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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Ripto Mukti Wibowo Bahjat Fakieh Muhammad Sher Ramzan Ahmed Saeed Alzahrani Muazzam Siddiqui Bander Alzahrani Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Luddy School of Informatics Computing and Engineering Indiana University Bloomington

Several regional head elections had to be postponed due to the pandemic, including in Indonesia because of the COVID-19 pandemic. Several big cities in Indonesia are of concern because of their large population and GDP. This study conducts analysis and testing of datasets taken from Open Data in a city in Indonesia. In addition to conducting research on regional head elections, we also present information on voters from the category of kids with disabilities. The steps used in this research are using regional mapping data of the city of Surabaya in the Election of the Regional Head. Download the data or dataset for the Regional Head Election ampersand Categories of kids with disabilities. Based on the dataset voters from the category of children with disabilities are more than 5 *** this research, we use Python to process our datasets & Big Data technology. Data cleaning or cleansing, Exploratory Data Analysis, and Empirical Cumulative Distribution Functions (ECDF) in python are also needed. Result from ECDF chart with steady increase (increment of 0.1). The highest variance value is in Electoral District 5 = 6.090 and the lowest value is in Electoral District 4 = 0.90. The result of Open Data is graphical data visualization and candidate scores to help as an alternative for the 2024 Regional Head Election and the Category of kids with disabilities.

关键词： Technological innovation Data analysis Pandemics Voting Data visualization Big Data Data models

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Estimation of Hourly PM2.5 Mass Concentration from Geostationary Satellite Aerosol Optical Depth Data

Estimation of Hourly PM2.5 Mass Concentration from Geostatio...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yuxin Sun Yong Xue Tengfei Cui Xingxing Jiang Shuhui Wu Chunlin Jin School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China School of Electronics Computing and Mathematics College of Engineering and Technology University of Derby Derby UK

Remote sensing inversion of global PM 2.5 is an important research topic. In the present study, the Aerosol Optical Depth (AOD) dataset was established by four geostationary satellites to estimate global PM 2.5 concentrations using improved Geographic Time-Weighted Regression model (IGTWR) models. Then a global hourly PM 2.5 concentration dataset was obtained in May 2020. The estimated result for PM 2.5 is verified at ground stations with R of 0.71 and RMSE (Root Mean Square Error) of 26.6 μg/m 3 . The results indicate that PM 2.5 has obvious spatial and temporal distribution in the world.

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An Artificial Intelligence (AI) Based X-AODV Routing Protocol in Wireless Networks 4

An Artificial Intelligence (AI) Based X-AODV Routing Protoco...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Hussain, Khalid Ullah Sheikh, Md Amin Jazri, Husin Bin Muzzaffar, Saira Chowdhury, Sohini Kavita School of Computing and Informatics Albukhary International University Kedah Alor Setar Malaysia School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Chandigarh Group of Colleges Jhanjeri Department of Computer Science and Engineering Punjab Mohali140307 India

ISBN: (纸本)9798350353266

The multi-hopping mechanism features in the wireless network environment, in which all nodes contain the information of link and connectivity in any infrastructure less manner. When mobile devices either jump from their original network or change their location without describing any fashion, it is named mobile ad hoc network MANET. To ensure the up to date information related to the location and to provide the routing traffic of the node, numerous protocols have been used to remain efficient in achieving this for instance, AODV, DSR, DSDV etc. While we, the peer-to-peer networking, have challenges in many places, security is something that warrants attention more than any other things. The signal corruption hampers data transmission in bedwire networks when the attacks take place. This kind of activity can touch anyone including the layers lying above the physical and below the network layer. In the current discussion we developed, proposition of diversified, efficient and secure AI-based X-AODV routing protocol. We have investigated our proposed protocol against the recent modified two protocols which are SAODV and ARAN. The outcome of simulation reveals that our proposed routing protocol takes place in successfully performing of attack detection and is also the best when it comes to providing security and performance. The Simulation outcomes exhibit the Networks' performance improvement. © 2024 IEEE.

