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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2023年第1期85卷 53-64页

作者： Sohrabi, S. Moradinejad, A. Asgharian, A. Department of Mathematics Faculty of Science Urmia University Urmia57561-51818 Iran Faculty of Engineering Urmia University Urmia Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering Urmia University Urmia57561-51818 Iran

This paper presents a new and efficient tree data structure for sorting and collision detection of disks in 2D based on a new tree-based data structure, called hexatree, which is introduced for the first time in this paper. The data structure relies on mapping the objects to a space 1 dimension higher and then doing an appropriate range query in that range which is 3D range queries with circular cones of fixed direction and opening angle. Once the tree is constructed in O(n log n) time, disk collision detection queries can be answered in time O(n0.61). The proposed algorithm, in principle, is a dynamic structure;that is, it’s a structure that can be modified once it’s built. The proposed algorithm can be used for designing CAD softwares and other issues that require searching for a disk among a multitude of 2D disks. © 2023, Politechnica University of Bucharest. All rights reserved.

关键词： computer aided design

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Detection of Diabetic Retinopathy from Retinal Images Using DenseNet Models

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computer Systems science & engineering 2023年第4期45卷 279-292页

作者： R.Nandakumar P.Saranya Vijayakumar Ponnusamy Subhashree Hazra Antara Gupta Department of ECE K S R Institute for Engineering and TechnologyTiruchengode637215India Department of Computer Science and Engineering SRM Institute of Science and TechnologyChennai603203India Department of Electronics and Communication and Engineering SRM Institute of Science and TechnologyChennai603203India

A prevalent diabetic complication is Diabetic Retinopathy(DR),which can damage the retina’s veins,leading to a severe loss of *** treated in the early stage,it can help to prevent vision *** since its diagnosis takes time and there is a shortage of ophthalmologists,patients suffer vision loss even before ***,early detection of DR is the necessity of the *** primary purpose of the work is to apply the data fusion/feature fusion technique,which combines more than one relevant feature to predict diabetic retinopathy at an early stage with greater *** procedures for diabetic retinopathy analysis are fundamental in taking care of these *** profound learning for parallel characterization has accomplished high approval exactness’s,multi-stage order results are less noteworthy,especially during beginning phase *** Connected Convolutional Networks are suggested to detect of Diabetic Retinopathy on retinal *** presented model is trained on a Diabetic Retinopathy Dataset having 3,662 images given by *** results suggest that the training accuracy of 93.51%0.98 precision,0.98 recall and 0.98 F1-score has been achieved through the best one out of the three models in the proposed *** same model is tested on 550 images of the Kaggle 2015 dataset where the proposed model was able to detect No DR images with 96%accuracy,Mild DR images with 90%accuracy,Moderate DR images with 89%accuracy,Severe DR images with 87%accuracy and Proliferative DR images with 93%accuracy.

关键词： Convolutional Neural Networks vision loss pathogenic blood vessels DenseNet AlexNet ResNet

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A location-based fog computing optimization of energy management in smart buildings:DEVS modeling and design of connected objects

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Frontiers of computer science 2023年第2期17卷 179-195页

作者： Abdelfettah MAATOUG Ghalem BELALEM Saïd MAHMOUDI Computer Science Department Faculty of Exact and Applied SciencesUniversity of Oran 1 Ahmed Ben BellaOran 31000Algeria Science and Technology Department Faculty of Science and TechnologyUniversity of TIARETTiaret 14000Algeria Computer Science Department Faculty of EngineeringUniversity of MonsMons 7000Belgium

Nowadays,smart buildings rely on Internet of things(loT)technology derived from the cloud and fog computing paradigms to coordinate and collaborate between connected *** is characterized by low latency with a wider spread and geographically distributed nodes to support mobility,real-time interaction,and location-based *** provide optimum quality of user life in moderm buildings,we rely on a holistic Framework,designed in a way that decreases latency and improves energy saving and services efficiency with different *** EVent system Specification(DEVS)is a formalism used to describe simulation models in a modular *** this work,the sub-models of connected objects in the building are accurately and independently designed,and after installing them together,we easily get an integrated model which is subject to the fog computing *** results show that this new approach significantly,improves energy efficiency of buildings and reduces ***,with DEVS,we can easily add or remove sub-models to or from the overall model,allowing us to continually improve our designs.

