Journal of Machine and Computing最新文献

{"title":"Transfer Learning Based Weighted Deep Learning Ensemble Model for Medical Image Classification","authors":"Giddaluru Lalitha, Riyazuddin Y MD","doi":"10.53759/7669/jmc202404063","DOIUrl":"https://doi.org/10.53759/7669/jmc202404063","url":null,"abstract":"Malignant melanoma is a well-known and deadly form of cancer that originates from epidermal melanocytes in humans. Early detection of such diseases, including various forms of cancer, is necessary for speeding up diagnosis and enhancing patient outcomes. A novel transfer learning-based ensemble-deep learning model was presented for diagnosing diseases at a preliminary stage. Data augmentation was used to increase the dataset, and integration of Inception-v3, DenseNet-121, and ResNet-50 techniques, along with an ensemble method, was employed to overcome the scarcity of labeled datasets and increase the accuracy as well as make the model more robust. The proposed system was trained and tested employing the International Skin Imaging Collaboration (ISIC) dataset. The suggested ensemble model gained the best performance, producing 98% accuracy, 98% area under the curve, 98% precision, and 98% F1 score. The proposed model outperformed the existing state-of-the-art models in disease classification. Furthermore, the proposed model will be beneficial for medical diagnosis and reduce the incidence of various diseases.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Effective Word Vectors on Deep Learning Based Subjective Classification of Online Reviews","authors":"Priya Kamath B, G. M., D. U, Ritika Nandi, S. Urolagin","doi":"10.53759/7669/jmc202404069","DOIUrl":"https://doi.org/10.53759/7669/jmc202404069","url":null,"abstract":"Sentiment Analysis tasks are made considerably simpler by extracting subjective statements from online reviews, thereby reducing the overhead of the classifiers. The review dataset encompasses both subjective and objective sentences, where subjective writing expresses the author's opinions, and objective text presents factual information. Assessing the subjectivity of review statements involves categorizing them as objective or subjective. The effectiveness of word vectors plays a crucial role in this process, as they capture the semantics and contextual cues of a subjective language. This study investigates the significance of employing sophisticated word vector representations to enhance the detection of subjective reviews. Several methodologies for generating word vectors have been investigated, encompassing both conventional approaches, such as Word2Vec and Global Vectors for word representation, and recent innovations, such as like Bidirectional Encoder Representations from Transformers (BERT), ALBERT, and Embeddings from Language Models. These neural word embeddings were applied using Keras and Scikit-Learn. The analysis focuses on Cornell subjectivity review data within the restaurant domain, and metrics evaluating performance, such as accuracy, F1-score, recall, and precision, are assessed on a dataset containing subjective reviews. A wide range of conventional vector models and deep learning-based word embeddings are utilized for subjective review classification, frequently in combination with deep learning architectures like Long Short-Term Memory (LSTM). Notably, pre-trained BERT-base word embeddings exhibited exceptional accuracy of 96.4%, surpassing the performance of all other models considered in this study. It has been observed that BERT-base is expensive because of its larger structure.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141677154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GRU Based MCS Selection in Tactical Vehicle Communication","authors":"Seok-Jin Hong, Woong-Jong Yun, Eui-Rim Jeong","doi":"10.53759/7669/jmc202404057","DOIUrl":"https://doi.org/10.53759/7669/jmc202404057","url":null,"abstract":"In this paper, we propose optimal modulation coding scheme (MCS) selection based on Gated Recurrent Unit (GRU) for one-to-one communication between tactical vehicles. The communication between tactical vehicles assumes orthogonal frequency division multiplexing (OFDM) and performs bidirectional communication with time division duplexing (TDD) manner. Since the TDD system uses the same frequency for transmitting and receiving, the bidirectional communication channels are the same. Based on the Signal-to-Noise Ratio (SNR) measuring from the received signal, the MCS at the future transmission time is predicted, utilizing a Gated Recurrent Unit (GRU), which is a type of Recurrent Neural Network (RNN). Existing methods for predicting the MCS from the received SNR include the mean value method and the recent value method, and the method based on the convolutional neural network (CNN). Based on the computer simulation results, the proposed GRU-based RNN technique shows a lower outage probability of communication than all conventional methods while provides the highest throughput.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Driven Feature Extraction and Dimensionality Reduction for Image Classification","authors":"Angati Kalyan Kumar, Gangadhara Rao Kancharla","doi":"10.53759/7669/jmc202404052","DOIUrl":"https://doi.org/10.53759/7669/jmc202404052","url":null,"abstract":"Cancer is the leading cause of death globally, affecting various organs in the human body. Early diagnosis of gastric cancer is essential for improving survival rates. However, traditional diagnosis methods are time-consuming, require multiple tests, and rely on specialist availability. This motivates the development of automated techniques for diagnosing gastric cancer using image analysis. While existing computerized techniques have been proposed, challenges remain. These include difficulty distinguishing healthy from cancerous regions in images and extracting irrelevant features during analysis. This research addresses these challenges by proposing a novel deep learning-based method for gastric cancer classification. The method utilizes deep feature extraction, dimensionality reduction, and classification techniques applied to a gastric cancer image dataset. This approach achieves high accuracy (99.32%), sensitivity (99.13%), and specificity (99.64%) in classifying gastric cancer.