Journal of Computer Networks and Virtualization最新文献

{"title":"Smart Timetable System Using AI and ML","authors":"Gaurav Sapar, Arbaj Shaikh, Pratik Divekar","doi":"10.48001/jocnv.2024.2121-23","DOIUrl":"https://doi.org/10.48001/jocnv.2024.2121-23","url":null,"abstract":"Professional colleges have distinct course offerings, each with its own curriculum that covers a variety of disciplines. Teachers in various universities teach unique subjects in different semesters, and outside of the same semester, the academy focuses on two distinct topics. The most significant charge is that the timetable must be programmed in accordance with the college's provided time slots, with timetables ordered so that faculty timings do not clash. The timetable no longer overlaps with their various schedules and timetables.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140665049","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 Financial Sentiment Analysis: A Deep Dive into Natural Language Processing for Market Prediction Industries","authors":"Dattatray G. Takale","doi":"10.48001/jocnv.2024.221-5","DOIUrl":"https://doi.org/10.48001/jocnv.2024.221-5","url":null,"abstract":"The purpose of this study is to investigate the enhancement of Financial Sentiment Analysis by conducting an in-depth investigation of Natural Language Processing (NLP) approaches for the purpose of improving market prediction. The purpose of this research is to investigate the potential of natural language processing (NLP) to improve the accuracy and efficiency of sentiment analysis. This is in response to the complex structure of financial markets and the crucial role that sentiment plays. The examination of the relevant literature highlights the limits of traditional methods and the urgent need for creative solutions in the field of financial sentiment research. The approach that we use entails the careful collecting of data from social media and financial news, with a particular emphasis on the utilization of strong pre-processing tools. The research assesses the performance parameters of accuracy, precision, recall, and correlation with market trends by using natural language processing (NLP) technologies such as algorithms for sentiment analysis, Named Entity Recognition, and deep learning models. The findings include a comparative examination of conventional methods and those based on natural language processing (NLP), therefore revealing insights into the significant influence that sentiment has on market patterns. The results not only provide a contribution to the theoretical knowledge of sentiment research, but they also offer real consequences for financial analysts who are looking to make market forecasts that are more accurate and timelier. The research suggests ways for refinement, with an emphasis on enhanced pre-processing and Explainable AI integration. These tactics are being proposed to address issues in data quality and bias. When looking to the future, the study provides an overview of potential future paths, which include the investigation of external influences and the development of deep learning models for accurate market forecasting respectively. To summaries, the findings of this research establish natural language processing (NLP) as a revolutionary force in the process of redefining financial sentiment analysis. Furthermore, it offers a path for future developments in the ever-changing world of market prediction.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"156 20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140717756","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":"Technological Prerequisites and Consequences of Ubiquitous Computing and Networking in Resurrecting Extinct Computers","authors":"Aditya Patel, Nidhi Singh","doi":"10.48001/jocnv.2024.2115-20","DOIUrl":"https://doi.org/10.48001/jocnv.2024.2115-20","url":null,"abstract":"The passage discusses the previous days of computing, highlighting the experiment of alternative processor designs, such as the Connection Machine (CM1). The CM1 was a unique architecture consisting of 65536 individual one-bit processors interconnected as a 12dimensional hyper-cube. Despite its innovative design, the machine faced challenges and eventually faded into obscurity. To preserve this piece of computing history, efforts have been made to develop a cycle accurate simulator of the Connection-Machine and create an RTL (Register Transfer Level) hardware description of its building block chip. These preservation steps are crucial in ensuring that the legacy of the Connection Machine is not forgotten. The evaluate of the Connection Machine performance yields mixed result. While it demonstrates impressive performance on certain tasks such as a breadth first search algorithm with a remarkably low cycle-per-element ratio, its limitations become apparent in other applications, particularly those in linear algebra. Factors like the 1 bit word size and latency of messages passing impose constraints on performance, especially in traditionally parallelizable applications. Overall, the passage underscores the importance of understanding and preserving the history of computing, including the exploration of alternative architectures like the Connection Machine, despite their eventual challenges and limitations.