关键词： Network layers

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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

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Computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Estimation Of Regional Air Pollution In Xuzhou City Based On WRF-Chem Model

Estimation Of Regional Air Pollution In Xuzhou City Based On...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wenhao Liu Yong Xue Xiaolu Ling Jian Gao Zeyu Tang Shuhui Wu Botao He School of Environment and Spatial Informatics China University of Mining and Technology Xuzhou China School of Electronics Computing and Mathematics College of Engineering and Technology University of Derby Derby UK

The air quality model can reproduce the processes of atmospheric transport, chemical reaction and removal, and analyze the spatio-temporal evolution law, internal mechanism and origin of air pollution. WRF-Chem model is the third generation of air quality model. Its biggest advantage is that the meteorological model and chemical transmission model are fully coupled in time and spatial resolution to achieve true online feedback, which is the main development direction of the future model. Therefore, this paper selected Xuzhou City, a heavy industry city with serious air pollution, as the research area, and took December when air quality was poor as the research time. WRF-Chem model was used to simulate and analyze meteorological parameters such as wind speed, temperature, relative humidity and concentration of air pollutants including PM 2.5 and O 3 in Xuzhou in December 2020. The simulation results were compared with the site data, and the simulation results under different parameter Settings of WRF-Chem mode were studied and compared.

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Backward Stepwise Modelling for Unemployment Rate in Malaysia

Backward Stepwise Modelling for Unemployment Rate in Malaysi...

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2023 IEEE International Conference on computing, ICOCO 2023

作者： Mansor, Mahayaudin M. Mostapar, Nur Fatehah Hasan, Nursalsabila Rozali, Norazuwa Mohamad Muhamed, Ariff Azly Masseran, Nurulkamal School of Mathematical Sciences College of Computing Informatics & Mathematics Universiti Teknologi Mara Shah Alam Malaysia Universiti Teknologi Mara Faculty of Business and Management Puncak Alam Malaysia Universiti Kebangsaan Malaysia Faculty of Science & Technology Department of Mathematical Sciences Bangi Malaysia

ISBN: (纸本)9798350302684

Although the post-COVID-19 unemployment rate is expected to decline further, in many economies, unemployment remains one of the most challenging issues to overcome. In addition, the unemployment rate is not always constant as many economic determinants are accountable for its variability. Thus, this study examines 15 economic determinants from the literature which are manufacturing production index (MPI), total salaries and wages, public consumption, total deposits, total loans, exports of goods and services, imports of goods and services, total employment, gross domestic product (GDP), consumer price index, producer price index (PPI), USD/MYR exchange rate (EXRT), population, overnight policy rate, and foreign direct investment. All data were collected from economic reports compiled by the Economic Planning Unit, Prime Minister's Department of Malaysia for the longest common period of 21 years beginning in 2001. A backward stepwise regression is implemented to determine a well-specified model to represent unemployment rates in Malaysia. In addition, this study focuses on a general-to-specific modelling procedure and its diagnostic tests using R programming. Multicollinearity existed, and the VIF table was used to examine the modelling transitions. The empirical evidence indicates that MPI, public consumption, total loan, GDP, PPI, EXRT, and total employment account for 94.3% of the variability in unemployment rates. We suggest that the use of machinery and technology in the manufacturing sector may contribute to the positive effects of MPI and PPI on unemployment. Findings indicate that higher growth in GDP, public consumption, total loans, and total employment would lead to a lower unemployment rate, unlike EXRT. This study reveals that GDP growth is the most significant economic determinant of the unemployment rate. © 2023 IEEE.

关键词： Data Analytics

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Go-X: Gain Ratio and Optuna Driven Optimization for Extreme Gradient Boosting (Xgboost) in Malware Detection

SSRN

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SSRN 2025年

作者： Rizal, Randi Fadilah, Haris Fikri Selamat, Siti Rahayu Rahmatulloh, Alam Bramantoro, Arif Department of Informatics Faculty of Engineering Universitas Siliwangi Tasikmalaya Indonesia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Malaysia School of Computing and Informatics Universiti Teknologi Brunei Brunei Universitas Negeri Yogyakarta Indonesia

