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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Pragathi, Jetti Sagar, D. M. S. Ramaiah University of Applied Sciences Computer Science Engineering Department Bengaluru560058 India

ISBN: (纸本)9798350394474

Customer segmentation is a key strategy in marketing and business that aims to divide a customer base into distinct groups based on common characteristics, preferences, and behaviors. This process enables businesses to tailor their marketing strategies, products and services to specific customer segments, increase customer satisfaction and maximize business results. In this context, the use of clustering algorithms such as k-means clustering, mini-batch clustering, and hierarchical clustering becomes essential to effectively categorize customers into meaningful groups. These algorithms provide a data-driven approach to uncovering patterns within large datasets, offering valuable insights for targeted marketing and personalized customer *** segmentation using k-means clustering involves data preparation, feature selection, and normalization. The optimal number of clusters (k) is determined using techniques such as the elbow method. The scikit-learn library in Python is commonly used to implement k-means. After fitting the model to the data, each customer is assigned to a cluster based on their characteristics. Visualizations such as scatter plots help interpret the results. The analysis of cluster characteristics and labeling of segments based on key features is crucial. The insights gained from segmentation can be used to adjust marketing strategies and improve the customer experience. This process allows businesses to understand different groups of customers, which improves targeted approaches and decision-making. © 2024 IEEE.

关键词： K-means clustering

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Augmented electric eel foraging optimization algorithm for feature selection with high-dimensional biological and medical diagnosis

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Neural Computing and Applications 2024年第35期36卷 22171-22221页

作者： Al-Betar, Mohammed Azmi Braik, Malik Sh. Mohamed, Elfadil A. Awadallah, Mohammed A. Nasor, Mohamed College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Science Al-Balqa Applied University Al-Salt19117 Jordan Department of Computer Science Al-Aqsa University P.O. Box 4051 Gaza Palestine Ajman University Ajman United Arab Emirates

This paper explores the importance of the electric eel foraging optimization (EEFO) algorithm in addressing feature selection (FS) problems, with the aim of ameliorating the practical benefit of FS in real-world applications. The use of EEFO to solve FS problems props our goal of providing clean and useful datasets that provide robust effectiveness for use in classification and clustering tasks. High-dimensional feature selection problems (HFSPs) are more common nowadays yet intricate where they contain a large number of features. Hence, the vast number of features in them should be carefully selected in order to determine the optimal subset of features. As the basic EEFO algorithm experiences premature convergence, there is a need to enhance its global and local search capabilities when applied in the field of FS. In order to tackle such issues, a binary augmented EEFO (BAEEFO) algorithm was developed and proposed for HFSPs. The following strategies were integrated into the mathematical model of the original EEFO algorithm to create BAEEFO: (1) resting behavior with nonlinear coefficient;(2) weight coefficient and confidence effect in the hunting process;(3) spiral search strategy;and (4) Gaussian mutation and random perturbations when the algorithm update is stagnant. Experimental findings confirm the effectiveness of the proposed BAEEFO method on 23 HFSPs gathered from the UCI repository, recording up to a 10% accuracy increment over the basic BEEFO algorithm. In most test cases, BAEEFO outperformed its competitors in classification accuracy rates and outperformed BEEFO in 90% of the datasets used. Thereby, BAEEFO has demonstrated strong competitiveness in terms of fitness scores and classification accuracy. When compared to its competitors, BAEEFO produced superior reduction rates with the fewest number of features selected. The findings in this research underscore the critical need for FS to combat the curse of dimensionality concerns and find highly useful fea

关键词： Heuristic algorithms

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Explorative Study on Motor Interference During Synchronous Human and Robot Arm Movements Under Varied Presence of a Robot Head

Explorative Study on Motor Interference During Synchronous H...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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DIFFRACTION EFFECTS IN A SOUND BEAM PROPAGATING THROUGH A NON-UNIFORM FLOWING FLUID

DIFFRACTION EFFECTS IN A SOUND BEAM PROPAGATING THROUGH A NO...

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2024 International Conference on Underwater Acoustics, ICUA 2024

作者： Tchatat Ngaha, D. Frøysa, K.-E. Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5063 Norway

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A Comprehensive Review of Bio-Medical Portable Devices for Blood Coagulation Detection Device 2

A Comprehensive Review of Bio-Medical Portable Devices for B...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bore, Joy Dhale, Trishul Kumar, Praveen Deshmukh, Gauri S. Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Meghe Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Department of Applied Chemistry Nagpur India

ISBN: (纸本)9798331528713

The paper provides a synoptic view of portable biomedical point-of-care devices for blood coagulation detection, emphasising the state-of-the-art technology adopted and its use in the medical industry. These devices give fast and precise results at the point of care and are indispensable for diagnosing coagulation and perceiving thrombotic states. It brought into focus the issues of monitoring blood coagulation across different areas of practice, such as the ICU, operative and perioperative services, and ambulatory care. This paper also highlights new trends in the development of sensors, miniaturisation and the integration of digital health solutions. Further, it explains how these devices are advantageous and disadvantageous in terms of speed, sample requirement, standardisation, calibration, and user-friendliness. Last but not least, the regulation and market uptake issue is discussed, focusing on how portable coagulation detection devices can revolutionise patient care and access to healthcare. Finally, the authors state that the paper should inspire further research to develop new solutions and collaboration to address obstacles and underpin advancements in the field. © 2024 IEEE.

