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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Ahmad, Imam Tri Prastowo, Agung Indra Borman, Rohmat Tonggiroh, Mursalim Jusman, Yessi Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Universitas Yapis Papua Faculty of Computer Science Jayapura Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Faculty of Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Lung cancer is the leading cause of cancer-related fatalities in Indonesia, primarily due to late-stage diagnoses. This study aims to develop a model that employs image processing to classify lung cancer from CT scan images. The system combines the Learning Vector Quantization (LVQ) algorithm with the Gray Level Co-occurrence Matrix (GLCM) feature extraction method and utilizes spatial filtering techniques such as low pass, median, and high pass filters. The LVQ algorithm effectively manages non-linear data and forms complex decision boundaries through the distribution of prototype vectors. The findings show that the median filtering technique achieved the highest accuracy of 90.14% at a GLCM angle of 0°, demonstrating its effectiveness in reducing noise while maintaining crucial details for accurate classification. This suggests that the horizontal texture orientation may be more critical for analyzing lung images in this dataset. © 2024 IEEE.

关键词： Lung cancer

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Parallelizing Random and SAT-based Verification Processes for Improving Toggle Coverage

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IPSJ Transactions on system LSI Design Methodology 2023年 16卷 45-53页

作者： Hamaguchi, Kiyoharu Department of Information and Computer Science Faculty of Engineering Kyoto Tachibana University Kyoto607–8175 Japan

Coverage-driven verification based on simulation has been a widely accepted methodology for verifying hardware logic designs. The goal of this methodology is to improve a metric called coverage. In this paper, we adopt toggle coverage as a target. In our prior work, we have shown an approach which combines random simulation with input pattern generation using a SAT solver. The approach has been shown to be effective throughout experiments. The run-times of the SAT-solver, however, were dominant in the verification process, which could prevent progress of verification. In order to improve the approach, we extended this prior work by parallelizing the random/SAT-based processes. Experimental results show that the parallelization can be effective for achieving higher coverage. © 2023 Information Processing Society of Japan. All rights reserved.

关键词： Model checking

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Smart Agriculture: Enhancing Crop Management through IoT-Based Real-Time Monitoring and Automation 9

Smart Agriculture: Enhancing Crop Management through IoT-Bas...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Paliyanny, Hushalictmy Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Nalluri, Madhusudana Rao Cholla, Ravindra Raman Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India Icfai Foundation for Higher Education Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331510732

Internet of Things plays an important role in agriculture in order to provide an innovative and smart solution to traditional farming. IOT is all about connecting physical devices to the internet and can access from a remote place. So that crop monitoring becomes easy even if the farmer is in a remote place. Due to automation, human efforts are required less. Also the IoT data helps the farmer make decisions about planting, harvesting or fertilizing. This paper explains the system we have implemented for monitoring the agriculture with the help of IoT. The system is developed in order to provide real time information on crop conditions to the farmer. For that, it uses various types of sensors connected to the microcontroller in order to measure parameters like temperature, soil moisture, and water level, and accordingly, it activates actuators like a fan, motor or pump to manage ventilation and irrigation. We have used the GSM module to send the alert to the farmer and the WiFi module is used to send the agriculture data to the cloud platform. So that farmer can monitor the farm or crops remotely. The system is continuously collecting farm data from the sensors. If the crop soil is very dry them system will turn on the water pump. Also, for high temperature, the fan will get activated in order to cool down that area. This feature is mostly used in indoor farming. The farmer can monitor the farm condition in real time with the help of cloud platform. This system is helpful for improving productivity, crop management, and save resources i.e. efficient use of water which prevents water wastage. © 2024 IEEE.

关键词： Soil moisture

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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A Hybrid WSVM-Levy Approach for Energy-Efficient Manufacturing Using Big Data and IoT

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computers, Materials & Continua 2024年第12期81卷 4895-4914页

作者： Surbhi Bhatia Khan Mohammad Alojail Mahesh Thyluru Ramakrishna Hemant Sharma School of Science Engineering and EnvironmentUniversity of SalfordManchesterM68HDUK University Centre for Research and Development Chandigarh UniversityMohali140307India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Science and Engineering JAIN(Deemed-to-be University)Bangalore560030India Department of Computer Science and Engineering IES College of TechnologyBhopal462044India

In Intelligent Manufacturing,Big Data and industrial information enable enterprises to closely monitor and respond to precise changes in both internal processes and external environmental factors,ensuring more informed decision-making and adaptive system *** also promotes decision making and provides scientific analysis to enhance the efficiency of the operation,cost reduction,maximizing the process of production and so *** methods are employed to enhance productivity,yet achieving sustainable manufacturing remains a complex challenge that requires careful *** study aims to develop a methodology for effective manufacturing sustainability by proposing a novel Hybrid Weighted Support Vector-based Lévy flight(HWS-LF)*** objective of the HWS-LF method is to improve the environmental,economic,and social aspects of manufacturing *** this approach,Support Vector Machines(SVM)are used to classify data points by identifying the optimal hyperplane to separate different classes,thereby supporting predictive maintenance and quality control in *** Forest is applied to boost efficiency,resource allocation,and production optimization.A Weighted Average Ensemble technique is employed to combine predictions from multiple models,assigning different weights to ensure an accurate system for evaluating manufacturing ***,Lévy flight Optimization is incorporated to enhance the performance of the HWS-LF method *** method’s effectiveness is assessed using various evaluation metrics,including accuracy,precision,recall,F1-score,and *** show that the proposed HWS-LF method outperforms other state-of-the-art techniques,demonstrating superior productivity and system performance.

