ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal最新文献

{"title":"Optimized Deep Belief Network for Efficient Fault Detection in Induction Motor","authors":"Pradeep Katta, K. Karunanithi, S. Raja, S. Ramesh, S. Vinoth, John Prakash, Deepthi Joseph","doi":"10.14201/adcaij.31616","DOIUrl":"https://doi.org/10.14201/adcaij.31616","url":null,"abstract":"\u0000\u0000Numerous industrial applications depend heavily on induction motors and their malfunction causes considerable financial losses. Induction motors in industrial processes have recently expanded dramatically in size, and complexity of defect identification and diagnostics for such systems has increased as well. As a result, research has concentrated on developing novel methods for the quick and accurate identification of induction motor problems.In response to these needs, this paper provides an optimised algorithm for analysing the performance of an induction motor. To analyse the operation of induction motors, an enhanced methodology on Deep Belief Networks (DBN) is introduced for recovering properties from the sensor identified vibration signals. Restricted Boltzmann Machine (RBM) is stacked utilizing multiple units of DBN model, which is then trained adopting Ant colony algorithm.An innovative method of feature extraction for autonomous fault analysis in manufacturing is provided by experimental investigations utilising vibration signals and overall accuracy of 99.8% is obtained, which therefore confirms the efficiency of DBN architecture for features extraction.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"40 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809810","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":"Deep and Machine Learning for Acute Lymphoblastic Leukemia Diagnosis: A Comprehensive Review","authors":"Mohammad Faiz, Bakkanarappa Gari Mounika, Mohd Akbar, Swapnita Srivastava","doi":"10.14201/adcaij.31420","DOIUrl":"https://doi.org/10.14201/adcaij.31420","url":null,"abstract":"\u0000\u0000The medical condition known as acute lymphoblastic leukemia (ALL) is characterized by an excess of immature lymphocyte production, and it can affect people across all age ranges. Detecting it at an early stage is extremely important to increase the chances of successful treatment. Conventional diagnostic techniques for ALL, such as bone marrow and blood tests, can be expensive and time-consuming. They may be less useful in places with scarce resources. The primary objective of this research is to investigate automated techniques that can be employed to detect ALL at an early stage. This analysis covers both machine learning models (ML), such as support vector machine (SVM) & random forest (RF), as well as deep learning algorithms (DL), including convolution neural network (CNN), AlexNet, ResNet50, ShuffleNet, MobileNet, RNN. The effectiveness of these models in detecting ALL is evident through their ability to enhance accuracy and minimize human errors, which is essential for early diagnosis and successful treatment. In addition, the study also highlights several challenges and limitations in this field, including the scarcity of data available for ALL types, and the significant computational resources required to train and operate deep learning models.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"46 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647373","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":"Resolving Covid-19 with Blockchain and AI","authors":"Suyogita Singh, S. Verma","doi":"10.14201/adcaij.31454","DOIUrl":"https://doi.org/10.14201/adcaij.31454","url":null,"abstract":"\u0000\u0000In the early months of 2020, a fast-spreading outbreak was brought about by the new virus SARS-CoV-2. The uncontrolled spread, which led to a pandemic, illustrated the healthcare system’s slow response time to public health emergencies at that time. Blockchain technology was anticipated to be crucial in the effort to contain the COVID-19 pandemic. In that review, many potential blockchain applications were discovered; however, the majority of them were still in their infancy, and it couldn’t yet be predicted how they could contribute to the fight against COVID-19 through the use of platforms, access kinds, and consensus algorithms. Modern innovations such as blockchain and artificial intelligence (AI) were shown to be promising in limiting the spread of a virus. Blockchain could specifically aid in the battle against pandemics by supporting early epidemic identification, assuring the ordering of clinical information, and maintaining a trustworthy medical chain during disease tracing. AI also offered smart forms of diagnosing coronavirus therapies and supported the development of pharmaceuticals. Blockchain and AI software for epidemic and pandemic containment were analyzed in that research. First, a new conceptual strategy was proposed to tackle COVID-19 through an architecture that fused AI with blockchain. State-of-the-art research on the benefits of blockchain and AI in COVID-19 containment was then reviewed. Recent initiatives and use cases developed to tackle the coronavirus pandemic were also presented. A case study using federated intelligence for COVID-19 identification was also provided. Finally, attention was drawn to problems and prospective directions for further investigation into future coronavirus-like wide-ranging scenarios.