI Polytechnic University-Ai movement Rabat Morocco Sorbonne University Paris France College of Engineering Yuan Ze University Taoyuan Taiwan School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia Applied science research center Applied science private university Amman Jordan

This paper presents a novel hybrid optimization method to solve the resource allocation problem for multi-target multi-sensor tracking of drones. This hybrid approach, the Improved Prairie Dog Optimization Algorithm (IPDOA) with the Genetic Algorithm (GA), utilizes the strengths of both algorithms to improve the overall optimization performance. The goal is to select a set of sensors based on norms of weighted distances cost function. The norms are the Euclidean distance and the Mahalanobis distance between the drone location and the sensors. The second one depends on the predicted covariance of the tracker. The Extended Kalman Filter (EKF) is used for state estimation with proper clutter and detection models. Since we use Multi-objects to track, the Joint Probability Distribution Function (JPDA) estimates the best measurement values with a preset gating threshold. The goal is to find a sensor or minimum set of sensors that would be enough to generate high-quality tracking based on optimum resource allocation. In the experimentation simulated with Stone Soup, one radar among five radars is selected at every time step of 50-time steps for 200 tracks distributed over 20 different ground truths. The proposed IPDOA provided optimum solutions for this complex problem. The obtained solution is an optimum offline solution that is used to select one or more sensors for any future flights within the vicinity of the 5 radars. Environment and conditions are assumed to be similar in future drone flights within the radars’ defined zone. The IPDOA performance was compared with the other 8 metaheuristic optimization algorithms and the testing showed its superiority over those techniques for solving this complex problem. The proposed simulated model can find the most relevant sensor(s) capable of generating the best quality tracks based on weighted distance criteria (Euclidean and Mahalanobis). That would cut down the cost of operating extra sensors and then it would be possible to

关键词： Clutter (information theory)

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Simulation Secure MQTT Protocol Based on TLS in IoT-Fog Computing Environment 4th

Simulation Secure MQTT Protocol Based on TLS in IoT-Fog Comp...

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4th Doctoral Symposium on computational Intelligence, DoSCI 2023

作者： Kadhim, Ola Najah Ketab, Ahmed Saud Obaid, Ahmed J. Albermany, Salah Abdulhadi Raheem, Ahmed R. Hussien, Naseer Ali Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Engineering Technology National University of Science and Technology Dhi Qar Iraq Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (纸本)9789819937158

Fog computing is a way to run a computer that sends data from IoT devices to a group of nodes that get it in real time. These nodes do real-time processing of the data that they get, with the least amount of response time. The nodes send summaries of their analysis to the server regularly. A server-based application then looks at the data that has been sent from the different nodes and tries to come up with actionable information. Fog architecture needs more than just computer power. It needs to be able to communicate quickly between IoT devices and servers. In this paper, a secure Message Queuing Telemetry Transport Protocol (MQTT) broker in fog computing is described as an entity that acts as an intermediate to facilitate communication between MQTT clients, to receive messages that have been published by clients, to filter the messages based on topic, and to disseminate them to subscribers. The MQTT protocol publish/subscribe data transmission is encrypted using Transport Layer Security (TLS) in the proposed method. This has the additional advantages of maintaining network performance, improving channel utilization, lowering the number of loose packets in comparison with the case study that was carried out without TLS, and increasing network latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Internet of things

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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