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Machine Learning Algorithms and Advanced Computing Technology Using an Ensemble Hybrid Classifier","authors":"Roopashri Shetty, G. M., Shyamala G, D. U","doi":"10.53759/7669/jmc202404068","DOIUrl":"https://doi.org/10.53759/7669/jmc202404068","url":null,"abstract":"Ovarian Cancer (OC) is one of the major types of cancers in women worldwide. Despite the standardization of characteristics that can help distinguish benign from malignant ovarian masses, accurate predictive modelling following ultrasound (US) examination and biomarkers for ’progression-free survival’ is lacking in the field of ovarian cancer. Important leading factors in ovarian cancer lethality are the lack of diagnostic procedures and proper screening to detect early-stage ovarian cancer, and the rapid spread of the disease over the surface of the peritoneum. Therefore, developing tools for accurate screening and prognosis, as well as the diagnosis of early stage ovarian cancer, is a current clinical need. In this study, an ensemble classifier was developed as a novel means of ovarian cancer prediction, and its effectiveness was assessed. The ensemble classifier integrates various machine learning algorithms, including support vector machines (SVM), k-nearest neighbors (KNN), decision trees (DT), naïve Bayes (NB), and logistic regression (LR). Because ensembles may integrate the benefits of numerous models, they can mitigate the limitations of each model individually and improve the overall predictive performance, making them popular in the domain of machine learning. To increase predictive performance, an ensemble hybrid approach was created by utilizing a meta-classifier to merge many base classifiers. The performance with respect to various measures of the ensemble classifier was evaluated considering a comprehensive novel dataset of ovarian cancer patients, including tumor markers as well as clinical and ultrasound features. Through extensive cross-validation studies, the hybrid model showed better prediction accuracy of 95% which is approximately 6-17% improved than the baseline classifiers and state-of-the-art ensemble approaches in predicting ovarian cancer. After comparing the performance of the ensemble classifier with other existing classifiers, the ensemble classifier outperformed the individual models and conventional diagnostic techniques in terms of sensitivity (94%) and specificity (95%) through performance evaluation.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Groundwater Quality Evaluation Using Associative Rule Mining Technique with Random Forest Split Gini Indexing Algorithm for Nitrate Concentration Analysis","authors":"Siddthan R, Shanthi Pm","doi":"10.53759/7669/jmc202404067","DOIUrl":"https://doi.org/10.53759/7669/jmc202404067","url":null,"abstract":"Human actions and changing weather patterns are contributing to the growing demand for groundwater resources. Nevertheless, evaluating the quality of groundwater is crucial. Nitrate is a significant water contaminant that can lead to blue-baby syndrome or methemoglobinemia. Therefore, it is necessary to assess the level of nitrate in groundwater. Current methods involve evaluating the quality of groundwater and integrating it into the models. The inappropriate datasets, lack of performance, and other constraints are limitations of current methods. Ground water dataset is used and pre-processed the data’s. Selected data’s are feature extracted and associated with the rule ranking. In the suggested model, the use of associative rule mining technique has been implemented to address these challenges and assess nitrate levels in groundwater. The method of rule ranking is carried out using association rule mining technique to divide the datasets. The split gini indexing algorithm is introduced in the proposed model for data classification. The Split Gini Indexing algorithm is a decision tree induction algorithm that is used to build decision trees for classification tasks. It is based on the Gini impurity measure, which measures the heterogeneity of a dataset. The quality of groundwater has been classified using Naïve Bayes, SVM, and KNN algorithms. The proposed approach's efficiency is evaluated by calculating performance metrics such as precision, accuracy, F1-score, and recall values. The suggested method in the current research attains an improved accuracy of 0.99, demonstrating enhanced performance.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141673987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Cloud Data Deduplication with Dynamic Chunking and Public Blockchain","authors":"Richa Arora, Vetrithangam D","doi":"10.53759/7669/jmc202404050","DOIUrl":"https://doi.org/10.53759/7669/jmc202404050","url":null,"abstract":"The majority of cloud service providers (CSPs) store and remove customer data according to certain principles. The majority of them have designed their cloud platform to have very high levels of consistency, speed, availability, and durability. Their systems are built with these performance characteristics in mind, and the requirement to ensure precise and rapid data deletion must be carefully balanced. In the public blockchain, this paper suggests employing the rapid content-defined Chunking algorithm for data duplication. Acute data is frequently outsourced by individuals and organizations to distant cloud servers since doing so greatly reduces the headache of maintaining infrastructure and software. However, because user data is transmitted to cloud storage providers and stored on a remote cloud, ownership and control rights are nonetheless separated. Users thus have significant challenges when attempting to confirm the integrity of private information. According to the experiment results, the suggested dynamic chunking has a fast processing time that is on par with fixed-length chunking and significantly improves deduplication processing capability.