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arXiv 2025年

作者： Karagenc, Ayse Acikgoz, Mehmet Araci, Serkan Department of Mathematics Faculty of Science and Arts Gaziantep University GaziantepTR-27310 Turkey Department of Computer Engineering Faculty of Engineering Hasan Kalyoncu University GaziantepTR-27010 Turkey

In this paper, we introduce a new class of polynomials, called probabilistic q Bernstein polynomials, alongside their generating function. Assuming Y is a random variable satisfying moment conditions, we use the generating function of these polynomials to establish new relations. These include connections to probabilistic Stirling numbers of the second kind and higher-order probabilistic Bernoulli polynomials associated with Y. Additionally, we derive recurrence and differentiation properties for probabilistic q-Bernstein polynomials. Utilizing Leibniz’s formula, we give an identity for the generating function of these polynomials. In the latter part of the paper, we explore applications by choosing appropriate random variables such as Poisson, Bernoulli, Binomial, Geometric, Negative Binomial, and Uniform distributions. This allows us to derive relationships among probabilistic q-Bernstein polynomials, Bell polynomials, Stirling numbers of the second kind, higher-order Frobenius-Euler numbers, and higher-order Bernoulli polynomials. We also present p-adic q-integral and fermionic p-adic qintegral representations for probabilistic q-Bernstein *** Codes 11B83, 11B73, 05D40 © 2025, CC BY.

关键词： Random variables

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The Enhanced Optimization on Deep Learning Technologies for Data science Practices 3

The Enhanced Optimization on Deep Learning Technologies for ...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dhabliya, Dharmesh Reddy, B. Rajarajeswari, S. Ranganathaswamy, M.K. Nivesh Pandey, Megha Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Deep studying technologies (DLTs) were adopted with the aid of many fields, consisting of healthcare, finance, and records technology. This paper ambitions to take a look at how DLTs can be improved for facts technological know-how practices, in addition to to speak about their capacity for driving advances in statistics-pushed studies. First of all, we provide a top level view of the contemporary kingdom of DLTs, and the important thing algorithms that cause them to appealing for data science packages. Then, we delve into a number of the ways wherein DLTs may be optimized for better overall performance. We illustrate the optimization strategies via numerous examples, which include community structure search, computational useful resource management, and active mastering. Moreover, we explore the use of switch studying and reinforcement getting to know to in addition improve the performance of DLTs in information science. In the end, we talk the benefits of DLTs for statistics technology and their ability for advancing the field. This paper highlights the significance of optimizing DLTs for records technological know-how, and the numerous possibilities that leveraging these technology will offer. © 2023 IEEE.

关键词： computational management optimization performance possibilities

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Blockchain Technology for Enhanced Security of Iot Healthcare Devices: A Novel Lightweight Hash Function Approach and Secure Management system

SSRN

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SSRN 2024年

作者： Ramadan, Rabie A. Khalifa, Hany S. Dessouky, Mohamed M. Aboshosha, Bassam W. Department of Computer Engineering Cairo Universality Giza12613 Egypt College of Computer Science and Engineering University of Ha'il Ha'il81481 Saudi Arabia Hail University Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Communication and Computer Engineering The Higher Institute of Engineering El Shorouk Academy Cairo11837 Egypt

Blockchain technology has emerged as a novel innovation with diverse applications across different fields, including the healthcare industry. Within healthcare, blockchain technologies facilitate the secure storage and exchange of patient data among various stakeholders, such as hospitals, diagnostic labs, drug companies, and medical practitioners. The inherent features of Blockchain enable accurate identification of critical vulnerabilities within the medical sector, some of which can have life-threatening consequences. By enhancing the effectiveness, confidentiality, and availability of medical information exchange, Blockchain has the potential to revolutionize healthcare operations. This paper focuses on harnessing blockchain technology to enhance the security of Internet of Things (IoT) devices in the healthcare industry. This paper proposes a novel lightweight collision-resistant hash function family for IoT healthcare applications, producing 256- or 512-bit outcomes. The proposed hash function is based on the randomness principle to address the challenges associated with IoT devices' limited resources and power supply. The selection of hash function components is carefully balanced between security and simplicity, utilizing the nonlinear function represented in S-boxes. The proposed approach replaces the 8-bit S-boxes with a robust 4-bit to reduce the complexity. The linear function uses a permutation box and the XOR function for key mixing. This novel hash function is utilized as the core of the blockchain module. The paper also introduces a secure healthcare management system utilizing the proposed lightweight security algorithm. The proposed algorithm is examined for its security, and the healthcare management system is validated. © 2024, The Authors. All rights reserved.

