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Recommending Public Transit Route: Ant-Colony Optimization or Dijkstra?

Recommending Public Transit Route: Ant-Colony Optimization o...

2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Jabbar, Abdul Pinandito, Aryo Universitas Brawijaya Faculty of Computer Science Informatics Engineering Department Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia

ISBN: (纸本)9798350369359

Advances in algorithmic studies have been made recently, especially in the area of finding the shortest path. Despite taking a greedy approach, the Dijkstra algorithm is well recognized for its efficacy and has established itself as the industry standard for identifying the shortest path in a graph. Conversely, scientists have embraced the Ant-Colony Optimization (ACO) approach, which draws inspiration from the behavior of ants. This study examines the two methods and determines if, given equivalent resources, ACO can generate Dijkstra-like recommendations. Research data is subjected to both algorithms, and the outcomes are presented visually. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. In data measurement, Dijkstra is processed first to determine the best path, and then the ACO method is applied to produce findings that are similar to Dijkstra. Memory consumption, computation time, initial memory allocation, and final memory allocation are evaluation parameters. ACO is more efficient than Dijkstra in terms of execution time and number of pathways traversed, according to the study's repeated evaluation methodology. This iterative assessment procedure is carried out to address the possibility of single measurements resulting from the JavaScript Garbage Collector. The study's findings imply that a larger memory may be necessary for ACO to deliver the desired outcomes. These results could also be impacted by each algorithm's distinct qualities. © 2023 IEEE.

关键词： Ant colony optimization

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Trends in the detection and interpretation of human movements and mime-gesture 17

Trends in the detection and interpretation of human movement...

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17th International Conference on Development and Application Systems, DAS 2024

作者： Enachi, Andrei Turcu, Cornel Culea, George Faculty of Electrical Engineering and Computer Science Ștefan cel Mare University of Suceava Suceava720229 Romania Faculty of Energetics and Computer Science Vasile Alecsandri University of Bacău Bacău600115 Romania

ISBN: (纸本)9798350349290

Communication is an important part of our lives. Communication facilitates interaction between people, allowing us to share our ideas and emotions. The research has focused mainly on one side of communication, namely the conversion of sign language into speech. Contrary to traditional approaches, which focus on using static data to convert sign language into speech, our research brings an innovative perspective by integrating artificial intelligence technologies. This research has largely used static data sets such as alphabets, digits and specific gestures taken from a variety of languages. However, addressing this problem is done using artificial intelligence techniques and methods. In this context, detailed research will be presented, focusing on techniques and methods developed to improve the communication using artificial intelligence (Mediapipe, OpenCV, CNN). The contributions presented in this paper represent a significant advance in the field of communication bringing to the forefront cutting-edge methods and techniques in an evolving field. ©2024 IEEE.

关键词： Data handling

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Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

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computers, Materials & Continua 2022年第7期72卷 775-796页

作者： Zeyad Ghaleb Al-Mekhlafi Ebrahim Mohammed Senan Taha H.Rassem Badiea Abdulkarem Mohammed Nasrin M.Makbol Adwan Alownie Alanazi Tariq S.Almurayziq Fuad A.Ghaleb Department of Information and Computer Science College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia Department of Computer Science&Information Technology Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad431004India Faculty of Science and Technology Bournemouth UniversityPooleBH125BBUK Department of Computer Engineering College of Computer Science and EngineeringUniversity of Ha’ilHa’il81481Saudi Arabia College of Computer Science and Engineering Hodeidah UniversityHodeidah966Yemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaJohor BahruJohor81310Malaysia

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart *** this work,a dataset containing medical,physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning,deep learning and a hybrid technique between deep learning and machine learning on theMagnetic Resonance Imaging(MRI)dataset for cerebral *** the first dataset(medical records),two features,namely,diabetes and obesity,were created on the basis of the values of the corresponding *** t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data ***,the Recursive Feature Elimination algorithm(RFE)was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant *** features are fed into the various classification algorithms,namely,Support Vector Machine(SVM),K Nearest Neighbours(KNN),Decision Tree,Random Forest,and Multilayer *** algorithms achieved superior *** Random Forest algorithm achieved the best performance amongst the algorithms;it reached an overall accuracy of 99%.This algorithm classified stroke cases with Precision,Recall and F1 score of 98%,100%and 99%,*** the second dataset,the MRI image dataset was evaluated by using the AlexNet model and AlexNet+SVM hybrid *** hybrid model AlexNet+SVM performed is better than the AlexNet model;it reached accuracy,sensitivity,specificity and Area Under the Curve(AUC)of 99.9%,100%,99.80%and 99.86%,respectively.