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141675083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages","authors":"Sangeetha M, N. K","doi":"10.53759/7669/jmc202404012","DOIUrl":"https://doi.org/10.53759/7669/jmc202404012","url":null,"abstract":"Review comments from digital platform such as Facebook, Twitter and YouTube used for identification of emotional tones from text. Nowadays, reviews are posted in different languages such as English, French, Chinese, and Indian regional languages such as Tamil, Telegu, and Hindi. Identification of emotional tones from text written in Indian regional language is challenging. During the translation of the regional language to the English language for sentiment analysis, lexical and pragmatic ambiguity are the major problem. The above problem arises due to dialects in language such as regional, standard, and social dialects. In this paper, dialect-based ambiguity problems solve through proposed Hybrid optimized deep learning transformer Models like M-BERT, M-Roberta, and M-XLM-Roberta for Tamil language dialects recognise and classified. The proposed algorithms provide better sentimental analysis after Hybrid optimization due to adaptation mechanisms, dynamic changes in the parameters and strategies in fine-tuning the search. The proposed Hybrid optimized algorithms perform better than existing algorithms such as SVM, Naïve Bayes, and LSTM with an accuracy of 95%.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"36 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Based Precision Agriculture using Ensemble Classification with TPE Model","authors":"Latha M, Mandadi Vasavi, Chunduri Kiran Kumar, B. R, John Babu Guttikonda, Rajesh Kumar T","doi":"10.53759/7669/jmc202404025","DOIUrl":"https://doi.org/10.53759/7669/jmc202404025","url":null,"abstract":"Many tasks are part of smart farming, including predicting crop yields, analysing soil fertility, making crop recommendations, managing water, and many more. In order to execute smart agricultural tasks, researchers are constantly creating several Machine Learning (ML) models. In this work, we integrate ML with the Internet of Things. Either the UCI dataset or the Kaggle dataset was used to gather the data. Effective data pretreatment approaches, such as the Imputation and Outlier (IO) methods, are necessary to manage the intricacies and guarantee proper analysis when dealing with data that exhibits irregular patterns or contains little changes that can have a substantial influence on analysis and decision making. The goal of this research is to provide a more meaningful dataset by investigating data preparation approaches that are particular to processing data. Following the completion of preprocessing, the data is classified using an average approach based on the Ensemble of Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Neural Network (PNN), and Clustering-Based Decision Tree (CBDT) techniques. The next step in optimising the hyperparameter tuning of the proposed ensemble classifier is to employ a new Tree-Structured Parzen Estimator (TPE). Applying the suggested TPE based Ensemble classification method resulted in a 99.4 percent boost in accuracy","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"35 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Security Governance Policy and Risk Management: Research on Challenges and Coping Strategies","authors":"Jiehua Zhong, Xi Wang, Tao Zhang","doi":"10.53759/7669/jmc202404015","DOIUrl":"https://doi.org/10.53759/7669/jmc202404015","url":null,"abstract":"Cybersecurity is a big issue for major multinational corporations in today's lightning-fast digital world. Risk management and Network Security Governance (NSG) are complex, and this paper discusses the challenges and strategies needed to protect digital assets in a more vulnerable cyber environment. Cyber threats are constantly changing, technological integration is complex, and regulatory compliance is severe, all of which make it more challenging to maintain robust network security. NSG requires strong security rules and standards, which this conversation must address. The ever-changing threat environment demands that these regulations be open, accurate, and flexible. Risk management identifying, assessing, and mitigating threats—is essential to regulatory compliance and organizational reputation, according to the article. Risk mitigation methods like proactive, investigative, and remedial approaches are examined, along with cybersecurity advancements like Artificial Intelligence (AI) and Machine Learning (ML). In solving network security issues, the text emphasizes continuous learning, collaboration, and information sharing. Network Security Governance and Risk Management (NSGRM) is complex and dynamic, and this study covers its challenges and strategies.","PeriodicalId":516151,"journal":{"name":"Journal of Machine and Computing","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139449701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}