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An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization

International Journal of computers and Applications 2023年第1期45卷 36-50页

作者： Shukla, Alok Kumar Pippal, Sanjeev Kumar Chauhan, Sansar Singh Department of Computer Science and Engineering G.L. Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India

Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimization techniques are not able. To overcome the limitation of the classical approach, in the recent study, a large number of metaheuristic methods have been investigated to improve the solution quality concerning convergence and accuracy on the complex problems. Nowadays, a popular metaheuristic algorithm is introduced called teaching–learning-based optimization (TLBO). It is recently being used as an innovative, and robust method to solve the global optimization problem, inspired by the teaching–learning phenomenon. On the other hand, particle swarm optimization (PSO) algorithm is one of the most utilized algorithms in the current scenario, which has indicated acceptable efficiency. Genetic algorithm (GA) is a valuable component of metaheuristic which has been applied in various research applications. In this article, the performance of TLBO algorithm is estimated on 25 numerical test suites against other metaheuristic algorithms such as PSO and GA. The results of our experimental study show that TLBO outperforms the PSO and GA algorithm regarding convergence solution. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Genetic algorithms

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Echo Guidance: Voice-Activated Application for Blind with Smart Assistive Stick Using Machine Learning and IoT

Echo Guidance: Voice-Activated Application for Blind with Sm...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Abishek Kumar, G. Surya, G. Sathyadurga, V. Hindustan Institute Of Technology And Science Department Of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350364828

Navigating the world without sight presents profound challenges for millions of visually impaired individuals. Echo Guidance includes a pioneering application, addresses these obstacles by facilitating independence and mobility through voice-driven interaction and an IoT smart assistive device for obstacle detection by utilizing Ultrasonic, GPS and GSM modules powered by Arduino. This study explores the design and evaluation of Echo Guidance, highlighting its utilization of advanced algorithms like RGB888 conversion, matrix transformation, and Optical Character Recognition (OCR) for real-time object recognition. The application boasts a voice-centric interface tailored to the needs of the visually impaired, seamlessly integrating day-to-day functionalities such as weather forecasting, reminders, and a calculator accessible through voice commands. Central to Echo Guidance is its innovative object detection and recognition approach, employing the MobileNetObjDetector class powered by TensorFlow Lite for swift and accurate object detection. The OverlayView class visually represents detected objects, enhancing spatial awareness. Additionally, the integration of IoT devices, such as the smart stick with ultrasonic sensors for obstacle detection and head-level obstacle detection, coupled with SOS functionality in case of any emergency using GPS and GSM modules, further enhances Echo Guidance's capabilities. This research underscores Echo Guidance's transformative potential in improving the quality of life for visually impaired individuals, representing a significant advancement in assistive technology. Prioritizing accessibility and user experience, this paper empowers individuals worldwide with newfound independence and confidence in navigating their surroundings. © 2024 IEEE.

关键词： Linear transformations

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RBCA-ETS: enhancing extractive text summarization with contextual embedding and word-level attention

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International Journal of Information technology (Singapore) 2025年第2期17卷 1127-1135页

作者： Gangundi, Ravindra Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli 620015 India

The existing limitations in extractive text summarization encompass challenges related to preserving contextual features, limited feature extraction capabilities, and handling hierarchical and compositional aspects. To address these issues, the RoBERTa-BiLSTM-CNN-Attention Extractive Text Summarization, i.e., the RBCA-ETS model, is proposed in this work. RoBERTa word embedding is used to generate contextual embeddings. Parallelly connected CNN and BiLSTM layers extract textual features. CNN focuses more on local features, and BiLSTM captures long-range dependencies that extend across sentences. These two feature sets are concatenated and forwarded to the attention layer, highlighting the most relevant features. In the output layer, a fully connected layer receives the attention vector and calculates sentence scores for each sentence. This leads to the generation of the final summary. The RBCA-ETS model has demonstrated superior performance on the CNN-Daily Mail (CNN/DM) dataset compared to many state-of-the-art methods, and it has also outperformed existing state-of-the-art techniques when tested on the out-of-domain DUC 2002 dataset. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Attention BiLSTM CNN RoBERTa Text summarization

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Exploring Transfer Learning in Brain Tumor Classification with Deep Neural Networks 11

Exploring Transfer Learning in Brain Tumor Classification wi...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Rawat, Vikas Singh Johar, Sakshi Kumar, Amit Singh, Ninni University Department of Computer Science and Engineering Dehradun India Cmr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350357

