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computing and Data Analytics (ICCDA), International Conference on

作者： Junath Naseer Ahamed Jaber Saleh Al Jabri Saif Awadh Al Saadi Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This research investigates the potential of Artificial Intelligence (AI) to improve educational resourcing in Oman. This research addresses the prevalent issues of inefficiency and inequity in Omani educational resourcing, by proposing hypothesis that AI integration can lead to more efficient resource allocation and enhanced educational outcomes. The study aims to explore this hypothesis through a comprehensive review of existing literature. The ultimate goal is to empower individuals, enhance educational opportunities, and promote innovation and growth within Oman. Literature review provide us a comprehensive understanding of the current state of research area and identifies gaps and opportunities, to enhance our research and contribute to the advancement of knowledge in the field. Analysis of Variance or ANOVA resulted in accepting the null hypothesis. This indicates no statistically significant differences in agreement levels between groups regarding AI and educational resourcing. However, the overall research suggests potential benefits of AI in this area. Future studies could explore implementation strategies, user needs, and the impact of AI on specific educational outcomes in Oman, while also considering the country's unique cultural context and educational needs.

关键词： Technological innovation Data analysis Education Learning (artificial intelligence) Resource management Stakeholders Cultural differences Artificial intelligence Analysis of variance Systematic literature review

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Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

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Computers, Materials & Continua 2022年第2期70卷 3295-3309页

作者： Mohamed Ibrahim Waly Mohamed Yacin Sikkandar Mohamed Abdelkader Aboamer Seifedine Kadry Orawit Thinnukool Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Faculty of Applied Computing and Technology Noroff University CollegeKristiansand4608Norway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Biomedical imaging is an effective way of examining the internal organ of the human body and its *** important kind of biomedical image is Pap smear image that iswidely employed for cervical cancer *** cancer is a vital reason for increased women’s mortality *** screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical ***-aided systems for cancerous cell detection need to be developed using deep learning(DL)*** study introduces an intelligent deep convolutional neural network for cervical cancer detection and classification(IDCNN-CDC)model using biomedical pap smear *** proposed IDCNN-CDC model involves four major processes such as preprocessing,segmentation,feature extraction,and ***,the Gaussian filter(GF)technique is applied to enhance data through noise removal process in the Pap smear *** Tsallis entropy technique with the dragonfly optimization(TE-DFO)algorithm determines the segmentation of an image to identify the diseased portions *** cell images are fed into the DL based SqueezeNet model to extract deeplearned ***,the extracted features fromSqueezeNet are applied to the weighted extreme learning machine(ELM)classification model to detect and classify the cervix *** experimental validation,the Herlev database is *** database was developed at Herlev University Hospital(Denmark).The experimental outcomes make sure that higher performance of the proposed technique interms of sensitivity,specificity,accuracy,and F-Score.

关键词： Biomedical images deep learning cervical cancer pap smear images computer aided diagnosis herlev database

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Posture Monitoring and Classification for Health Care of Bedridden Patients Using IoT and AI Algorithms

Posture Monitoring and Classification for Health Care of Bed...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Sunitha Cheriyan Sudha Sakthivel Thirupathi Regula Kiran Kumar S. G. Said Al Riyami Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Posture monitoring has become a crucial area of research and application, particularly considering the increasing concerns about musculoskeletal disorders and their association with prolonged lying and poor ergonomic practices. To solve the crucial problem of bedridden patient care, this study investigates the categorization of lying postures using four different deep learning models: The models used are Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), VGG16, and ResNe $t$ 50. Due to bedridden patients' difficulty in developing pressure ulcers, respiratory disorders, and other complications, the classification of laying positions which includes supine, prone, and lateral is essential. Some possible assistive devices that can inform caregivers or initiate some techniques to improve the patient's comfort and eliminate adverse effects of immobility can benefit from the identification of some postures in real-time. The models were trained using both balanced and unbalanced data sets from a set of labeled images covering different laying postures. Their performance was then determined by criteria including accuracy, RMSE, Kappa, recall, and precision. As for the performance, the proposed VGG model achieved better classification accuracy compared to the CNN, RNN, and ResNet models. In light of enhancing patient care as well as monitoring within the health care facilities, it is suggested from our result that these models could suggest potential solutions to posture identification for bedridden patients. By illustrating how AI posture classification systems can be implemented in IoT-based applications, the study also shows how future healthcare delivery can be improved and the problems caused by long periods of inactivity can be prevented.