关键词： Stroke cerebral haemorrhage deep learning machine learning t-SNE and RFE algorithms

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Computational Investigation of Multiband EMNZ Metamaterial Absorber for Terahertz Applications

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computers, Materials & Continua 2022年第5期71卷 3905-3920页

作者： Ismail Hossain Md Samsuzzaman Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mandeep Singh Jit Singh Mohammad Tariqul Islam Space Science Center Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaMalaysia

This study presents an Epsilon Mu near-zero(EMNZ)nanostructured metamaterial absorber(NMMA)for visible regime *** resonator and dielectric layers are made of tungsten(W)and quartz(fused),where the working band is expanded by changing the resonator layer’s *** to perfect impedance matching with plasmonic resonance characteristics,the proposed NMMA structure is achieved an excellent absorption of 99.99%at 571 THz,99.50%at 488.26 THz,and 99.32%at 598 THz *** absorption mechanism is demonstrated by the theory of impedance,electric field,and power loss density distributions,*** geometric parameters are explored and analyzed to show the structure’s performance,and a near-field pattern is used to explain the absorption mechanism at the resonance frequency *** numerical analysis method describes that the proposed structure exhibited more than 80%absorbability between 550 and 900 *** computer Simulation Technology(CST Microwave Studio 2019)software is used to design the proposed ***,CSTHFSS interference is validated by the simulation data with the help of the finite element method(FEM).The proposed NMMA structure is also exhibits glucose concentration sensing capability as *** the proposed broadband absorber may have a potential application in THz sensing,imaging(MRI,thermal,color),solar energy harvesting,light modulators,and optoelectronic devices.

关键词： Metamaterial absorber terahertz applications multiband EM near zero nanostructured visible regime applications

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A Few-Shot Learning Approach for Sound Source Distance Estimation Using Relation Networks 23

A Few-Shot Learning Approach for Sound Source Distance Estim...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Sobhdel, Amirreza Razavi-Far, Roozbeh Palade, Vasile Urmia University Dept. of Electrical and Computer Engineering Iran University of New Brunswick Faculty of Computer Science Canada Centre for Computational Science Coventry University United Kingdom

ISBN: (纸本)9798350374889

In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem of Sound Source Distance Estimation (SSDE). In previous research on deep supervised SSDE, low accuracies have often resulted from the mismatch between the training data (from known environments) and the test data (from unknown environments). By performing comparative experiments on a sufficient amount of data, we show that the few-shot relation network outperforms other competitors including eXtreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and MultiLayer Perceptron (MLP). Hence it is possible to calibrate a microphone-equipped system, with a few labeled samples of audio recorded in a particular unknown environment to adjust and generalize our classifier to the possible input data and gain higher accuracies. © 2024 IEEE.

关键词： Self-supervised learning

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A High Gain Wide Band Compact Size Dual Band Patch Antenna for 5G Applications

A High Gain Wide Band Compact Size Dual Band Patch Antenna f...

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2023 IEEE International Conference on Emerging Trends in engineering, sciences and Technology, ICES and T 2023

作者： Azam, Tahir Tahir, Farooq Ahmed Sarwar, Asima Qayyum, Muhammad Aqib National University of Science and Technology School of Electrical Engineering and Computer Science Islamabad Pakistan GIK Institute of Engineering Sciences and Technology Faculty of Computer Science and Engineering Topi Swabi Pakistan

ISBN: (纸本)9781665455602

This paper presents a high gain, compact size and dual band rectangular patch antenna for 5G applications. To enhance the gain of antenna, an equilateral triangle slots on the upper rectangular patch are constructed. The broadband antenna is operating at 27.4 GHz to 35.5 GHz and 38GHz to 41.8GHz having fractional bandwidth of 25.7% and 9% respectively. The maximum gain of 8.19 dBi is observed at 28 GHz and the antenna has radiation efficiency greater than 90% over the entire band. Moreover, the Voltage Standing Wave Ratio (VSWR) is lower than 2 dB at operational bands. © 2023 IEEE.

关键词： Radiation efficiency

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A Comprehensive Review for Optimizing the Design of Cluster Head Selection to Enhance the Network Lifetime in WSN

A Comprehensive Review for Optimizing the Design of Cluster ...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Bhuvaneswari, S. Abirami, A. Suvetha Sri, R.P. Srinithy, R. Swathi, R. Easwari Engineering College Faculty of Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665492942