In today's era, medical advancements have reached their peak but still, some bugs led to the emergence and involvement of advanced techniques like Machine learning, Artificial Intelligence, and Deep Learning. These three are interconnected and have strong relationships that work as such, to increase the growth of work in any domain with rapid accuracy and without any error. This paper deals with the identification and categorization of tumors. Brain tumors are a result of abnormal behavior of cell division which leads to uncontrolled growth of cells and results in the formation of masses and lumps in the brain. The detection of tumors is done using radiology with the help of MRI techniques or MRI scans. Traditional scans sometimes lead to errors causing misdiagnoses. This process is also timeconsuming and as such requires high expertise. To elevate this process, in this paper, we used a multi-layered CNN model which takes the MRI images as input and classifies it into one of the four major categories i.e., 'Glioma', 'Meningioma', 'Pituitary' or 'No Tumor'. The publicly available dataset used to train this model contains over 7000 images which have been segregated into testing and training where both classes contain 4 different segmentations depicting the different brain tumors. The training folder contains nearly 5200 images, and the testing folder has over 1500 images. The accuracy of the model has been tested to over 96 percent. Then we converted the above model to the VGG16 model by using the study of Transfer Learning and the model gave up to 98.04% accuracy and validation accuracy was approximately 94.73%. © 2024 IEEE.

关键词： Tumors

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Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

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Multimedia Tools and Applications 2025年第9期84卷 5653-5698页

作者： Datta, Kankana Jana, Biswapati Chakraborty, Mamata Dalui Department of Computer Applications Haldia Institute of Technology HIT Campus Hatiberia Haldia Purba Medinipur West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it has become one of the biggest challenge to meet all these requirements. Embedding secret messages in dual images is one of the latest techniques that aims to cater such needs. However, there are very few data hiding schemes that are currently using dual images, but are suffering from several shortcomings, such as security loophole and poor visual quality with limited hiding capacity. In this paper, a dual image-based reversible data hiding scheme has been proposed where multiple bits can be embedded within a pair of pixels dynamically by Huffman compression where different pseudo codes are generated for different chunk of secret bits which are embedded by Bit-Reversal Permutation (BRP) and X-OR operations. Finally, pixels are interchanged between the dual images based on odd-even properties of the pixel values. The functional efficiency of the proposed scheme is evaluated by comparing the relevant parameters of different state-of-the-art schemes. It is established that the proposed scheme provides more than 64 dB PSNR value on average after embedding a maximum of 3, 27, 680 bits of secret data which ensures good visual quality and high embedding capacity as compared to other state-of-the-art schemes thereby benefitting innumerable applications across public and private sectors. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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Deep Adversarial Network Based Dental Inlay Restoration Using Point Cloud Segmenntation 3

Deep Adversarial Network Based Dental Inlay Restoration Usin...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Lakshmi, S. Shanmuganathan, C. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

computer Aided Design (CAD) and dental industry are going hand-in-hand due to the increasing requirements for digitalizing the process of dental diagnosis and treatment. The crucial step in dental treatment is the identification of the individual affected teeth and gingiva region. These days 3D intraoral scanners are widely used to take the inputs for analysis and design of the tooth. High accuracy and resolution play a predominant role in providing efficient and hassle-free treatment to the patients. Since the intraoral scanners provide low quality input images in deep intraoral regions, current automated systems are facing the challenge of automated tooth labeling. The problem of segmentation of teeth and gingiva region of 3D IOS mesh scans are complex to process and produce desired output. The design of dental inlay restoration requires a fully automated end-to-end network to be developed. We propose a deep learning method called DPCNet to produce segmentation outputs that can automate the construction of a realistic surface of the defective tooth. Our goal was to start using point clouds as input to the neural network, which are obtained from mesh scans. DPCNet employs graph-based learning modules to acquire local features, which are summarized and fed into a stack of convolutional layers to achieve global features. Our approach is effective in real-time datasets, with minimal need for manual adjustment scenarios encountered. © 2024 IEEE.

关键词： Mesh generation

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A Novel Approach to Optimize Energy Consumption in Industries Using IIoT and Machine Learning

A Novel Approach to Optimize Energy Consumption in Industrie...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Yadav, Ritesh Kumar Malavika, V. Rajendran, P.Selvi Hindustan Institute Of Technology And Science Department Of Computer Science Engineering Chennai India

ISBN: (纸本)9798350364828

Managing Electrical Energy has become crucial nowadays where everything we use works on electrical energy. Utilizing energy in an efficient and effective way can save a lot of energy. Reducing unnecessary usage of electrical current can lead to lowering carbon emission and also can directly benefit the production cost in industries. This reduction is possible when we continuously monitor the usage of the current when it's being utilized. In order to overcome this problem our novel idea proposes a state of art algorithm such as the Autoregressive Integrated Moving Average (ARIMA) model which is the link between one observation and several lagged observations of the same series modeled by the autoregressive component. In essence, it depicts how the current value is linearly dependent on its own historical values. Linear regression is a prevalent statistical technique utilized to model the association between a dependent variable and one or more independent variables. When it comes to energy consumption, linear regression can be used to examine and forecast patterns of energy use depending on a variety of influencing factors. To improve the accuracy of the proposed work, the use of Time Series Analysis aids in making the system more reliable and efficient. We have developed a system in which an all-in-one energy management solution provides a platform where we can control this wastage and manage all equipment in one place. Our proposed system monitors electrical energy using various IOT sensors to continuously monitor energy utilization and predicting the usage earlier to help the industries to make better financial plans. © 2024 IEEE.