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"44 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141350597","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":"Investigation of the Role of Machine Learning and Deep Learning in Improving Clinical Decision Making for Musculoskeletal Rehabilitation","authors":"Madhu Yadav, Pushpendra Kumar Verma, Sumaiya Ansari","doi":"10.14201/adcaij.31590","DOIUrl":"https://doi.org/10.14201/adcaij.31590","url":null,"abstract":"\u0000\u0000Musculoskeletal rehabilitation is an important aspect of healthcare that involves the treatment and management of injuries and conditions affecting the muscles, bones, joints, and related tissues. Clinical decision-making in musculoskeletal rehabilitation involves complex and multifactorial considerations that can be challenging for healthcare professionals. Machine learning and deep learning techniques have the potential to enhance clinical judgement in musculoskeletal rehabilitation by providing insights into complex relationships between patient characteristics, treatment interventions, and outcomes. These techniques can help identify patterns and predict outcomes, allowing for personalized treatment plans and improved patient outcomes. In this investigation, we explore the various applications of machine learning and deep learning in musculoskeletal rehabilitation, including image analysis, predictive modelling, and decision support systems. We also examine the challenges and limitations associated with implementing these techniques in clinical practice and the ethical considerations surrounding their use. This investigation aims to highlight the potential benefits of using machine learning and deep learning in musculoskeletal rehabilitation and the need for further research to optimize their use in clinical practice.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"35 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141354086","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":"Computer-Aided Detection and Diagnosis of Breast Cancer: a Review","authors":"B. Sharma, R. Purwar","doi":"10.14201/adcaij.31412","DOIUrl":"https://doi.org/10.14201/adcaij.31412","url":null,"abstract":"\u0000\u0000Statistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for this purpose. Many perform well (over 90% of classification accuracy, sensitivity, specificity, and f-1 sore), nevertheless, there is still room for improvement. This paper reviews literature related to breast cancer and the challenges faced by the research community. It discusses the common stages of breast cancer detection/ diagnosis using CAD models along with deep learning and transfer learning (TL) methods. In recent studies, deep learning models outperformed the handcrafted feature extraction and classification task and the semantic segmentation of ROI images achieved good results. An accuracy of up to 99.8% has been obtained using these techniques. Furthermore, using TL, researchers combine the power of both, pre-trained deep learning-based networks and traditional feature extraction approaches.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"287 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386635","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":"An Efficient Approach to Extract and Store Big Semantic Web Data Using Hadoop and Apache Spark GraphX","authors":"Wria Mohammed Salih Mohammed, Alaa Khalil Ju Maa","doi":"10.14201/adcaij.31506","DOIUrl":"https://doi.org/10.14201/adcaij.31506","url":null,"abstract":"\u0000\u0000The volume of data is growing at an astonishingly high speed. Traditional techniques for storing and processing data, such as relational and centralized databases, have become inefficient and time-consuming. Linked data and the Semantic Web make internet data machine-readable. Because of the increasing volume of linked data and Semantic Web data, storing and working with them using traditional approaches is not enough, and this causes limited hardware resources. To solve this problem, storing datasets using distributed and clustered methods is essential. Hadoop can store datasets because it can use many hard disks for distributed data clustering; Apache Spark can be used for parallel data processing more efficiently than Hadoop MapReduce because Spark uses memory instead of the hard disk. Semantic Web data has been stored and processed in this paper using Apache Spark GraphX and the Hadoop Distributed File System (HDFS). Spark's in-memory processing and distributed computing enable efficient data analysis of massive datasets stored in HDFS. Spark GraphX allows graph-based semantic web data processing. The fundamental objective of this work is to provide a way for efficiently combining Semantic Web and big data technologies to utilize their combined strengths in data analysis and processing.\u0000First, the proposed approach uses the SPARQL query language to extract Semantic Web data from DBpedia datasets. DBpedia is a hugely available Semantic Web dataset built on Wikipedia. Secondly, the extracted Semantic Web data was converted to the GraphX data format; vertices and edges files were generated. The conversion process is implemented using Apache Spark GraphX. Third, both vertices and edge tables are stored in HDFS and are available for visualization and analysis operations. Furthermore, the proposed techniques improve the data storage efficiency by reducing the amount of storage space by half when converting from Semantic Web Data to a GraphX file, meaning the RDF size is around 133.8 and GraphX is 75.3. Adopting parallel data processing provided by Apache Spark in the proposed technique reduces the required data processing and analysis time.\u0000This article concludes that Apache Spark GraphX can enhance Semantic Web and Big Data technologies. We minimize data size and processing time by converting Semantic Web data to GraphX format, enabling efficient data management and seamless integration.