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7th International Conference of computer and Informatics engineering, IC2IE 2024

作者： Kurniawan, Rahmad Ibnu Daqiqil, I.D. Fatayat Sukamto Fitriansyah, Aidil Lestari, Fitra Husti, Ilyas Universitas Riau Faculty of Mathematics and Natural Sciences Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Industrial Engineering Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798331505530

The assignment of preachers to mosques in Riau Province faces significant challenges due to the geographic dispersion of mosques and inconsistent scheduling practices. Preachers often travel long distances, and mismatched assignments lead to inefficiencies and reduced effectiveness in delivering sermons. This study addresses these issues by leveraging advanced clustering techniques to optimize preacher assignments. Specifically, we applied Gaussian Mixture Models (GMM) to cluster mosque and preacher data based on geolocation, activity, and profiles. We integrated Principal Component Analysis (PCA) for dimensionality reduction to enhance the clustering performance, forming a GMM-PCA hybrid approach. Data was obtained from the Indonesian Ulema Council Riau Province Chapter, which consists of 185 mosques and 250 preachers, totaling 435 data points. The results indicate that using GMM alone, the optimal clustering was achieved with 10 clusters, yielding a Silhouette Score of 0.31. The GMM-PCABIC hybrid approach improved clustering quality. It achieved optimal results with 9 clusters, a significantly higher Silhouette Score of 0.48, and a lower BIC Score of 2696.18. These findings demonstrate the novelty of the GMM-PCA-BIC hybrid method in effectively capturing the complex characteristics of mosque and preacher data, ensuring efficient and relevant preacher assignments. Implementing this optimized clustering approach allows mosque administrators to make data-driven decisions, enhancing the effectiveness of religious activities. © 2024 IEEE.

关键词： Dimensionality reduction

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AI and IoT in Digital Marketing: Enhancing Automation, Personalization, and Consumer Interaction

AI and IoT in Digital Marketing: Enhancing Automation, Perso...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Naredla, Suman Singh, Madhulika Chaturvedi, Nidhi Banerjee, Devleena Gobinath, V.M. Anil Tiwari, Dr. Sr University School of Business Telangana Warangal India Quantum University Computer Science and Engineering Uttarakhand Roorkee India Emirates Aviation University Faculty of Business Management Dubai United Arab Emirates Christ University India Rajalakshmi Institute of Technology Mechanical Engineering Department Tamilnadu Chennai India Rnb Global University Bikaner Department of Commerce and Management India

ISBN: (纸本)9798331508685

An interconnected system of Internet-enabled devices that can collect and transmit statistics via a wireless connection without the assistance of people is known as the Internet of Things (IoTs). Accordingly, the IoT's seductive power is causing significant shifts in the current corporate environment. The digital marketing industry stands to gain the most from this innovation, which is currently causing major shifts in many other sectors. Using a variety of digital marketing strategies, this innovation gathers numerous types of consumer statistics. IoT technological advancements' impact on digital marketing tactics and customer interaction has emerged as a crucial research topic as it begins to pervade many facets of everyday existence. This investigation examines the application of IoT-based machine learning (ML) in digital marketing for the food business. To give ML-based suggestions, consumer data is analyzed, interests are identified, and conduct is predicted using ML approaches. The ensemble technique aggregates the results of multiple ML techniques to produce an individual forecast. The accuracy matrices graphs for the K-nearest neighbor and decision trees produced excellent estimations, with 100% accuracy and 0.0 error, correspondingly. The naïve Bayes method achieved 97.2% accuracy with a 0.029 error, successfully identifying the right tags across every category. The guided ensemble of 3 ML methods is demonstrated by effectively enhancing digital marketing tactics in the food distribution industry by reducing duration and expenses. © 2025 IEEE.

关键词： Decision trees

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Deep Learning Technique for Oropharyngeal Cancer Detection in Medical Imaging

Deep Learning Technique for Oropharyngeal Cancer Detection i...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Kumar R S. Pazhanirajan Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram Tamil Nadu India

Oropharyngeal carcinoma is a life-threatening disease that requires prompt diagnosis and treatment. This study uses deep learning algorithms to present a novel method for improving medical imaging to detect oropharyngeal cancer. Deep learning has demonstrated immense potential for medical image processing applications in recent years. This research capitalizes on this opportunity by devising a dependable model for detecting oropharyngeal cancer. Examining oropharyngeal cancer with an emphasis on the critical significance of early detection. This study emphasizes the need for more precise and efficient diagnostic techniques. This work presents a comprehensive strategy from data acquisition and cleansing to the development of the model's underlying architecture, training, validation, and explication. The results section presents quantitative and qualitative data demonstrating the model's ability to detect oropharyngeal cancer. The proposed model has the potential to improve the quickness and accuracy with which oropharyngeal cancer is diagnosed, thereby benefiting patients. It emphasizes the need for additional research and the implementation of deep learning innovations in healthcare.