The rapid expansion of the Internet of Things (IoT) has revolutionized industries by enabling seamless connectivity and automation, yet it has also exposed critical vulnerabilities to cyberattacks. IoT devices, often lacking robust security mechanisms, present easy targets for hackers, and their interconnected nature amplifies the risks of malware proliferation, potentially disrupting entire networks. To address these issues, this research aims to develop an eXtreme Gradient Boosting (XGBoost) algorithm integrated with Gain Ratio for feature selection and Optuna for hyperparameter optimization. This approach focuses on improving malware detection accuracy while reducing computational overhead by prioritizing the most relevant features and fine-tuning model configurations. The research method used is experimental research with several stages, starting from data collection, model design, model optimization, and finally model evaluation. The experiments showed that using Gain Ratio reduced model execution time by 5 seconds and Optuna optimization time by 1899.33 seconds. The final model achieved outstanding performance, with precision, recall, accuracy, and F1-score all at 99.99%. When compared to previous research using the same dataset, this model outperformed with an accuracy improvement of 5.99%. This research demonstrates the effectiveness of feature selection in reducing Optuna optimization time and highlights the successful implementation of the XGBoost model. While the model achieves good accuracy and reduces execution time slightly, it remains resource-intensive, making it more suitable for deployment on edge servers rather than direct integration into IoT devices. Future research should explore methods to reduce resource requirements, such as implementing quantization techniques or utilizing lighter models like LightGBM. © 2025, The Authors. All rights reserved.

关键词： Malware

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Behind the Scenes: An Explainable Artificial Intelligence (XAI) on the Service Classification of the 5G/B5G Network

Behind the Scenes: An Explainable Artificial Intelligence (X...

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Digital Transformation and Applications (ICDXA), International Conference on

作者： Noormadinah Allias Diyana Ab Kadir Akibu Mahmoud Abdullahi Shahrinaz Ismail Faculty of Computing and Information Technology Tunku Abdul Rahman University of Management and Technology Kuala Lumpur Malaysia Computer Engineering Technology Universiti Kuala Lumpur- Malaysia Institute of Information Technology Kuala Lumpur Malaysia School of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology Asia Pacific University of Technology & Innovation (APU) Kuala Lumpur Malaysia

Fifth generation and beyond (5G/B5G) is part of the next generation of mobile technology, which is expected to offer more capacity and faster speeds than the previous generation Long-Term Evolution (LTE) network. These features enable 5G/B5G to offer users a wide range of services, including smart cities, entertainment and multimedia, healthcare and mission-critical applications that benefit the entire economy and communities. However, due to growing demand, telecom service providers need to provide better Quality of service (QoS) on their networks to meet user expectations. Therefore, it is necessary to implement service classification that enables service providers to select the right network slices for each service to improve network QoS. Previous studies have predicted the classification of 5G/B5G services using traditional machine learning algorithms such as random forest and decision tree, achieving good accuracy. However, prediction based on black-box machine learning models was not transparent enough, so mobile service providers could not understand the reliability and interpretation of the results. Therefore, this article aims to model the system performance of 5G Key Performance Indicators (KPI) and extend the experiment to Explainable Artificial Intelligence (XAI) by using the Explainable AI to look behind the scenes of the chosen black-box models for service classification of 5G/B5G networks, as this is crucial for telecom service providers to make critical investment decisions around customer and employee experience. The results from the experiments show that the random forest has the highest confidence towards class Label 3 with the probability of 35 percent.

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Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chains

arXiv

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arXiv 2024年

作者： Zhao, Jiale Zhuang, Wanru Song, Jia Li, Yaqi Lu, Shuqi DP Technology Beijing China Institute of Computing Technology UCAS Beijing China Xiamen University School of Informatics Xiamen China Xiamen University Institute of Artificial Intelligence Xiamen China

In recent years, there has been a surge in the development of 3D structure-based pre-trained protein models, representing a significant advancement over pre-trained protein language models in various downstream tasks. However, most existing structure-based pre-trained models primarily focus on the residue level, i.e., alpha carbon atoms, while ignoring other atoms like side chain atoms. We argue that modeling proteins at both residue and atom levels is important since the side chain atoms can also be crucial for numerous downstream tasks, for example, molecular docking. Nevertheless, we find that naively combining residue and atom information during pre-training typically fails. We identify a key reason is the information leakage caused by the inclusion of atom structure in the input, which renders residue-level pre-training tasks trivial and results in insufficiently expressive residue representations. To address this issue, we introduce a span mask pretraining strategy on 3D protein chains to learn meaningful representations of both residues and atoms. This leads to a simple yet effective approach to learning protein representation suitable for diverse downstream tasks. Extensive experimental results on binding site prediction and function prediction tasks demonstrate our proposed pre-training approach significantly outperforms other methods. Our code will be made public. © 2024, CC BY.

关键词： Proteins

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Accidental Face Recognition and Detection Using Machine Learning 1

Accidental Face Recognition and Detection Using Machine Lear...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Sharma, Ashish Agrawal, Piyansh Krishan Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications UP Mathura India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

The data collected from all the States and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms. © 2023 IEEE.

关键词： Face recognition

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