关键词： Recurrent neural networks Accuracy Ergonomics Prevention and mitigation Refining Medical services Transforms Real-time systems Convolutional neural networks Monitoring

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First-principles electron-phonon interactions and polarons in the parent cuprate La2CuO4

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Physical Review Research 2025年第1期7卷 L012073-L012073页

作者： Benjamin K. Chang Iurii Timrov Jinsoo Park Jin-Jian Zhou Nicola Marzari Marco Bernardi Department of Applied Physics and Materials Science and Department of Physics California Institute of Technology Pasadena California 91125 USA PSI Center for Scientific Computing Theory and Data Paul Scherrer Institut 5232 Villigen PSI Switzerland Department of Physics Pohang University of Science and Technology Pohang 37673 Korea School of Physics Beijing Institute of Technology Beijing 100081 China Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL) École Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland

Understanding electronic interactions in high-temperature superconductors is an outstanding challenge. In the widely studied cuprate materials, experimental evidence points to strong electron-phonon (e-ph) coupling and broad photoemission spectra. Yet, the microscopic origin of this behavior is not fully understood. Here, we study e-ph interactions and polarons in a prototypical parent (undoped) cuprate, La2CuO4 (LCO), by means of first-principles calculations. Leveraging parameter-free Hubbard-corrected density functional theory, we obtain a ground state with the band gap and Cu magnetic moment in nearly exact agreement with experiments. This enables a quantitative characterization of e-ph interactions. Our calculations reveal two classes of longitudinal optical (LO) phonons with strong e-ph coupling to hole states. These modes consist of bond stretching and bond bending in the Cu-O plane as well as vibrations of apical O atoms. The hole spectral functions, obtained with a cumulant method that can capture strong e-ph coupling, exhibit broad quasiparticle peaks with a small spectral weight (Z≈0.25) and pronounced LO-phonon sidebands characteristic of polaron effects. Our calculations predict features observed in photoemission spectra, including a 40-meV peak in the e-ph coupling distribution function not explained by existing models. These results show that the universal strong e-ph coupling found experimentally in doped lanthanum cuprates is also present in the parent compound, and elucidate its microscopic origin.

关键词： High temperature superconductors

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An Efficient Distributed Energy and Consumption Method for Ensuring Wireless Sensor Network (WSN) Coverage Using the Firefly Algorithm 4th

An Efficient Distributed Energy and Consumption Method for E...

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4th International Conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Uma, K. Ramesh Kumar, C. Shanmugam, Thirumurugan School of Information Technology Vellore Institute of Technology Tamil Nadu Vellore India School of Computing Science and Engineering Galgotias University UP Greater Noida India Department of Information Technology University of Technology and Applied Sciences Suhar Muscat Oman

ISBN: (纸本)9789819916641

In this article, we are talking about the wireless sensor network sub-field of a body area sensor network. The establishment of the wireless body area sensor network has taken place after the refinement of the wireless sensor network was underway. We have introduced an innovative energy-efficient routing system that incorporates a fuzzy logic also bio-inspired firefly algorithm, which considers these limitations. Together, our proposal of correlation region selection then bio-stimulated firefly algorithm centered NBSs use fuzzy logic-based correlations to minimize energy transmission, while our joint firefly NBS proposal makes a valuable contribution by cutting energy consumption. To identify and select enriched regions that contain high energy and detect information, our suggested fuzzy logic-based correlation region selection mechanism is configured to favor those regions. We, however, suggested a bio-stimulated firefly algorithm fitness function for the collection of NBSs. To prevent exhaustion of valuable transmission, the fitness function accounts for the diffusion antiquity and residual energy for NBSs. To confirm the in-depth influence of our suggested scheme, we deportment numerous simulations exhausting the Nano-SIM tool to measure the various aspects involved in conserving energy resources, sinking endwise deferment, and increasing packet delivery ratio. We found that by using an optimized selection for correlated regions besides NBSs in refining network lifespan and packet delivery ratio although dipping the typical endwise delay, our proposed system accomplishes the significant task of determining which regions and NBSs are more important to the network and allows for the lower end-to-end delay. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fuzzy logic

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Tourism Enhancement Through Hybrid Mobile Application with Web Scraping and Recommendation System Using KNN and MF

Tourism Enhancement Through Hybrid Mobile Application with W...

