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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nikhil, Shreyas Sah, Rahul Kumar Kumar Parki, Santosh Tamang, Til Bikram Somashekhara Reddy, D. Mahesh, T.R. Faculty of Engineering and Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Stock price prediction has always been a tough task for all the stakeholders involved. This paper focusses on four different models, namely LSTM, CNN, LSTM-CNN, and Genetic Algorithm-Assisted LSTM-CNN (GA-LSTM-CNN) for stock price prediction. The day-to-day stock prices of a certain company for a period of more than 10 years make up the dataset utilized in this study. Root mean square error (RMSE) has been utilized as performance metric for each model. Outcomes of our experiment show that the GA-LSTM-CNN model performed better than other models with an RMSE value of 0.0064 upon training and an RMSE value of 0.03 upon testing. The GA-LSTM-CNN model combines the strengths of LSTM as well as CNN models with the help of a genetic algorithm, which helps in optimizing the hyperparameters. According to the results, the GA-LSTM-CNN model can be very helpful for the stakeholders in predicting future stock values. © 2023 IEEE.

关键词： Genetic algorithms

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Enhanced Unimodal Continuous Authentication Architecture on Smartphones for User Identification through Behavioral Biometrics 2

Enhanced Unimodal Continuous Authentication Architecture on ...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rayani, Praveen Kumar Changder, Suvamoy National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350347982

Nowadays intruders are smart and intelligent to initiate various attacks on the smartphone's entry-point au- thentication methods to access the user's stored information on the smartphone. Accessing users' confidential information rather than personal information by attackers can create a monetary or non-monetary gain. Thus, continuous authentication was proposed on smartphones to recognize the user from the entry point to the end of the session. Even though, we noticed that earlier works on unimodal-based continuous authentication were limited to specific applications on smartphones. This can create a vulnerable gap to access the users' information on the smartphone. Thus, we propose an enhanced unimodal-based continuous authentication architecture for user identification on the smartphone. The proposed approach instantaneously chooses the unimodal behavioral biometrics based on the foreground application of the device and performs the user identification through a machine learning model. The experimental results pro- posed approach show better results than the existing literature. © 2023 IEEE.

关键词： Smartphones

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Tourist Place Classification Using Topic Modeling Algorithms 56

Tourist Place Classification Using Topic Modeling Algorithms

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56th International Carnahan Conference on Security technology, ICCST 2023

作者： Wadhe, Apeksha Arun Suratkar, Shraddha S. Vjti Mumbai Dept. of Computer Engineering & Information Technology 400019 India

ISBN: (纸本)9798350315875

Today social media is one of the most powerful and popular tool which is expanding rapidly in the world. Many tourism websites publish tourist places data for describing places in the form of Description. Over this data, Analysis can be performed and meaningful insights can be drawn. By analyzing this data we can find the subject or topic discussed in Description. By performing topic modeling over these data we can predict the category of tourist place. Also topic Modeling can help to cluster textual content into groups as per the subject of the text. In research study, Comparative analysis of topic modeling algorithms has been performed which will find a category of tourist places. Tourist place description blog data was gathered from tourism websites. Using tourist place Description dataset, comparative study of Topic modeling algorithms i.e. LDA Variational Inference(VI) algorithm and LDA Gibbs Sampling(GS) algorithm has been performed. Results of experiments has been analyzed with performance evaluation using various factors such as coherence score, perplexity, training execution time and accuracy percentage. LDA Gibbs Sampling(GS) algorithm outperforms LDA Variational Inference (VI) algorithm in terms of coherence score, perplexity, accuracy percentage. Whereas, LDA Variational Inference(VI) algorithm requires less execution time as compared to the LDA Gibbs Sampling(GS) algorithm © 2023 IEEE.