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Blockchain Based Healthcare System to Secure Health Records

Blockchain Based Healthcare System to Secure Health Records

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International Conference on computer Communication and Informatics (ICCCI)

作者： Kanika Agrawal Mayank Aggarwal Department of Computer Science & Engineering Faculty of Engineering & Technology Gurukula Kangri (Deemed to be University) Haridwarx India

ISBN: (数字)9798350348217

ISBN: (纸本)9798350348224

With the advancement in technology, blockchain is playing a major role all around the world. The blockchain applications shows it's significance in many applications such as financial services, aircraft industry, e-voting mechanisms etc. However, the healthcare sector is focusing more attention towards the technology to use benefits of security, decentralization and trust. Motivated from the aforementioned facts, this paper proposes MyEasyHeathcare, an efficient healthcare model to help provide the patients a secure facility from the hospital. The model helps doctor and patient to register and then authorizes a particular doctor to a particular patient. The physician or pathologist checks the patients accordingly and gives prescription as treatment. This paper also discusses research challenges faced by traditional systems and their possible blockchain solutions.

关键词： Authorization Industries Hospitals Atmospheric modeling Focusing computer architecture Blockchains

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The Veselago Lens as a Scattering Problem: 2D Solutions Through Asymptotics 25

The Veselago Lens as a Scattering Problem: 2D Solutions Thro...

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25th International Conference on Electromagnetics in Advanced Applications, ICEAA 2024

作者： Bevacqua, Martina T. Keleshteri, Marzieh Eini Okhmatovski, Vladimir LoVetri, Joe Isernia, Tommaso DUES Université degli Studi Mediterranea di Reggio Calabria Reggio Calabria Italy Department of Pharmacology and Therapeutics Rady Faculty of Health Science University of Manitoba WinnipegMB Canada Department of Electrical and Computer Engineering University of Manitoba WinnipegMB Canada

ISBN: (纸本)9798350360974

This contribution focuses on the analytical derivation of the Green's function of the ideal lossless Veselago lens. The problem is dealt with by considering the lens as a scattering object. Then, the electromagnetic field of a line-source illuminating the lens is seen as the sum of the field due to the line-source in free space plus the scattered field due to the lens. The asymptotic evaluation is exploited to evaluate the thus obtained field integral representation. The approach is also a step towards the truncated lens case. © 2024 IEEE.

关键词： Inverse problems

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Classification Rules based Breast Cancer Detection using Machine Learning Approach 5

Classification Rules based Breast Cancer Detection using Mac...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Mitra, Dipra Sharma, Neha Rashid, Mamoon Singh, Rajesh Amity University Computer Science & Engineering Jharkhand Ranchi India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (纸本)9798350398267

One of the medical field's most researched issues is cancer diagnosis. Many researchers have concentrated on performance enhancement and achieving successful outcomes. One of the most lethal forms of cancer is breast cancer. A significant issue in cancer diagnosis research is the diagnosis of this cancer. A kind of artificial intelligence called machine learning allows a machine to grow over time. In bio informatics, machine learning is frequently employed, notably in the detection of breast cancer. supervised learning method known as K-nearest neighbors' approach, is one well-liked techniques. It's really intriguing to use the K-NN in medical diagnostics. The value of parameter 'k' & distance have a significant impact on the findings' quality. This indicates how many neighbors are in proximity. In this paper, we assess the performance of various K-NN algorithmic distances. Additionally, we investigate this distance using various 'k' parameter values and classification algorithms (the formula used to determine a sample's classification). © 2022 IEEE.

关键词： Data mining

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Sine-Squared Pulse Approximation for Matched Filter Design Using Generalized Bessel Polynomials and Particle Swarm Optimization 11

Sine-Squared Pulse Approximation for Matched Filter Design U...

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2022 11th International Conference on Information and Electronics engineering, ICIEE 2022

作者： Chutchavong, V. Anuwongpinit, T. Pumee, T. Benjangkaprasert, C. Janchitrapongvej, K. Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Faculty of Science and Technology South East Bangkok College Bangkok Thailand

This paper presents the study of mathematical characteristics of generalized Bessel polynomial that can be applied to approximate a sine-squared pulse for designing matched filters in communication systems. The proposed pulse can be designed by using the transfer function, in which the numerator is the five pairs of a transmissions zero pairs, and the generalized Bessel polynomial is used as the denominator. A parameters of generalized Bessel polynomials can be adjusted by particle swarm optimization to find the best parameter value. from the simulation results can be found that a parameter can be adjusted. A proposed pulse is close to the ideal response in mainlobe, and one sidelobe to four sidelobes with stability, which outperformed previous research. © Published under licence by IOP Publishing Ltd.

关键词： Matched filters

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Integrating Software-Defined Networking and Fog Computing for a Novel Routing Protocol in Vehicular Ad-Hoc Networks

Integrating Software-Defined Networking and Fog Computing fo...