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"80 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140720139","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":"Driver Drowsiness and Distraction Detection: An Image Processing-Based Comparative Analysis for Improved Accuracy","authors":"Dattatray G. Takale","doi":"10.48001/jocnv.2024.226-9","DOIUrl":"https://doi.org/10.48001/jocnv.2024.226-9","url":null,"abstract":"This research presents a comprehensive examination and implementation of driver drowsiness, distraction, and detection systems utilizing advanced image processing techniques. The literature review encompasses an in-depth analysis of drowsiness, distraction, and detection parameters, presented in tabulated form. The proposed architecture is detailed through flow charts outlining both software and hardware components. A comparative analysis of key parameters, along with their corresponding accuracy percentages, is provided in a structured table. The findings demonstrate that the proposed system exhibits superior accuracy compared to existing results. Through practical implementation, the system proves effective in accurately detecting driver sleepiness, classifying states as Sleepy, Drowsy, or Active. Notably, the proposed work achieves high accuracy, with eye detection accuracy at 98% and drowsiness accuracy at 96%, showcasing an improvement of approximately 10% when compared to existing solutions.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"43 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368085","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":"Prostate Cancer Detection Using Deep Learning and Traditional Techniques","authors":"Shreyash Matte, Sairaj Mengal, Tanmay Jadhav, Prafull Jadhav, Poorab Khawale, Atharva Khachane, Dattatray G. Takale","doi":"10.48001/jocnv.2024.211-8","DOIUrl":"https://doi.org/10.48001/jocnv.2024.211-8","url":null,"abstract":"Worldwide, men are affected by prostate cancer, which is a condition that is both common and has the potential to be fatal. Detection that is both timely and accurate is of the utmost importance for successfully treating patients and improving their outcomes. The technique of machine learning, which is a subfield of artificial intelligence, has recently emerged as a game-changing instrument for the identification of prostate cancer. The purpose of this work is to provide a complete overview and analysis of the use of machine learning methods in the detection, diagnosis, and prognosis of prostate cancer. The study that is being suggested makes use of a wide variety of datasets, which include genetic information, clinical records, and medical photographs. To guarantee the quality of the data, preprocessing techniques are used, and feature extraction techniques are utilized to assist the extraction of relevant information for the construction of models. There are several different machine learning algorithms that are being investigated to see whether they are effective in the identification of prostate cancer. These techniques include support vector machines (SVMs), convolutional neural networks (CNNs), and deep learning architectures. Several performance indicators, including accuracy, precision, recall, F1-score, and ROC-AUC, are taken into consideration throughout the training, validation, and assessment phases of our approach processes. In addition, the research covers ethical aspects, such as data protection, fairness, and the interpretability of models, which are essential for the use of machine learning solutions in healthcare settings. These findings provide evidence that machine learning has the potential to improve prostate cancer detection, which would allow for earlier diagnosis and more individualized therapy courses of treatment. In addition, the capacity to comprehend the predictions of the model and the openness of the model facilitate the ability of healthcare professionals to make educated judgements. This study contributes to the ever-changing environment of prostate cancer diagnosis by providing insights into the incorporation of machine learning into clinical practice. This, in turn, eventually leads to improvements in patient care and outcomes. To further advancing prostate cancer diagnosis and therapy, future approaches include the continuous development of models, the implementation of larger-scale clinical trials, and the utilization of developing technology respectively.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"46 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847849","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":"Sentiment Analysis Through the Application of Machine Learning Algorithms","authors":"Dattatray G. Takale, Aadesh Patil, Shirishkumar Jadhav, Sanika Masram, Shruti Masarkar, Raj Kate, Vivek Patil","doi":"10.48001/jocnv.2024.219-14","DOIUrl":"https://doi.org/10.48001/jocnv.2024.219-14","url":null,"abstract":"Sentiment analysis, also called opinion mining. It is a natural language processing (NLP) task that involves fixing the sentiment tone expressed in a piece of text including a review, tweet, post, comment, or news article, using machine learning algorithms. There are various social networking sites where people express their views, opinions, and emotions freely. All such posts are recorded and analyzed to determine emotions of the people. Present study recovers posts and finds emotions and polarity of posts. To determine emotions and polarity of posts, various techniques are used.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"158 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849686","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":"Sentiment Analysis Through the Application of Machine Learning Algorithms","authors":"Dattatray G. Takale, Aadesh Patil, Shirishkumar Jadhav, Sanika Masram, Shruti Masarkar, Raj Kate, Vivek Patil","doi":"10.48001/jocnv.2024.219-14","DOIUrl":"https://doi.org/10.48001/jocnv.2024.219-14","url":null,"abstract":"Sentiment analysis, also called opinion mining. It is a natural language processing (NLP) task that involves fixing the sentiment tone expressed in a piece of text including a review, tweet, post, comment, or news article, using machine learning algorithms. There are various social networking sites where people express their views, opinions, and emotions freely. All such posts are recorded and analyzed to determine emotions of the people. Present study recovers posts and finds emotions and polarity of posts. To determine emotions and polarity of posts, various techniques are used.