关键词： Websites

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Efficiency Measurement of k-Partitioning of Mobile WSN 4

Efficiency Measurement of k-Partitioning of Mobile WSN

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Hossain, Azad Rehena, Zeenat Murshidabad Institute of Technology Lecturer in Computer Science and Technology Berhampore India Aliah University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350306118

Network partitioning improves the energy efficiency of the network. In this work, the algorithm (KP-MWSN) can determine the no. of partitions (k) required based on the delay tolerance limit of each node of MWSN. It can be fruitful for the events where a small amount of delay can be accepted but efficiency has not been calculated in respect of a metric. So, we have measured the efficiency of KP-MWSN algorithm and the results are compared with the Topology Partitioning Scheme (TPS). The simulation result shows that in every randomly created event the efficiency of KP-MWSN is always better than other related state-of-The-Art work. © 2023 IEEE.

关键词： Energy efficiency

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Detection of Cataracts from Retinal Fundus Image Using Ensemble Machine Learning Approach

Detection of Cataracts from Retinal Fundus Image Using Ensem...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Rajshahi University of Engineering and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350316001

Cataract is a common eye condition that causes clouding of the eye's natural lens, resulting in blurry vision and it is very common in older ages people. However, it can also occur in younger aged people due to various genetical effects, diabetes, smoking, and prolonged exposure to sunlight. Cataracts can be treated through surgery to remove the clouded lens and replaced by an artificial one, which can significantly improve the vision. This problem can be reduced through early detection, regular eye examinations etc. In this research, a Contrast Limited Histogram Equalization (CLAHE) of retinal fundus images is applied for a better presentation of the above cataract effects. These improved images are fed to a machine learning-based model for effective detection. Finally, the ensembling of the proposed machine learning-based classifiers is performed for the accurate detection of cataracts. The proposed model was tested experimentally on a real dataset and achieves excellent performance with accuracy, precision, and recall scores of 99.67%, 100%, and 99.25%, respectively which outperform the baseline methods. The robustness of the proposed ensemble model is evaluated through 5-fold cross-validation and achieved an average accuracy of 99.6%. The implementation code can be accessed from the GitHub repository: https://***/nahid-tech/cataract-detection. © 2023 IEEE.

关键词： Image enhancement

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Identification of Malignant Cells Using Convolutional Neural Network 1

Identification of Malignant Cells Using Convolutional Neural...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Sahu, Rekha Dash, Akshaya Kumar Silicon Institute of Technology Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350315769

Breast cancer has a high impact as a leading cause of death. Doctors face problems with the quick diagnosis of malignant tumors, which would help in the prevention of cancer at an early stage. Automatic detection of malignant cells is a life-saving boon. The deep learning approach extensively manipulates the data non-linearly to obtain suitable information. In this study, a one-dimensional convolutional neural network is implemented on a tumor dataset to detect malignant and benign tumors. The deep learning approach has achieved 99.9% classification accuracy with 45 epochs and 98.8% ROC-AUC. More experiments are needed on new tumor datasets to study the performance of the one-dimensional convolutional neural network approach in identifying malignant cells. © 2023 IEEE.

关键词： Tumors

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Operating System Command Execution Using Voice Command 3

Operating System Command Execution Using Voice Command

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Singh, Paras Nath Navaneetha, M. Tijare, Poonam Vijay Cmr Institute of Technology Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350302288

Recent years have seen a considerable increase in the use of voice commands as a result of the growing popularity of smart gadgets, virtual assistants, and other related technologies. In this study, we investigate the viability of voice commands for operating system commands. In this paper a system is proposed and demonstrated that enables voice commands to be recognized by the operating system and executed according to parameters. This way it is offering a fresh and more usable voice assistant to carry out typical computer chores. Python tools are used to go over how the system is put into practice, including how speech recognition and natural language processing are used, and how well it executes in terms of accuracy, speed, and usefulness. The main impact factor of this paper is to provide voice assistance to disadvantageous people whose hands and fingers are not working well. © 2023 IEEE.

关键词： Speech recognition

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Blockchain and IoT-Based Medical Care Utilizing Secure and Protection Safeguarding Watermark

Blockchain and IoT-Based Medical Care Utilizing Secure and P...