关键词： Sustainable manufacturing support vector machine random forest levy flight optimization weighted average ensemble

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A Novel Approach for Detection of Agricultural Crops Using CNN 14

A Novel Approach for Detection of Agricultural Crops Using C...

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

作者： Mahesh, T.R. Singh Chandel, Aman Gupta, Harshitha Jain, Rakshit Garg, Sadhvi Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350335095

Crop classification is an important aspect of farming because it improves crop management and increases crop yield. This study proposes a CNN-based crop classification technique using high-resolution images. To use this method, we created a database of 30 crops, including jute, maize, almonds, rice, sugarcane, banana, wheat, and others. The Convolutional Neural Network (CNN) is an Artificial Intelligence-based algorithm that learns similar features from images and is capable of performing a variety of tasks such as image classification, object detection, and segmentation. In this system, CNN or ConvNet is used to learn the features provided in the input images by sequentially entering several layers such as Convolution, Activation function (RELU), Pooling, and Flattening. Finally, with only a few explanations in the training set, this providing outstanding classifies new images. The proposed method has two major components: preprocessing and categorization. The experimental results show that the proposed tactic worked extremely well, with a ground truth of 0.9264. © 2023 IEEE.

关键词： Convolution

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Analysis on Improved Gaussian-Wiener filtering technique and GLCM based Feature Extraction for Breast Cancer Diagnosis 2

Analysis on Improved Gaussian-Wiener filtering technique and...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Ranjitha, K.V. Pushphavathi, T.P. Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India Department of Computer Science and Engineering Faculty of Engineering and Technology Ramaiah University of Applied Sciences Bangalore India

Breast cancer is become the most prevailing and fastest growing disease. In medical imaging, the use of machine learning and deep learning algorithms is essential. Classification of the tumor to predict the chemotherapy response for survival is trivial. In this paper, an innovative Gaussian-Wiener filter combination is used for de-noising the MRI images. These pre-processed images with good image quality are selected for tumor detection. On the basis of these pre-processed outputs, important features are extracted to determine the spatial relationship between the image pixels which results in better texture analysis for the tumor images. Analysis is made on the ISPY-2 trial breast MRI database. Results are analyzed which gives better image quality performance for MRI images. The filters and feature extraction method analyzed is used further in the segmentation and optimization process for breast detection and diagnosis to get the best accuracy of nearly 100%. The results also show better texture analysis for extracting features using GLCM based method. Furthermore, the MRI images for these methods used are explained for better performance in the process of breast cancer detection and diagnosis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Feature extraction

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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iWBFCxr: improved weighted box fusion for analyzing chest x ray images for enhancing thoracic abnormalities

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International Journal of Information Technology (Singapore) 2025年第2期17卷 711-720页

作者： Verma, Sonia Devarajan, Ganesh Gopal Sharma, Pankaj Kumar Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi - NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad 201204 India Department of Computer Science ABES Engineering College UP Ghaziabad 201009 India

Chest x-ray studies can be automatically detected and their locations located using artificial intelligence (AI) in healthcare. To detect the location of findings, additional annotation in the form of bounding boxes is required, rather than image-level labeling. Accurate interpretation of chest radiographs is essential for diagnosing thoracic abnormalities. This paper introduces a novel method for abnormality detection in chest radiographs using the VinDr-CXR dataset. Labeled training sets are improved by incorporating data augmentation and advanced pre-processing techniques. To improve the accuracy of anomaly identification, the study makes use of ensemble object detection models, such as the modified weighted boxes fusion (iWBFCxr) technique. With an average precision (AP) of 0.2434 for class 14, experimental results on the VinDr-CXR dataset show an overall mean average precision (mAP) of 0.2791. The iWBFCxr approach, when combined with ensemble predictions, has the potential to improve abnormality localization. By integrating enhanced label merging with ensemble object detection models, our method improves abnormality localization in chest radiographs significantly, improving thoracic abnormality diagnosis accuracy and possibly leading to new medical interventions. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Automated classification Automated localization Chest radiographs computer vision Deep learning Machine learning Thoracic anomalies

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Analysis of Deep Learning and Machine Learning Methods for Breast Cancer Detection 1

Analysis of Deep Learning and Machine Learning Methods for B...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Latha, D.U. Mahesh, T.R. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350341737

Breast cancer is the disease with the greatest incidence rate and the fastest global spread. Compared to men, women are diagnosed with breast cancer far more frequently. If detected early enough, breast cancer can be successfully treated. One of the leading causes of death internationally, breast cancer accounted for 22.6% of all cancer-related deaths worldwide in 2013. This indicates that more than one crore cancer-related deaths were brought on by breast cancer. 14.7% of all cancer cases in India are caused by breast cancer. Early breast cancer recognition has been the subject of extensive investigation since it can help with treatment start-up and reduce overall mortality rates. Many instances can be managed and the overall death rate can be lowered with an early diagnosis. Breast cancer has been the subject of numerous research studies. Machine learning is the method being applied in research. Older machine learning-based research that excelled in their respective fields include those that used decision trees, KNN, SVM, and naive bays techniques. Better algorithms have been created as a result of these investigations. On the other hand, deep learning, a recently created technique, is currently being utilised to categorize breast cancer. Data science commonly makes use of convolutional neural networks, recurrent neural networks, and other deep learning-based techniques. Deep learning algorithms outperform machine learning algorithms in terms of performance. The most captivating portions of the photographs have been removed. Many academics utilize CNN to categorize the images. In a nutshell, CNN is the technique that is most frequently used to categorize images. This piece of work compares and contrasts the employment of machine learning and deep learning techniques in numerous journals. © 2023 IEEE.

关键词： Support vector machines

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