关键词： smart building energy consumption IoT fog computing Framework DEVS simulation models

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Music genre classification and recognition using convolutional neural network

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Multimedia Tools and Applications 2025年第4期84卷 1845-1860页

作者： Narkhede, Nandkishor Mathur, Sumit Bhaskar, Anand Kalla, Mukesh Shah and Anchor Kutchhi Engineering College Mumbai Chembur India Department of Electronics and Communication Engineering Sir Padampat Singhania University Rajasthan Udaipur India Department of Computer Science Engineering Sir Padampat Singhania University Rajasthan Udaipur India

The music genre classification system is crucial to users in the digital music business since it allows them to be more effective. Music suggestion and availability to consumers is one of the most successful uses of genre classification. Songs may be easily accessible by users when the genre of the song is recognized, and music recommendations to users are made simple with an accurate categorization system in place. Furthermore, automated genre categorization is necessary to tackle difficulties such as finding similar songs, identifying cultures that would enjoy certain music, and conducting surveys. Machine learning approaches have recently been shown to be useful in a variety of classification tasks, including music genre categorization. As a result, this research investigates the use of Convolutional Neural Networks (CNN) for music genre categorization. For this study, a fresh dataset of 1000 traditional music from ten genres was employed. Content-based features, were retrieved from the songs in the dataset and used as input into the classifier, as feature extraction is critical to audio analysis. We got the results of the accuracy level of the system is 98.9% with a precision of 98.7%, recall of 98.5%, and f1 score of 97.5%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Power spectrum

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SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction

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SN computer science 2024年第6期5卷 698页

作者： Shetty, Sahana Mahesh, T.R. Department of Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore 562112 India

In agriculture, detecting plant diseases is crucial for optimal plant growth. Initially, input images are collected from three datasets: banana leaf spot diseases (BananaLSD) dataset, banana leaf dataset, and PSFD-Musa dataset. These collected images undergo filtering using the upgraded self-guided filtering (Up-SGF) model, chosen for its ability to preserve sharp edges effectively with strong smoothing, enhancing edge perception. Additionally, the improved Wiener convolution filter (IWCF) is applied to enhance image quality by reducing noise while preserving features. Following pre-processing, the images are segmented using spatial kernelized gravity-based density clustering (SKGDC) to identify disease-affected regions. Kaze, Blob, and histogram of oriented gradient (HOG) methods are utilized to extract relevant features: Kaze for scale-invariant key points, Blob for identifying and characterizing areas with shared color or intensity characteristics, and HOG for detecting edges and contours. These extracted features are then fused together using convolutional pyramid fusion (CPF). Finally, the hybrid channel attention-based YOLOv8 (HCA-YOLOv8) model is employed to predict diseases in banana fruit, stems, and leaves. Channel attention is incorporated into the backbone network architecture, and an improved deformable convolution replaces the normal convolution. Hyper parameters of the network model can be optimized using the improved chimp optimization (ICO) algorithm. Simulation results are obtained using the Python programming language. The proposed model attained 98.12% of accuracy in BananaLSD dataset, 99% of accuracy in banana leaf dataset, and 98.5% of accuracy in PSFD-Musa dataset. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Banana leaf disease Chimp optimization Clustering Pyramid fusion Self-guided filtering Wiener filter YOLOv8

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Night Vision Object Tracking System Using Correlation Aware LSTM-Based Modified Yolo Algorithm

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Intelligent Automation & Soft Computing 2023年第4期36卷 353-368页

作者： R.Anandha Murugan B.Sathyabama Department of Computer Science and Engineering K.L.N.College of Engineering630612TamilnaduIndia Department of Electronics and Communication Engineering Thiagarajar College of EngineeringMadurai625015TamilnaduIndia

Improved picture quality is critical to the effectiveness of object recog-nition and *** consistency of those photos is impacted by night-video systems because the contrast between high-proﬁle items and different atmospheric conditions,such as mist,fog,dust *** pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient *** recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance ***,the iden-tiﬁcation of objects in the night world also poses further problems because of the distorted backdrop and dim *** Correlation aware LSTM based YOLO(You Look Only Once)classiﬁer method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this *** order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerﬁlter for removing the noise in the *** process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set ***,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalﬁndings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mec

关键词： Object monitoring night vision image SSAN dataset adaptive internal linear embedding uplift linear discriminant analysis recurrent-phase level set segmentation correlation aware LSTM based yolo classiﬁer algorithm

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Unravelling the Complex Networks of Social Physics:Exploring Human Behavior,Big Data,and Distributed Systems

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Journal of Social Computing 2024年第2期5卷 165-179页

作者： Subrata Paul Dhritiman Mukherjee Anirban Mitra Anuradha Mitra Pushan Kumar Dutta the Department of Computer Science and Engineering Brainware UniversityKolkata 700125India. the Department of Computer Science and Engineering Amity University KolkataKolkata 700135India the Department of Management University of LiverpoolLiverpoolL697ZXUK the Department of Electronics and Communication Engineering Amity University KolkataKolkata 700135India

Networks form an integral part of our daily lives, comprising structures known as nodes connected via ties. These networks exhibit a complex interplay of random and structured features. We encounter networks regularly in our daily activities, whether it is logging into websites, commuting by train, going to work, or attending school. Networks are prevalent in various academic disciplines and practical applications, spanning biology, physics, engineering, social science, and numerous other fields. The early 21st century has witnessed a rapid expansion of techniques facilitating human interaction, significantly impacting behavior and social bonding in both urban and rural settings. The availability and accessibility of extensive population datasets have spurred ongoing research into understanding and shaping human inclinations and sociological phenomena in unconventional ways. This review delves into the nuanced aspects of social physics, exploring the rationale behind choosing this research topic, various conceptual frameworks, categorizations, and distinctions between machine learning and deep learning. The article briefly outlines the advantages of leveraging big data in social physics, highlighting its transformative potential. Lastly, the authors provide insights into several applications of social physics in subsequent sections, offering a glimpse into its real-world implications.