关键词： Portable equipment

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Improved Bounds on the Size of Permutation codes under Kendall τ-metric

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IEEE Transactions on Information Theory 2025年第6期71卷 4156-4166页

作者： Parvaresh, Farzad Sobhani, Reza Abdollahi, Alireza Bagherian, Javad Jafari, Fatemeh Khatami, Maryam University of Isfahan Faculty of Engineering Department of Electrical Engineering Isfahan Iran School of Mathematics Tehran19395-5746 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Applied Mathematics and Computer Science Isfahan81746-73441 Iran University of Isfahan Faculty of Mathematics and Statistics Department of Pure Mathematics Isfahan81746-73441 Iran

In order to overcome the challenges caused by flash memories and also to protect against errors related to reading information stored in DNA molecules in the shotgun sequencing method, the rank modulation method has been proposed. In the rank modulation framework, codewords are permutations. In this paper, we study the largest size P(n, d) of permutation codes of length n, i.e., subsets of the set Sn of all permutations on {1, ..., n} with the minimum distance at least (Formula presented) under the Kendall τ-metric. By presenting an algorithm and two theorems, we improve the known lower and upper bounds for P(n, d). In particular, we show that P(n, d) = 4 for all n ≥ 6 and (Formula presented). Additionally, we prove that for any prime number n and integer (Formula presented). This result greatly improves the upper bound of P(n, 3) for all primes n ≥ 37. © 1963-2012 IEEE.

关键词： Flash memory

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Frequency Generation for a Quantum Current Sensor in Smart Grids 26

Frequency Generation for a Quantum Current Sensor in Smart G...

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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Hoffmann, Frederik Bolter, Ann-Sophie Horsthemke, Ludwig Stiegekotter, Dennis Pogorzelski, Jens Glosekotter, Peter Gregor, Markus FH Münster - University of Applied Sciences Department of Electrical Engineering and Computer Science Steinfurt48565 Germany FH Münster - University of Applied Sciences Department of Engineering Physics Steinfurt48565 Germany

ISBN: (纸本)9798331532833

Integrating renewable energy sources, electric vehicles, and storage systems into power grids demands advanced control and monitoring systems. Precise current sensors are a critical component of these systems, essential for ensuring reliable and efficient electricity distribution. Quantum sensors, particularly those based on nitrogen vacancy (NV) centers in diamond, provide a promising solution for high-precision current measurement. This document introduces an innovative method for generating microwave signals using direct digital synthesis (DDS) and field-programmable gate arrays (FPGAs) to drive multiple signals. © 2024 IEEE.

关键词： Power grids

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ECDSA-based tamper detection in medical data using a watermarking technique

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 78-87页

作者： Ch, Rupa Vivek K, Naga Srivastava, Gautam Reddy Gadekallu Department CSE VR Siddhartha Engineering College Andhra Pradesh Vijayawada520 007 India Dept of Math and Computer Science Brandon University MB Canada Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon Centre of Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara Punjab Rajpura140401 India Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon Lovely Professional University Division of Research and development Phagwara India Jiaxing University College of Information Science and Engineering Jiaxing China

Telemedicine is a form of healthcare delivery that employs communication technology to provide medical care to patients remotely. The use of telemedicine has seen a significant increase in recent years, presenting challenges such as patient privacy, data security, the need for reliable communication technology, and the potential for misdiagnosis without a physical examination. Digital Watermarking can assist in addressing such issues by incorporating a unique identifier into an image that can be used to authenticate its validity. To tackle these issues, this study proposes a robust digital watermarking approach tailored to brain medical images, combining hashing, the Elliptic Curve Digital Signature Algorithm (ECDSA), and the Integer Wavelet Transform-Discrete Cosine Transform (IWT-DCT). This method utilizes the Secure Hash Algorithm (SHA-256) to first segment the brain's Region of Interest (RoI). Subsequently, the hashed RoI, along with an ECDSA signature, is embedded into the high-frequency sub-bands of the medical image using IWT-DCT. The embedding process strategically alters the coefficients of the high-frequency sub-bands to accommodate the signature while minimizing perceptual distortion. The technique leverages the robustness of transformed-domain image watermarking techniques against various attacks and combines it with SHA-256 for integrity and ECDSA for authentication purposes. The results demonstrate that the suggested approach is robust to a variety of image processing techniques, including noise addition, filtering, and compression while maintaining high levels of imperceptibility. Key metrics such as the Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Structural Similarity Index (SSIM) were used to evaluate performance. The suggested strategy exhibited a substantial improvement over existing methods. The PSNR increased to 68.67, indicating higher image quality, while the MSE reduced to 0.96, demonstrating closer pixel values to the or

关键词： Mean square error

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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An Overview of Moving Object Detection Using YOLO Deep Learning Models 2

An Overview of Moving Object Detection Using YOLO Deep Learn...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Dwivedi, Upendra Joshi, Kireet Shukla, Surendra Kumar Rajawat, Anand Singh G L Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India SVKM S Nmims Mpstme Department of Computer Science and Engineering Shirpur Campus Shirpur India School of Computer Sciences and Engineering Sandip University Nashik India

ISBN: (纸本)9798350371055

computer vision is a promising domain that focuses on emerging approaches, algorithms and technologies to provide computing capability to machine to analysis visual data, such as image files, videos files and real time video streaming. In computer Vision and image processing detecting object from images and videos has been topic of extensive research. This is accomplished by applying various computer vision techniques to analyze the visual data and determine class of objects from image and videos files. One widespread approach to object detection is using the deep learning models. YOLO (You Only Look Once) is a convolutional neural network that offers a fast and efficient solution to object detection using deep learning. It enable computer to handle real-time detection of objects in video frames and accurately locate and classify moving objects. YOLO is able to simultaneously detect and classify objects in an efficient manner using convolutional neural networks. © 2024 IEEE.

关键词： Deep Learning Image Processing Motion Detection Moving Object Detection YOLO

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