关键词： Hospitals

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The Development of a Glove-like Controller Interface for VR applications 14

The Development of a Glove-like Controller Interface for VR ...

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14th ACM SIGMM Workshop on Immersive Mixed and Virtual Environment systems, MMVE 2022

作者： Salgado, Eduardo Pereira Salgado, Eurico Marques Salgado, Débora Pereira Rodrigues, Thiago Braga Faculty of Computer Science Federal University of Uberlândia Uberlândia Brazil Faculty of Mechanical Engineering Federal University of Uberlândia Uberlândia Brazil Faculty of Engineering & Informatics Technological University of the Shannon Athlone Ireland Department Computer and Software Engineering Technological University of the Shannon Athlone Ireland

ISBN: (纸本)9781450393829

Smart gloves are wearable interfaces which provide detection of movements and gestures, kinesthetic, and even tactile feedback. These capabilities are important for immersive applications as it can bring more realistic features. The presented paper shows a concept of a working in progress application of a smart glove. The proposed hardware uses resistive flexible and electromagnets sensors to provide tactile and haptic feedback to be used in immersive applications such as the ones that use Virtual Reality (VR). The project will be done in a way that can provide user-centric requirement such as: the glove must be lightweight, comfortable, adjustable to different hands and finger shapes, and low cost to be manufactured. The intend is to use this glove as part of a biggest framework and immersive application. © 2022 ACM.

关键词： Wearable sensors

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Steel Plate Fault Detection using the Fitness Dependent Optimizer and Neural Networks

arXiv

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arXiv 2024年

作者： Farahmand-Tabar, Salar Rashid, Tarik A. Department of Civil Engineering Eng. Faculty of Engineering University of Zanjan Zanjan Iran Department of Computer Science and Engineering University of Kurdistan Hewler KR Erbil Iraq

Detecting faults in steel plates is crucial for ensuring the safety and reliability of the structures and industrial equipment. Early detection of faults can prevent further damage and costly repairs. This chapter aims at diagnosing and predicting the likelihood of steel plates developing faults using experimental text data. Various machine learning methods such as GWO-based and FDO-based MLP and CMLP are tested to classify steel plates as either faulty or non-faulty. The experiments produced promising results for all models, with similar accuracy and performance. However, the FDO-based MLP and CMLP models consistently achieved the best results, with 100% accuracy in all tested datasets. The other models' outcomes varied from one experiment to another. The findings indicate that models that employed the FDO as a learning algorithm had the potential to achieve higher accuracy with a little longer runtime compared to other algorithms. In conclusion, early detection of faults in steel plates is critical for maintaining safety and reliability, and machine learning techniques can help predict and diagnose these faults accurately. © 2024, CC BY.

关键词： Fault detection

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Performance Evaluation of Diverse ML Techniques in Disasters Twitter classification 2

Performance Evaluation of Diverse ML Techniques in Disasters...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Nath, Basudev Sahoo, Deepak Biswal, Bhramara Bar Patra, Sudhansu Shekhar Ray, Rakesh Kumar Sri Sri University Faculty of Emerging Technologies Cuttack India Giet University Dept. of Computer Science & Engineering Gunpur India Kiit Deemed to be University School of Computer Applications Bhubaneswar India Centurian University of Technology and Management Mca Department Khordha India

ISBN: (纸本)9798350372892

Social media has grown to be an essential and significant aspect of our lives in the modern day. Due to the widespread usage of laptops, mobile phones, and other devices, individuals find it simpler to communicate with one another and to post their opinions, concerns, and other information on social media sites like Facebook, Instagram, and tweeter. Several government and commercial organizations use these social media platforms to gather text and assess people's sentiments during times of crisis. Tracking the tweets relating to the accident is too tough for them to do. To categories tweets as 'disaster' or 'non-disaster,' the paper implemented three models, the Naïve-Bayes, KNN and support vector machine classification models. The Naïve-Bayes algorithm shows the highest accuracy of 86.591. © 2024 IEEE.

关键词： Tweets

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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization 1st

A Comparative Study of the Coulomb’s and Franklin’s Laws I...