关键词： networks social physics machine learning deep learning social science

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Analytical Performance Comparison of IoT communication Protocols MQTT and CoAP from Security Perspective 3

Analytical Performance Comparison of IoT Communication Proto...

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3rd Odisha International Conference on Electrical Power engineering, communication and Computing Technology, ODICON 2024

作者： Swain, Satyaprakash Pattanayak, Binod Kumar Mohanty, Mihir Narayan Senapati, Chinmayee Department of Computer Science and Engineering Bhubaneswar India Department of Electronics and Communication Engineering Bhubaneswar India Department of Civil Engineering Bhubaneswar India

ISBN: (纸本)9798350354379

In this present scenario, world Internet is maximum based on H2H communication only. But in the principle of IoT, it reduces direct Human-Human and Human-Machine communication. IoT provides support to work 24 X 7 over the year without affecting the accuracy of result. IoT environment is fully based on sensors and actuators. Sensor is responsible for data collection as well as for the route of transmission and actuator is responsible for security of data, communication of data and for accuracy of data. From application point of view, now days all critical sectors like: health care sectors, defense sectors, Aviation sectors, Space sectors are strictly working on the application of IoT. Most of the work is there performed by their automated systems. If any error occurs at the time of data transmission, then that may cause a serious loss for the society. So, security mechanism is most vital part for all IoT systems. Though IoT is based on machine-to-machine communications without human intervention, we need to emphasize both on device structures and their protocols. Generally, IoT devices are very small in size and its embedded applications are less complexity. We need maximum attention if any modification is carried out in its structure. There are various types of IoT protocols available, out of them two most common protocols are CoAP and MQTT. Different security algorithms are used by these protocols for encryption and decryption of these data. Two most security algorithms are AES and ECC algorithm. The algorithm which takes low time for key generation is most suitable for IoT protocols. In this research, we are going to find out different Lou points of these two protocols. Then we will analyze two most common security algorithms i.e. AES and ECC key generation time using python codes and we will compare these times to its hybrid model key generation time. At last we have proposed a new security algorithm i.e. AES-ECC hybrid algorithm for IoT communication protocols.

关键词： Data communication systems

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SPECKLE NOISE DETECTION AND REMOVING BY MACHINE LEARNING ALGORITHMS IN MULTISENSORY IMAGES FOR 5G TRANSMISSION

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Scalable Computing 2024年第4期25卷 2679-2686页

作者： DHARANI, M. NAGENDRANATH, M.V.V.S. RAFEE, SHAIK MOHAMMAD KISHORE, G.NAVEEN VENKATAKRISHNAMOORTHY, T. Department of Electronics and Communiation Engineering Mohan Babu University Andhra Pradesh Tirupathi India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of AIML Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Electronics and Communication Engineering Andhra Pradesh Tadepalligudem India Department of Electronics and Communication Engineering Sasi Institute of Technology & Engineering AP Tadepalligudem India

The multispectral satellite sensor images have multibands, which have some typical noise. There is difficult to detect this tipical noise with low resolution image. The satellite local or gloval pixel information and quantificationare degraded due to this noise. Many standard transformations and filtering operations are developed for detection and removing of non-gaussion noise, which are not given sophisticated results with existing methods. These statistical characteristics are applied to those samples to identify and quantify present tipical noise. The higher-order statistical based machine learning algorithm is developing to remove the speckle noise from satellite image. In this proposed algorithm, implemented the higher order statistical approache such as skewness, kurtosis based adaptive curvelet methods are implemented for the detection and suppression of speckle noise with retrieve of spectral and spectral values. The proposed algorithm preserves smooth and sharp details and maintains the tradeoff level in multispectral bands is suitable for advanced high speed 5G communication with the effective rate of transmission. The proposed results are verified with suitable statistical parameters such as PSNR,Entropy and ERGAS values. © 2024 SCPE.

关键词： Learning algorithms

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A new family of q-Bernstein polynomials: probabilistic viewpoint

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Arab Journal of Basic and Applied sciences 2025年第1期32卷 42-50页

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

In this paper, we introduce a new class of polynomials, called probabilistic q-Bernstein polynomials, alongside their generating function. Assuming (Formula presented.) 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 (Formula presented.) 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 q-integral representations for probabilistic q-Bernstein polynomials. © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the University of Bahrain.

关键词： 05D40 11B73 11B83 distributions generating functions moment generating functions probabilistic q-Bernstein polynomials

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