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2023 International Conference on Smart Systems for Applications in Electrical Sciences, ICSSES 2023

作者： Shruthi, K. Poornima, A.S. Siddaganga Institute of Technology Dept. of Computer Science and Engineering Tumakuru India

ISBN: (纸本)9798350347296

Patient clinical information is critical information for a well-being application. Clinical information regularly assumes a significant part in illness examination and is ensnared in persuading values for instructing symptomatic techniques and altering analytic outcomes. Privacy of patient well-being reports is more significant, furthermore, subsequently, specialists have considered numerous safety efforts, counting access control, classification, and uprightness of these records. To take care of the security issue, this paper presents a high-level arrangement in light of changing over clinical information into minimized size, as a watermark before being sent on Blockchain. Along these lines, difficult tasks will be securely performed off-chain, like gathering and preprocessing patient information, while accommodation of this information is finished on an hourly premise chain. We suggest an information stream design that consolidates the IoT with blockchain using the Ethereum platform, utilizing brilliant agreements to access and store information against many assaults. The proposed model is successful in sharing and overseeing patient electronic well-being information. The tests need to be carried out on Ethereum chain arrangements on laptops, and desktops. © 2023 IEEE.

关键词： Blockchain

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Comparative Study of Convolutional Neural Network Object Detection Algorithms for Image Processing

Comparative Study of Convolutional Neural Network Object Det...

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2023 IEEE International Conference on Electrical, computer and Energy Technologies, ICECET 2023

作者： Reddy, Saieshan Pillay, Nelendran Singh, Navin University of Technology Dept. of Electronic & Computer Engineering Durban South Africa

ISBN: (纸本)9798350327816

This paper presents a comparative study on three Convolutional Neural Network (CNN) object detection algorithms to find the best detector based on the combination of speed and accuracy on a personal computer. The MATLAB® development environment is used to evaluate three different object detector algorithms, namely Faster Region-Based Convolutional Network (R-CNN), Single Shot Detector (SSD) and You Only Look Once (YOLO). These algorithms are trained, and their performance metrics are tested on a small sample dataset. The results show that the SSD object detector algorithm performs best when considering both performance and processing speeds. Faster R-CNN detected objects at an average speed of 4.838 seconds and achieved a mean average precision of 0.76 with an average loss of 0.429. SSD detected objects at an average speed of 0.377 seconds and achieved a mean average precision of 0.92 with an average loss of 1.754. YOLO v3 detected objects at an average speed of 1.004 seconds and achieved a mean average precision of 0.81 with an average loss of 2.739. © 2023 IEEE.

关键词： computer Vision Convolutional Neural Network Image Processing MATLAB Object Detection

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Enhanced Autonomous Mobile Robot Navigation Using a Hybrid BFO/PSO Algorithm for Dynamic Obstacle Avoidance

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第17期48卷 209-222页

作者： Makhlouf, Amina Benmachiche, Abdelmadjid Boutabia, Ines LIMA Laboratory Dept. of Computer Science Faculty of science and technology House of Artificial Intelligence Chadli Bendjedid University El-Tarf PB 73 Algeria36000 Algeria

Determining path planning for mobile robots has received a lot of attention over the last three decades, with the goal of identifying safe and effective paths between starting points and destinations. The investigation of diverse navigation methodologies exhibits potential in expanding the range of uses for autonomous mobile robots. This research introduces a novel navigation strategy by combining the Particle Swarm Optimization (PSO) method with the Bacteria Foraging Optimization Algorithm (BFO). Utilizing the benefits of PSO, this bio-inspired method is used to optimize paths for mobile robots, drawing inspiration from the foraging behavior of bacteria, namely E. coli. The principal aim of this study is to design routes that are practical and safe. The proposed model features a robot that emulates bacterial foraging behavior to determine the most optimal path through environments densely populated with obstacles, effectively linking a starting point to a specified destination. This strategy has been implemented and rigorously evaluated across a variety of scenarios. Experimental results reveal that the hybrid BFO-PSO algorithm achieves a success rate exceeding 90% in navigating dynamic environments. Furthermore, the algorithm significantly outperforms traditional methods, offering approximately 50% less computational speed, efficiency, and superior security metrics, confirming its effectiveness and potential for real-world applications. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Escherichia coli

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