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":" 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139790050","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":"Prostate Cancer Detection Using Deep Learning and Traditional Techniques","authors":"Shreyash Matte, Sairaj Mengal, Tanmay Jadhav, Prafull Jadhav, Poorab Khawale, Atharva Khachane, Dattatray G. Takale","doi":"10.48001/jocnv.2024.211-8","DOIUrl":"https://doi.org/10.48001/jocnv.2024.211-8","url":null,"abstract":"Worldwide, men are affected by prostate cancer, which is a condition that is both common and has the potential to be fatal. Detection that is both timely and accurate is of the utmost importance for successfully treating patients and improving their outcomes. The technique of machine learning, which is a subfield of artificial intelligence, has recently emerged as a game-changing instrument for the identification of prostate cancer. The purpose of this work is to provide a complete overview and analysis of the use of machine learning methods in the detection, diagnosis, and prognosis of prostate cancer. The study that is being suggested makes use of a wide variety of datasets, which include genetic information, clinical records, and medical photographs. To guarantee the quality of the data, preprocessing techniques are used, and feature extraction techniques are utilized to assist the extraction of relevant information for the construction of models. There are several different machine learning algorithms that are being investigated to see whether they are effective in the identification of prostate cancer. These techniques include support vector machines (SVMs), convolutional neural networks (CNNs), and deep learning architectures. Several performance indicators, including accuracy, precision, recall, F1-score, and ROC-AUC, are taken into consideration throughout the training, validation, and assessment phases of our approach processes. In addition, the research covers ethical aspects, such as data protection, fairness, and the interpretability of models, which are essential for the use of machine learning solutions in healthcare settings. These findings provide evidence that machine learning has the potential to improve prostate cancer detection, which would allow for earlier diagnosis and more individualized therapy courses of treatment. In addition, the capacity to comprehend the predictions of the model and the openness of the model facilitate the ability of healthcare professionals to make educated judgements. This study contributes to the ever-changing environment of prostate cancer diagnosis by providing insights into the incorporation of machine learning into clinical practice. This, in turn, eventually leads to improvements in patient care and outcomes. To further advancing prostate cancer diagnosis and therapy, future approaches include the continuous development of models, the implementation of larger-scale clinical trials, and the utilization of developing technology respectively.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":" 99","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787899","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":"Scheduling Algorithm in Infrastructure Less Network for SJF","authors":"K. Thamizhmaran","doi":"10.48001/jocnv.2023.118-10","DOIUrl":"https://doi.org/10.48001/jocnv.2023.118-10","url":null,"abstract":"Some of recent researchers interested in the field of sensor network based wireless field (WSN) because of very difficult to communication via air medium due to lack of problems in using same carrier channel in all communication and easy to attack for security issues like malicious attacks or misbehaviour attacks, grey hole attacks, block hole attacks, etc. In truest based path not easy to fine this drawback overcome we proposed and implement one type of scheduling algorithm called shortest first job with help of infrastructure less network. This leading algorithm is overcome above issue of security issue in the wireless sensor networks problems, because of finding best cost and distance unbreakable path between sender to receiver simulated using NS2.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130087669","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":"Design and Synthesis of AMBA APB Protocol for SoC","authors":"V. S. Kumar, Jeevika R, Harsheet Savadkar, Laxmi A Baligar, Akasha Marnnur","doi":"10.48001/jocnv.2023.1111-17","DOIUrl":"https://doi.org/10.48001/jocnv.2023.1111-17","url":null,"abstract":"The AMBA (Advanced Microcontroller Bus Architecture) and APB (Advanced Peripheral Bus) protocol is a widely used bus protocol designed by ARM for integrating peripheral devices into a system-on- chip (SoC) design. It provides a low-power, low-complexity interface that allows efficient communication between the master and slave devices within the SoC. The APB protocol offers a straightforward and flexible architecture, making it suitable for a wide range of SoC applications. It supports a single-master, multiple-slave bus structure, where an APB master can communicate with multiple APB slave devices. This architecture enables seamless integration of various peripheral devices, such as timers, UARTs, I/O controllers, and more, into the SoC. One of the key advantages of the APB protocol is its power efficiency. It achieves this by utilizing a simplified set of signals and reduced complexity compared to other bus protocols. The APB bus operates based on a common clock signal, ensuring proper timing and synchronization of data transfers while minimizing power consumption.","PeriodicalId":402315,"journal":{"name":"Journal of Computer Networks and Virtualization","volume":"21 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130661612","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}