关键词： Energy utilization

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CNN-LSTM: A Novel Hybrid Deep Neural Network Model for Brain Tumor Classification

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Intelligent Automation & Soft Computing 2023年第7期37卷 1129-1143页

作者： R.D.Dhaniya K.M.Umamaheswari Department of Computer Science and Engineering SRM Institute of Science and TechnologyKattankulathur603202India Department of Computing technologies SRM Institute of Science and TechnologyKattankulathur603202India

Current revelations in medical imaging have seen a slew of computer-aided diagnostic(CAD)tools for radiologists *** tumor classification is essential for radiologists to fully support and better interpret magnetic resonance imaging(MRI).In this work,we reported on new observations based on binary brain tumor categorization using HYBRID ***,the collected image is pre-processed and augmented using the following steps such as rotation,cropping,zooming,CLAHE(Contrast Limited Adaptive Histogram Equalization),and Random Rotation with panoramic stitching(RRPS).Then,a method called particle swarm optimization(PSO)is used to segment tumor regions in an MR *** that,a hybrid CNN-LSTM classifier is applied to classify an image as a tumor or *** this proposed hybrid model,the CNN classifier is used for generating the feature map and the LSTM classifier is used for the classification *** effectiveness of the proposed approach is analyzed based on the different metrics and outcomes compared to different methods.

关键词： Brain tumor segmentation particle swarm optimization CNN-LSTM convolution neural network

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The Development of Polycystic Ovary Syndrome Risk Evaluation System using Advanced Machine Learning Technique 7

The Development of Polycystic Ovary Syndrome Risk Evaluation...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Velvizhi, R. Arya Kiruthiknivas, R. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

A complex endocrine disorder that affects fertile women, polycystic ovary syndrome, also known as PCOS, is characterized by an extensive spectrum of symptoms. This work extends previous efforts in PCOS detection and tracking with the goal of utilizing state-of-the-art machine learning techniques to overcome existing constraints. The goal of the project is to improve medical outcomes by utilizing predictive analytics to analyze big patient datasets that include genetic and clinical data. In this work, robust prediction models are developed by applying machine learning techniques, including Random Forest, decision trees, and support vector machines. These models aim to accurately identify individuals who are susceptible to PCOS and forecast the course of the disease in those who have already been diagnosed. Crucially, an extensive performance assessment is provided, contrasting the suggested models with current ones to emphasize their advantages and shortcomings. In-depth dataset investigation, rigorous model training, and comprehensive assessment metrics are conducted as part of the experiments. The outcomes highlight the usefulness of the models and indicate how they could assist medical practitioners in making defensible decisions. Predictive analytics integrated into electronic health records (EHRs) allows for ongoing PCOS patient monitoring, enabling timely interventions, individualized treatment plans, and better patient results. This research advances the area by improving the accuracy of PCOS diagnosis and therapy and by addressing the shortcomings of the previous literature. The suggested machine learning methods improve patient satisfaction, health outcomes, and system productivity, in addition to enhancing identification accuracy. This revised strategy guarantees a more impactful and targeted portrayal of the research activity, in line with the input that was given. © 2024 IEEE.

关键词： Decision trees

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Document Generation and Validation using Blockchain 8

Document Generation and Validation using Blockchain

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Bharati, Rajesh Jaiswal, Deepika Jadhav, Priyanka Patil, Pranav Joshi, Sarthak Lashkare, Venkatesh Patil, Hrushikesh Ahire, Prashant Dypit Department of Computer Engineering Pune India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350391770

Counterfeit educational diplomas are a prevalent issue, stemming from the large number of graduates and a lack of efficient anti-forgery measures. Traditional document certification and verification processes are time-consuming. The proposed solution is a blockchain-based digital document system, which enhances security and transparency. This system converts paper documents into electronic files, calculates their hash values, and stores them in a blockchain, making forgery less likely. By identifying security themes and addressing gaps in the current blockchain verification, the system creates QR codes and inquiry codes for attaching to paper documents, enabling easy verification via mobile scanning or website inquiries. This addresses the pressing issue of counterfeit documents. This article describes a blockchain technology algorithm for validating digital certificates. As the number of university and higher education students and graduates continues to grow each year, there is a need to easily validate graduation certificates. In this paper, we intend two financial models in which the price of services is balanced between graduates and employers as the main actors of services. Students demand cheap and easily verifiable certificates, and employers demand fast and reliable proof of their degrees. The issue of fake credentials is a big one. Getting a fake education certificate in India is not that difficult. Companies that hire thousands of freshmen spend a lot of money verifying applicants' educational credentials and qualifications. © 2024 IEEE.

关键词： Blockchain

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