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International Conference on Engineering and Emerging Technologies (ICEET)

作者： Thirupathi Regula Anshar Ali Dhanasekar N Department of IT College of Computing and Information Sciences University of Technology and Applied Science Muscat Oman

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Mobile applications have become an essential part of daily life. The tourists develop their tourist attractions and other facilities, which were being struggled after making plans within constraints. However, it is still incomplete and calls for some fine-tuning mechanisms with a view to creating the ideal tourist itinerary. This research has proposed a hybrid mobile app that integrates web scraping with machine learning technology to enhance the trip experience of visitors. The software does web scraping to scrape data in real time from several sites for bookings of hotels, flights, cabs, and food services with one interface. In solving the challenge of personalized tour planning, this app makes use of powerful machine learning techniques: K-Nearest Neighbors and matrix factorization-based recommendation algorithms. These algorithms consider user preferences for budget, length of travel, ratings, and other constraints while making personalized recommendations for tourism sites and activities. Software is constantly reacting to user feedback and behavior to enhance the relevance of recommendations over time. Unique in its method, it enables trip planning not only by unifying services but also makes sure travelers make informed and cost-effective decisions. It represents a huge step forward in intelligent, user-centric travel solutions by embedding the powers of web scraping for extensive data gathering and machine learning for personalization.

关键词： Machine learning algorithms Software algorithms Machine learning Nearest neighbor methods Software Real-time systems Mobile handsets Mobile applications Planning Recommender systems

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Enhancing Tourism Recommendation System Using KNN-Based Hybrid Mobile Application

Enhancing Tourism Recommendation System Using KNN-Based Hybr...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Thirupathi Regula Anshar Ali Dhanasekar N Department of IT College of Computing and Information Sciences University of Technology and Applied Science Muscat Oman

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Tourism development in today's digital era much counts on technology in order to achieve convenience and easiness of travel. The design of a hybrid mobile application recommendation system using K-Nearest Neighbors algorithm, the KNN, improves the development of the tourists. This application is designed to monitor information on locations of tourists, hotel room reservations, booking a flight, booking of a taxi, and interests before use. It allows for real-time personal behavioral choices based on budget, time, and location information for the Recommendation system. KNN algorithm in identifying similar users and locations more properly and therefore leads to more appropriate and correct choices. A hybrid mobile application will provide the advantages of both a native and an online application, ensuring consistency and responsiveness irrespective of devices and operating systems. The mobile application is supposed to run on both the Android and iOS systems. Tests and evaluation results reveal that the system is effective in enhancing user engagement with destinations, satisfaction, and intentionality of revisiting, thereby underpinning its contribution to the sustainable development of tourism. This work contributes to the creation of a scalable user-centered strategy driven by insights and machine learning. This paper achieved the accuracy of 82.1 % on Kaggle dataset of restaurants available in Amsterdam. This approach encourages tourism, that is one of the main pillars of economy development of many countries.

关键词： Reviews Operating systems Scalability Random access memory Nearest neighbor methods Real-time systems Mobile applications Sustainable development Recommender systems Public transportation

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Phytochemical screening, antioxidant properties, and photocytotoxicity of Clinacanthus nutans leaf extracts

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Vegetos 2024年第4期37卷 1652-1661页

作者： Chen, Chee-Shien Loke, Chui-Fung Poh, Tze-Ven Tan, Siow-Ping Department of Physical Science Faculty of Applied Sciences Tunku Abdul Rahman University of Management and Technology Kuala Lumpur 53300 Malaysia Research & Development and Commercialization Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia Department of Computer Science and Embedded Systems Faculty of Computing and Information Technology Tunku Abdul Rahman University Management and Technology Kuala Lumpur 53300 Malaysia

