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3rd International Multidisciplinary Information Technology and engineering Conference, IMITEC 2021

作者： Chikohora, Edmore Motimedi, Mabedi Namibia University of Science and Technology Informatics Department Windhoek Namibia Botswana Accountancy College Computer Systems Engineering Francistown Botswana

ISBN: (纸本)9781665417495

Nowadays a large number of mobile devices are in use and this has been exacerbated by the technologies embedded in them. The embedded technologies allow the devices to access and use Location-Based Services (LBS) as a way to improve service delivery by service providers. The issue of LBS privacy security comes in two ways which makes it complex. On one hand, for a user to receive LBS his/her location must be known to the service providers. While on the other hand, if this information falls in wrong hands it can be used in wrong ways and harm the respective users. This paper surveyed location privacy techniques developed by different authors and made a critical evaluation of the techniques to give recommendations on their use and for further studies. © 2021 IEEE.

关键词： Location based services

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Application of Artificial Intelligence in Malignant Tumors: A science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence Bioinformatics Cancer Genetic algorithms

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An Evolutionary Task Scheduling Algorithm Using Fuzzy Fitness Evaluation Method for Communication Satellite Network

arXiv

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arXiv 2024年

作者： Jiang, Xuemei Guo, Yangyang Zhang, Yue Song, Yanjie Pedrycz, Witold Xing, Lining School of Electronic Engineering Xidian University Xi’an China School of Systems Science Beijing Jiaotong University Beijing China School of Reliability and Systems Engineering Beihang University Beijing China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission in this network primarily relies on two modes: inter-satellite communication and satellite-to-ground station communication. The latter directly impacts the successful reception of data by users. However, due to resource and task limitations, finding a satisfactory solution poses a significant challenge. The communication satellite-ground station network scheduling problem (CS-GSNSP) aims to optimize CSN effectiveness by devising a plan that maximizes link construction time while considering constraints associated with satellite operation modes. The large number of tasks and numerous constraints in the problem result in a time-consuming evaluation of fitness function values. To address this issue, we propose a fuzzy fitness evaluation method (FFEM) that employs fuzzy or real evaluation methods based on individual similarity degrees. Additionally, we introduce an evolutionary algorithm based on FFEM, called evolutionary algorithm based on FFEM (FFEEA), for iteratively searching high-quality network construction schemes. In FFEEA, an adaptive crossover approach is used for efficient population search. Finally, extensive experiments are conducted to demonstrate that our proposed fuzzy fitness evaluation method and other improvement strategies significantly enhance satellite network service time. The study introduces a novel approach to enhance the efficiency of solving combinatorial optimization problems, such as CS-GSNSP, by mitigating the complexity associated with fitness evaluation. © 2024, CC BY-NC-ND.

关键词： Combinatorial optimization

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An Intelligent Face Detection by Corner Detection using Special Morphological Masking System and Fast Algorithm 2

An Intelligent Face Detection by Corner Detection using Spec...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Arulkumar, V. Jaya Prakash, S. Subramanian, E.K. Thangadurai, N. Sri Krishna College of Engineering and Technology Computer Science and Business Systems Coimbatore India Sir CR Reddy College of Engineering Department of Computer Science and Engineering Andhra Pradesh Eluru India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Electronic and Communication Engineering Faculty of Engineering and Technology !!!Centre For Emerging Technologies Karnataka India

ISBN: (纸本)9781665433686

This research study has applied facial recognition techniques using the angle detection algorithm. Also, a fast angle detection algorithm has been used here, but modified it by applying a shielding technique to create a technique related to loud noise. This article describes twelve facial signs that include the corner of the left eye, the corner of the right eye, the left eyebrow, the right eyebrow, the corner of the lip, and the nostril. It consists of two parts;first, a private browsing technique has been performed to filter the image from noise. The proposed method is based on the assumption that an image is available from the front (fully front). Skin areas were first detected using a color-based learning algorithm and six sigma techniques on RGB, HSV, and NTSC scales. Other analyzes involve morphological processing using the detection of the borderline and the detection of the reflection from the light source of the eye commonly referred to as the eye point. In the second step, a fast angle detection algorithm has been used to detect the placeholders on the face. The Fast Angle Finder works on the Angular Response Function (CRF) which is calculated as the minimum change in intensity in all possible directions. Finally, a comparison has been made with other filtering techniques based on the proposed protection techniques. This article has performed different experiments by using the IRIS Face Database, BioID, and the Cohn Canada Database. The recognition rate obtained by the proposed method is appreciable. © 2021 IEEE.