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International Conference on Advanced Communication Technologies and Networking (CommNet)

作者： Khalid A. Darabkh Mamoun F. Al-Mistarihi Bayan Abdallah Odat Computer Engineering Department School of Engineering The University of Jordan Amman Jordan Electrical Engineering Department Faculty of Engineering Jordan University of Science and Technology P.O. Box 3030 Irbid Jordan

ISBN: (数字)9798350367027

ISBN: (纸本)9798350367034

This study presents an intelligent strategy designed to enhance the efficacy of routing in VANETs. In particular, this work presents a new clustering approach for Cluster Head (CH) selection based on a weighted formula. Our protocol’s objectives are to create clusters and allocate $\mathbf{C H}$ to each of them. In our protocol, three significant parameters are involved in $\mathbf{C H}$ selection process, including vehicle speed, average vehicle distance among other vehicles, and vehicle LifeTime (LT). By considering vehicle speed, the protocol enhances route stability and dependability while the average distance of a vehicle among other vehicles mitigates long-distance communications. Interestingly, the vehicle LT greatly minimizes the control overhead. Furthermore, for improved programmability and scalability, our approach incorporates novel technologies like Software Defined Network (SDN) and Fog Computing (FC) for location awareness. Proper network simulations are performed to test the performance of our proposed protocol, and considerable increases in terms of throughput are achieved over those of our protocol’s counterparts.

关键词： Computational modeling Voting Transportation Throughput Routing protocols Stability analysis Software defined networking Vehicle dynamics Standards Edge computing

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Joint Radar-Communication Systems by Optimizing Radar Performance and Quality of Service for Communication Users

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 778-790页

作者： Tsinos, Christos G. Arora, Aakash Tsiftsis, Theodoros A. National and Kapodistrian University of Athens Department of Digital Industry Technologies Evripus Campus Euboea 34400 Greece University of Twente Radio Systems Group Faculty of Electrical Engineering Mathematics and Computer Science AE Enschede7500 Netherlands Indian Institute of Technology Delhi Centre for Applied Research in Electronics New Delhi110016 India University of Thessaly Department of Informatics and Telecommunications Lamia35100 Greece Jinan University Zhuhai Campus School of Intelligent Systems Science and Engineering Zhuhai519070 China

In this article, the problem of linear precoding and radar receive beamforming design for joint radar-communication (JRC) systems is studied. A multiple antenna base station (BS) that serves multiple single-antenna user terminals on the downlink is assumed. Furthermore, the BS employs a simultaneous radar function in the form of point-like target detection from the reflected return signals in a signal-dependent interference environment. In this work, we jointly design the JRC linear precoder and the radar receive beamformer, thus aiming to optimize the performance of the radar part while maintaining a desired quality of service (QoS) for the communication one subject to a total transmit power constraint. To that end, we formulate a challenging fractional nonconvex optimization problem via which the optimal precoder and radar receive beamformer are derived. Then, we develop algorithmic solutions based on the majorization-minimization (MM) principle and the semidefinite relaxation (SDR) methodology for the formulated optimization problem. The performance of both the proposed solutions is examined and compared to the one of a system that supports only the radar functionality via numerical results. © 2023 IEEE.

关键词： Signal to noise ratio

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Executive Function and Intelligent Goal-Directed Behavior: Perspectives from Psychology, Neurology, and computer science

Executive Function and Intelligent Goal-Directed Behavior: ...

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Workshops on AI4EA, F-IDE, CoSim-CPS, CIFMA 2022, Collocated with the 20th International Conference on Software engineering and Formal Methods, SEFM 2022

作者： Pluck, Graham Cerone, Antonio Villagomez-Pacheco, David Faculty of Psychology Chulalongkorn University Bangkok Thailand Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Universidad Nacional de Educación Azogues Ecuador

ISBN: (纸本)9783031262357

The concept of executive function, as top-down control of processes, originated in computer science in the 1950s. However, it has since become an important concept in a range of human sciences, particularly for its explanatory power in psychology, education, and clinical neurosciences. Nevertheless, its use has been limited by vague definitions and confusion between the related conceptualizations of executive process and intelligence. Here we explore the concept of executive control in detail, drawing on psychology, neurology, and computer science/human-machine interaction. We explore both computationalist and embodied cognition approaches. We describe the core goal-directed and resource-limited features of executive control, its fractionation into components, and partial overlap with psychometric conceptions of intelligence. We also examine its associations with neurological systems beyond those usually linked to executive function (i.e., the frontal lobes). We propose that executive functions are ‘intelligent’, and can be defined by their goal-directedness. Furthermore, executive function tasks can be classified by their task goals into one of three types: Those that involve i) convergent, or ii) divergent thinking, or iii) not responding, such as in psychomotor response inhibition. Conventional intelligence tests measure only convergent thinking. The recognition of non-convergent executive functions allows the identification of executively controlled intelligent goal-directed behavior beyond that controlled by domain-general cognitive processes. This reconceptualization may benefit research in education, clinical and cognitive sciences, as well as the quest for artificial general intelligence. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neurology

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