Clinacanthus nutans (Burm. f.) Lindau (C. nutans) is renowned in many tropical countries for its wide range of traditional uses and medicinal properties. This study qualitatively screened ethanol, hexane, and ethyl acetate (EA) leaf extracts of C. nutans for phytochemicals and evaluated their antioxidant activities using 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and ferric reducing antioxidant power (FRAP) assays. Furthermore, the photocytotoxicity of the extracts against the A431 human skin squamous carcinoma cells was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Qualitative phytochemicals screening revealed the presence of terpenes, alkaloids, phenols, quinones, and chlorophyll, whereas flavonoids and anthraquinones were absent in all crude extracts. In the DPPH assay, the EA crude extract exhibited the lowest EC₅₀ value of 31 ± 2 μg/mL, whereas the same extract demonstrated the highest FRAP value of 7.42 ± 1.87 mmol Fe2+/g DW. After photodynamic therapy (PDT) activation, EA crude extract demonstrated the strongest photocytotoxic activity against A431 cells with an IC₅₀ value of 8 ± 1 μg/mL, followed by the hexane crude extract (IC₅₀ = 16 ± 2 μg/mL) and the ethanol crude extract (IC₅₀ = 20 ± 1 μg/mL). However, all three crude extracts showed negligible activity against the A431 cells in the absence of PDT treatment, even at concentrations as high as 200 μg/mL after 24 h of dark incubation. These findings suggest that C. nutans could serve as a potential source of natural antioxidants and photosensitizers for photodynamic therapy in cancer treatment. © 2023, The Author(s) under exclusive licence to Society for Plant Research.

关键词： Antioxidant activity Clinacanthus nutans PDT treatment Photocytotoxicity Phytochemical screening

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Exploring Sensor-Based Smart Farming Technologies in the Internet of Things (IoT) 1

Exploring Sensor-Based Smart Farming Technologies in the Int...

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1st IEEE International Conference on computing and Data Science, ICCDS 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Balakumar, C. Sujatha, T. Wilfred Blessing, N.R. College of Computing and Information Sciences University of Technology and Applied Sciences Information Technology Department Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350365337

The Internet of Things (loT) is a new technology trend that is being used in almost every area of human life. IoT is used almost every aspect of people's lives. Significantly, with a projected increase in the world's population to 9.7 billion by 2050, agricultural output would need to increase at an even more rapid rate to fulfill the requirement. Modern tools, notably the Internet of Things, make this a reality. The IoT makes it possible for farms to function without human labor. It has several potential applications in agriculture, including large- scale farming, greenhouse farming and management. The sensors serve as the most crucial component of the loT. Sensing devices are mostly used for the purpose of learning about the soil and its surroundings. The sensor has several applications in agriculture, including but not limited to NPK (nitrogen, phosphorus, and potassium) measurement, disease detection, and soil moisture analysis. This study discusses how IoT applications contribute to efficient farming practices. It shows how the IoT can be applied to agriculture and exhibits the many sensors, applications, problems, strengths, and shortcomings that underpin this field. © 2024 IEEE.

关键词： Internet of things

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A Pruned Multi-Scale Skip Connected and Asymmetric-Convolution-Based U-Net Model for Land Cover Classification

A Pruned Multi-Scale Skip Connected and Asymmetric-Convoluti...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Almusawi, Muntather Parameshachari, B.D. Sunil, G. Reddy Muvva, Vijaya Bhaskar Siri, Dharmapuri The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Ece Bengaluru India Sr University Department of Computer Science & Artificial Intelligence Warangal India University of Technology and Applied Sciences -Shinas College of Computing and Information Sciences Department of Information Technology Shinas Oman Gokaraju Rangaraju Institute of Engineering and Technology Department of Cse Hyderabad India

ISBN: (纸本)9798350375442

Recently, land cover classification became an evolutionary study in Remote Sensing (RS) satellite interpretations. While extracting features from satellite images, spatial information causes the overfitting issue. In addition, computational complexity is occurring in land cover segmentation due to the usage of complex architectures. Thus, in this research, Multi-scale Skip connected and Asymmetric-Convolution-based U-Net (MACUNet) with pruning method is introduced to reduce the computational complexity. To reduce the overfitting, Batch Normalization (BN) layer is employed, simultaneously 3D Convolutional Neural Network (3D CNN) for feature extraction. Initially, the input is taken as satellite images from the GID dataset, where spatial and temporal features are extracted using 3D CNN model with BN layer. After that, these features are given as input to the MACUNet method to improve segmentation performance. Then, classification is done with class_num value utilization based on identified pixel region maps and classified into six classes such as build up, forest, water, farmland, meadows and others. From the results, the proposed pruned-MACUNet model produces better results than existing GeohashNet in terms of Mean Intersection over Union (MIoU) as 80.45% and f1-score as 90.23% respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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