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"12 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141385210","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":"ML-Based Quantitative Analysis of Linguistic and Speech Features Relevant in Predicting Alzheimer’s Disease","authors":"Tripti Tripathi, Rakesh Kumar","doi":"10.14201/adcaij.31625","DOIUrl":"https://doi.org/10.14201/adcaij.31625","url":null,"abstract":"\u0000\u0000Alzheimer’s disease (AD) is a severe neurological condition that affects numerous people globally with detrimental consequences. Detecting AD early is crucial for prompt treatment and effective management. This study presents a novel approach for detecting and classifying six types of cognitive impairment using speech-based analysis, including probable AD, possible AD, mild cognitive impairment (MCI), memory impairments, vascular dementia, and control. The method employs speech data from DementiaBank’s Pitt Corpus, which is preprocessed and analyzed to extract pertinent acoustic features. The characteristics are subsequently used to educate five machine learning algorithms, namely k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), XGBoost, and random forest (RF). The effectiveness of every algorithm is assessed through a 10-fold cross-validation. According to the research findings, the suggested method based on speech obtains a total accuracy of 75.59% concerning the six-class categorization issue. Among the five machine learning algorithms tested, the XGBoost classifier showed the highest accuracy of 75.59%. These findings indicate that speech-based approaches can potentially be valuable for detecting and classifying cognitive impairment, including AD. The paper also explores robustness testing, evaluating the algorithms’ performance under various circumstances, such as noise variability, voice quality changes, and accent variations. The proposed approach can be developed into a noninvasive, cost-effective, and accessible diagnostic tool for the early detection and management of cognitive impairment.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"332 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141386537","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":"Sarcasm Text Detection on News Headlines Using Novel Hybrid Machine Learning Techniques","authors":"Neha Singh","doi":"10.14201/adcaij.31601","DOIUrl":"https://doi.org/10.14201/adcaij.31601","url":null,"abstract":"\u0000\u0000One of the biggest problems with sentiment analysis systems is sarcasm. The use of implicit, indirect language to express opinions is what gives it its complexity. Sarcasm can be represented in a number of ways, such as in headings, conversations, or book titles. Even for a human, recognizing sarcasm can be difficult because it conveys feelings that are diametrically contrary to the literal meaning expressed in the text. There are several different models for sarcasm detection. To identify humorous news headlines, this article assessed vectorization algorithms and several machine learning models. The recommended hybrid technique using the bag-of-words and TF-IDF feature vectorization models is compared experimentally to other machine learning approaches. In comparison to existing strategies, experiments demonstrate that the proposed hybrid technique with the bag-of-word vectorization model offers greater accuracy and F1-score results.\u0000","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"72 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141382484","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 VR on Learning Experience compared to a Paper based Approach","authors":"Stella Kolarik, Christoph Schlüter, Katharina Ziolkowski","doi":"10.14201/adcaij.31134","DOIUrl":"https://doi.org/10.14201/adcaij.31134","url":null,"abstract":"Different learning theories encourage different kinds of learning approaches. Following constructivist theories, learning experiences should be realistic in order to facilitate learning. Virtual Reality (VR) serious games could be a realistic learning approach without the challenges of the real situation. The serious game InGo allows a user to learn the intralogistics process of receiving goods. In this work we explore whether learning in VR is more effective concerning learning success and learning experience than traditional learning approaches. No significant difference between the two approaches concerning learning success is found. However, other factors that have a long term effect on learning, such as intrinsic motivation, flow and mood, are significantly higher for the VR approach. Thus, our research fits with past research which indicated the high potential of VR based learnig and educational games. This work encourages future research to compare VR based and traditional learning approaches in the long term.","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"61 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527031","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":"A Detailed Sentiment Analysis Survey Based on Machine Learning Techniques","authors":"Neha Singh, U. C. Jaiswal","doi":"10.14201/adcaij.29105","DOIUrl":"https://doi.org/10.14201/adcaij.29105","url":null,"abstract":"Sentiment analysis is a rapidly growing topic of research as a result of the tremendous growth of digital information. In the modern era of artificial intelligence, one of the most crucial technologies for obtaining sentiment data from the vast amounts of data is sentiment analysis. It refers to a procedure of finding and categorising the opinions expressed in a source text. Reaching a consensus regarding business decisions is made much easier by conducting a sentiment analysis on consumer data. Machine learning offers an efficient and trustworthy technique for sentiment categorization and opinion mining. State-of-art machine learning techniques and methodologies have evolved and expanded. In addition to summarising research articles based on movie reviews, product reviews, and Twitter reviews, this survey article covers sentiment analysis notations, needs, levels, methodologies, sources, and machine learning approaches and tools. This research aims to determine the significance of sentiment analysis and to generate interest in the subject.","PeriodicalId":504145,"journal":{"name":"ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal","volume":"181 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139171524","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}