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1st International Conference on Pervasive Knowledge and Collective Intelligence on Web and Social Media, PerSOM 2022

作者： Ghasemi, Mojtaba Zare, Mohsen Zahedi, Amir Hemmati, Rasul Abualigah, Laith Forestiero, Agostino Department of Electronics and Electrical Engineering Shiraz University of Technology Shiraz Iran Department of Electrical Engineering Faculty of Engineering Jahrom University Fras Jahrom Iran Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Department of Electrical and Computer Engineering Marquette University MilwaukeeWI United States Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Faculty of Information Technology Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang George Town11800 Malaysia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al Al-Bayt University Mafraq25113 Jordan Institute for High Performance Computing and Networking National Research Council of Italy Rende Italy

ISBN: (纸本)9783031314681

Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available at https://***/projects/cfa, https://***/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Charged particles

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A Novel Approach for Detection of Agricultural Crops Using CNN

A Novel Approach for Detection of Agricultural Crops Using C...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Mahesh T R Aman Singh Chandel Harshitha Gupta Rakshit Jain Sadhvi Garg Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed to-be University) Bangalore India

Crop classification is an important aspect of farming because it improves crop management and increases crop yield. This study proposes a CNN-based crop classification technique using high-resolution images. To use this method, we created a database of 30 crops, including jute, maize, almonds, rice, sugarcane, banana, wheat, and others. The Convolutional Neural Network (CNN) is an Artificial Intelligence-based algorithm that learns similar features from images and is capable of performing a variety of tasks such as image classification, object detection, and segmentation. In this system, CNN or ConvNet is used to learn the features provided in the input images by sequentially entering several layers such as Convolution, Activation function (RELU), Pooling, and Flattening. Finally, with only a few explanations in the training set, this providing outstanding classifies new images. The proposed method has two major components: preprocessing and categorization. The experimental results show that the proposed tactic worked extremely well, with a ground truth of 0.9264.

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Discovery of association rules between factors affecting user satisfaction in software project by FP-GROWTH Algorithm 18

Discovery of association rules between factors affecting use...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Kaewbanjong, Katawut Intakosum, Sarun King Mongkut's Institute of Technology Ladkrabang Software System Engineering Laboratory Department of Computer Science Faculty of Science Bangkok Thailand

ISBN: (纸本)9780738111278

The main objective of this work was to find the association rules between factors affecting user satisfaction in software project by using an association rule discovery technique. Data from 191 software projects were collected and association rules between 15 Selected factors were discovered by FP-growth algorithm. Primarily, 281 association rules were discovered. Rules that were not directly related to user satisfaction were filtered out by simple criteria. In the end, 11 final association rules that passed those criteria were obtained with an average confidence value of 65.30%. These rules can incorporated in the planning and administration of a software project to gain utmost user satisfaction © 2021 IEEE.

关键词： Association rules

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A Type-1 Fuzzy Logic Framework for Early Risk Assessment of Gestational Diabetes Mellitus Using Clinical Risk Factors 2

A Type-1 Fuzzy Logic Framework for Early Risk Assessment of ...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Suman, Saurabh Pacharaney, Utkarsha Gote, Pradnyawant M. Faculty of Engineering and - Technology Department of Computer Science & Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence & Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

Gestational Diabetes Mellitus (GDM) is a condition that can put at risk both mother and fetus but early and proper risk assessment need to be carried out for effective management. This paper discloses a Mamdani-kind Fuzzy Inference system to evaluate GDM risk dependent on clinical parameters like age, parity, glucose levels, body mass index (BMI), and insulin levels. The system can simulate all the numerous risk factors in a more gentle way by the utilization of the triangular membership function that converts accurate clinical data into a fuzzy format. A clear rule base, got from the knowledge maps in the clinical field, is put together from the input data to determining the GDM risk levels. This is neither then defuzzied via the centroid method to generate a determinable score. Results show that this system can be used along with the experts, thus yielding the same results as them, meanwhile, it is capable of providing an intelligible output. It lends further credence to the role of fuzzy logic in uncertainties in medical data and their potential by decision support systems in clinical context. While reasonably beneficial results from the system are reported, the static nature of the membership functions implies, for the future, adaptability in methodologies for relative accuracy. This study still emphasizes that fuzzy logic finds application in settings of clinical delineation, which are distinctly more complex for their intended usefulness for conditions such as GDM here. © 2025 IEEE.

关键词： Risk assessment

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