Large numbers of cooperative sensor nodes make up wireless sensor networks (WSNs). Sensors are the integral component in WSN IoT. Sensors separate and organize in the clusters by similar characteristics. A cluster head is in charge of gathering information from the nodes in every cluster and transmitting it to its base station (BS). Because every node are powered by batteries, lessens energy usage and elongating lifetime of network are crucial concerns for WSN. The primary goals of this analysis are to address issues that previous writers have not addressed and to provide beginning researchers with a fundamental understanding of how to choose cluster head (CH) based on various methodologies. To designate a node as CH, some enhancing parameters are chosen, such as residual energy, packet transmission rate, throughput, distance between BS and Cluster Node (CN), and betweenness centrality. Data packets are transferred using the optimized protocol to reduce delay time and increase transmission rate after many CH are chosen. Analysis of the many literatures was done. An Optimization Algorithm is needed to enhance network lifetime from this analysis. A comparison of various algorithm based on the features and difficulties from the analysis is done. There are many drawbacks, such as a cluster head is chosen at random, data transmission rate is not considered, no proper formation of clusters, importance for load and temperature is not given, stability is not taken into account. Thus, there is a need for optimized algorithm to overcome all the mentioned issues. © 2023 IEEE.

关键词： Packet networks

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A Critical Analysis for Detection of Parkinson's Disease Based on Voice Signals

A Critical Analysis for Detection of Parkinson's Disease Bas...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Kaur, Prabhjot Gupta, Meenu Kumar, Rakesh Obaid, Ahmed J. Chandigarh University Department Of Computer Science & Engineering Punjab 140413 India University Of Kufa Faculty Of Computer Science And Mathematics Najaf Iraq National University Of Science And Technology Dhi Qar Iraq

ISBN: (纸本)9798350306446

Parkinson's disease (PD) is a neurological condition that results in a variety of motor and non-motor symptoms. It is caused due to degeneration of nerve cells in the central nervous system. The motor symptoms include posture instability, gait disorder, bradykinesia and tremors etc. while non-motor symptoms are related to dysphonia, sleep disorder etc. PD is an incurable disease at present. Therefore, an early detection and treatment is required to improve life conditions and is motivation for the research. Various research analyzed motor symptoms to detect PD. However, motor symptoms become visible in late stages. Speech impairment is a symptom which is visible in early stages of PD and therefore remains an open area for research. The state-of-art has utilized various Machine Learning (ML), Deep Learning (DL) and Generative Adversarial Network (GAN) techniques to detect PD in patients. This paper presents a rigorous study of works done between 2018-2023. 14 papers based on detection of PD have been chosen and an analysis of their methodology, limitations and results has been done. Various DL models outperformed the ML models in terms of accuracy but do have certain limitations including imbalanced and noisy datasets and the model's interpretability. The paper also tries to explore the possibilities of implementation of GAN to improve dataset and proposes a model for PD classification which achieved an accuracy of 94.06. In summary, this study aims to provide an overview of research conducted in the field of speech signal-based PD detection and provides a valuable insight for future research aimed at improving PD detection methods. © 2024 IEEE.

关键词： Generative adversarial networks

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Skin Cancer Detection using Machine Learning Framework with Mobile Application 7

Skin Cancer Detection using Machine Learning Framework with ...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Emam Ananna, Mariam Nayeem, Jannatul Jahangir Alam, Mohammad Islam, Saiful Daffodil International University Computer Science and Engineering Faculty of Science and Information Technology Bangladesh

ISBN: (纸本)9798350397284

A notable increase in skin cancer mortality, one of the most lethal kinds of cancer, has been caused by a lack of awareness of warning signals and preventative measures. The need for early skin cancer diagnosis has increased because of the fast development rate of melanoma skin cancer, its high cost of treatment, and its mortality risk. Treatment of cancer cells usually requires perseverance and manual identification. This study recommends using image synthesis and machine learning techniques to develop a system for diagnosing skin cancer. Thermoscopic pictures are the input for the pre-processing phase. Following the segmentation of the thermoscopic images, the attributes of the injured skin cells are obtained using a feature extraction approach. Utilizing a convolutional neural network classifier with deep learning, the collected characteristics are stratified. An accuracy of 89% has been discovered using the publicly available dataset. © 2023 IEEE.

关键词： Feature extraction

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Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

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computer Systems science & engineering 2023年第9期46卷 2651-2666页

作者： A.M.Hafiz M.Hassaballah Abdullah Alqahtani Shtwai Alsubai Mohamed Abdel Hameed Department of Electronics&Communication Engineering Institute of TechnologyUniversity of KashmirSrinagarJ&K190006India Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkhrjKSA Department of Computer Science Faculty of Computers and InformationLuxor UniversityLuxorEgypt

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world *** the scope of this area,various techniques are found in the *** such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each *** learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully *** more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance *** a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL *** uses multiple binary action DQNs having shared state and *** benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based *** approach can potentially be extended to any type of DQN by forming its *** extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent *** proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

关键词： Deep Q-networks ensemble learning reinforcement learning OpenAI Gym environments

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