关键词： Face recognition

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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Congress on Evolutionary Computation

作者： Wenjing Hong Cheng Chen Zexuan Zhu Ke Tang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design.

关键词： Measurement Design methodology Evolutionary computation Search problems Linear programming Stability analysis Vaccines

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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A review of smartphones-based indoor positioning:Challenges and applications

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IET Cyber-systems and Robotics 2021年第1期3卷 1-30页

作者： Khuong An Nguyen Zhiyuan Luo Guang Li Chris Watkins School of Computing Engineering and MathematicsUniversity of BrightonBrightonUK Computer Science Department Royal HollowayUniversity of LondonEghamUK Institute of Cyber-Systems and Control Zhejiang UniversityHangzhouChina

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor *** article is dedicated to review the most recent and interesting smartphones-based indoor navigation systems,ranging from electromagnetic to inertia to visible light ones,with an emphasis on their unique challenges and potential realworld applications.A taxonomy of smartphone sensors will be introduced,which serves as the basis to categorise different positioning systems for reviewing.A set of criteria to be used for the evaluation purpose will be *** each sensor category,the most recent,interesting,and practical systems will be examined,with detailed discussion on the open research questions for the academics,and the practicality for the potential clients.

关键词： client smart taxonomy

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Incremental Composition of Learned Control Barrier Functions in Unknown Environments

arXiv

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arXiv 2024年

作者： Lutkus, Paul Anantharaman, Deepika Tu, Stephen Lindemann, Lars Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

We consider the problem of safely exploring a static and unknown environment while learning valid control barrier functions (CBFs) from sensor data. Existing works either assume known environments, target specific dynamics models, or use a-priori valid CBFs, and thus provide limited safety guarantees for general control-affine systems during exploration. We present a method for safely exploring by incrementally composing a global CBF from local CBFs. The challenge here is that local CBFs may not have well-defined end behavior outside their training domain, i.e. local CBFs may be positive (indicating safety) in regions where no training data is available. We show that well-defined end behavior can be obtained when local CBFs are parameterized by compactly-supported radial basis functions. For learning local CBFs, we collect sensor data, e.g. LiDAR capturing obstacles in the environment, and augment it with simulated data from a safe oracle controller. Our work complements recent efforts to learn CBFs from safe demonstrations, where learned safe sets are limited to their training domains, by demonstrating how to grow the safe set over time as more data becomes available. We evaluate our approach on two simulated systems, where our method successfully explores an unknown environment while maintaining safety throughout the entire execution. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

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How to Swarm: Entropy-Based Model Parameter Analysis

How to Swarm: Entropy-Based Model Parameter Analysis

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IEEE Southeastcon

作者： Nathan Rayon Stefan Wheeless Gabriel Broom Hakki Erhan Sevil Department of Computer Science University of West Florida (UWF) Pensacola FL USA Department of Electrical and Computer Engineering University of West Florida (UWF) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida (UWF) Pensacola FL USA

In this study, we implemented our entropy-based swarm model to an autonomous waypoint navigation application for a group of multi-rotor Unmanned Aerial Vehicles (UAVs) through a set course in free space. Multi-UAVs of multiple group sizes were run with variations in parameters, and the path lengths traveled were measured to determine the most efficient configurations, and we investigated the impact of varying parameters on the swarm behavior performance. The simulation of the UAV kinematics and environment was performed in AirSim. The results show that the swarm model with different parameter setup operates successfully and the effects of the parameter selection on our multi-UAV swarm model are discussed.

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Automated Classification of Medical Images Using Convolutional Neural Networks

Automated Classification of Medical Images Using Convolution...

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International Conference on Computing and Networking Technology (ICCNT)

作者： N. Muthuvairavan Pillai S Manimala Aruna Sri Rongali Anchit Gugnani A Senthil Kumar G. V. Sriramakrishnan Computer Science and Business Systems R.M.D Engineering College Kavarapettai Thiruvallur district Computer science and engineering University SJCE JSS Science and technology University Mysore Karnataka India Department Information Technology Emphasis/Information Technology Ph.D University and city with the country University of The Cumberlands US Physiotherapy Amity University Jaipur Rajasthan Artificial Intelligent department Shri Vishnu Engineering College for Women Bhimavaram AP Department Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort.

关键词： Training Image segmentation Accuracy Convolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging X-ray imaging Image classification

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