Recent Advances in Computer Science and Communications最新文献

{"title":"Supervised Learning based E-mail/ SMS Spam Classifier","authors":"Satendra Kumar, Raj Kumar, A. Saini","doi":"10.2174/0126662558279046240126051302","DOIUrl":"https://doi.org/10.2174/0126662558279046240126051302","url":null,"abstract":"\u0000\u0000One of the challenging problems facing the modern Internet is spam,\u0000which can annoy individual customers and wreak financial havoc on businesses. Spam communications target customers without their permission and clog their mailboxes. They consume\u0000more time and organizational resources when checking for and deleting spam. Even though\u0000most web users openly dislike spam, enough are willing to accept lucrative deals that spam remains a real problem. While most web users are well aware of their hatred of spam, the fact\u0000that enough of them still click on commercial offers means spammers can still make money\u0000from them. While most customers know what to do, they need clear instructions on avoiding\u0000and deleting spam. No matter what you do to eliminate spam, you won't succeed. Filtering is\u0000the most straightforward and practical technique in spam-blocking strategies.\u0000\u0000\u0000\u0000We present procedures for identifying emails as spam or ham based on text classification. Different methods of e-mail organization preprocessing are interrelated, for example, applying stop word exclusion, stemming, including reduction and highlight selection strategies to\u0000extract buzzwords from each quality, and finally, using unique classifiers to Quarantine messages as spam or ham.\u0000\u0000\u0000\u0000The Nave Bayes classifier is a good choice. Some classifiers, such as Simple Logistic\u0000and Adaboost, perform well. However, the Support Vector Machine Classifier (SVC) outperforms it. Therefore, the SVC makes decisions based on each case's comparisons and perspectives.\u0000\u0000\u0000\u0000Many spam separation studies have focused on recent classifier-related challenges. Machine Learning (ML) for spam detection is an important area of modern research. Today,\u0000spam detection using ML is an important area of research. Examine the adequacy of the proposed work and recognize the application of multiple learning estimates to extract spam from\u0000emails. Similarly, estimates have also been scrutinized.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" 57","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366173","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":"ROUGE-SS: A New ROUGE Variant for the Evaluation of Text\u0000Summarization","authors":"Sandeep Kumar, Arun Solanki, NZ Jhanjhi","doi":"10.2174/0126662558304595240528111535","DOIUrl":"https://doi.org/10.2174/0126662558304595240528111535","url":null,"abstract":"\u0000\u0000Prior research on abstractive text summarization has predominantly\u0000relied on the ROUGE evaluation metric, which, while effective, has limitations in capturing\u0000semantic meaning due to its focus on exact word or phrase matching. This deficiency is particularly pronounced in abstractive summarization approaches, where the goal is to generate novel summaries by rephrasing and paraphrasing the source text, highlighting the need for a more\u0000nuanced evaluation metric capable of capturing semantic similarity.\u0000\u0000\u0000\u0000In this study, the limitations of existing ROUGE metrics are addressed by proposing\u0000a novel variant called ROUGE-SS. Unlike traditional ROUGE metrics, ROUGE-SS extends\u0000beyond exact word matching to consider synonyms and semantic similarity. Leveraging resources such as the WordNet online dictionary, ROUGE-SS identifies matches between source\u0000text and summaries based on both exact word overlaps and semantic context. Experiments are\u0000conducted to evaluate the performance of ROUGE-SS compared to other ROUGE variants,\u0000particularly in assessing abstractive summarization models. The algorithm for the synonym\u0000features (ROUGE-SS) is also proposed.\u0000\u0000\u0000\u0000The experiments demonstrate the superior performance of ROUGE-SS in evaluating\u0000abstractive text summarization models compared to existing ROUGE variants. ROUGE-SS\u0000yields higher F1 scores and better overall performance, achieving a significant reduction in\u0000training loss and impressive accuracy. The proposed ROUGE-SS evaluation technique is evaluated in different datasets like CNN/Daily Mail, DUC-2004, Gigawords, and Inshorts News\u0000datasets. ROUGE-SS gives better results than other ROUGE variant metrics. The F1-score of\u0000the proposed ROUGE-SS metric is improved by an average of 8.8%. These findings underscore the effectiveness of ROUGE-SS in capturing semantic similarity and providing a more\u0000comprehensive evaluation metric for abstractive summarization.\u0000\u0000\u0000\u0000In conclusion, the introduction of ROUGE-SS represents a significant advancement in the field of abstractive text summarization evaluation. By extending beyond exact\u0000word matching to incorporate synonyms and semantic context, ROUGE-SS offers researchers\u0000a more effective tool for assessing summarization quality. This study highlights the importance\u0000of considering semantic meaning in evaluation metrics and provides a promising direction for\u0000future research on abstractive text summarization.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"207 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141375871","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 Generic Integrated Framework of Unsupervised Learning and Natural\u0000Language Processing Techniques for Digital Healthcare: A Comprehensive\u0000Review and Future Research Directions","authors":"K. Shastry","doi":"10.2174/0126662558297036240527120451","DOIUrl":"https://doi.org/10.2174/0126662558297036240527120451","url":null,"abstract":"\u0000\u0000The increasing availability of digital healthcare data has opened up fresh prospects\u0000for improving healthcare through data analysis. Machine learning (ML) procedures exhibit\u0000great promise in analyzing large volumes of healthcare data to extract insights that could be\u0000utilized to improve patient outcomes and healthcare delivery. In this work, we suggest an integrated\u0000framework for digital healthcare data analysis by integrating unsupervised learning\u0000techniques and natural language processing (NLP) techniques into the analysis pipeline. The\u0000module on unsupervised learning will involve techniques, such as clustering and anomaly detection.\u0000By clustering similar patients together based on their medical history and other relevant\u0000factors, healthcare providers can identify subgroups of patients who may require different\u0000treatment approaches. Anomaly detection can also help to detect patients who stray from the\u0000norm, which could be indicative of underlying health issues or other issues that need additional\u0000investigation. The second module on NLP will enable healthcare providers to analyze unstructured\u0000text data such as clinical notes, patient surveys, and social media posts. NLP techniques\u0000can help to identify key themes and patterns in these datasets, requiring awareness that could\u0000not be readily apparent through other means. Overall, incorporating unsupervised learning\u0000techniques and NLP into the analysis pipeline for digital healthcare data possesses the promise\u0000to enhance patient results and lead to more personalized treatments, and represents a potential\u0000domain for upcoming research in this field. In this research, we also review the current state of\u0000research in digital healthcare information examination with ML, including applications like\u0000forecasting clinic readmissions, finding cancerous tumors, and developing personalized drug\u0000dosing recommendations. We also examine the potential benefits and challenges of utilizing\u0000ML in healthcare data analysis, including issues related to data quality, privacy, and interpretability.\u0000Lastly, we discuss the forthcoming research paths, involving the necessity for enhanced\u0000methods for incorporating information from several resources, developing more interpretable\u0000ML patterns, and addressing ethical and regulatory challenges. The usage of ML in digital\u0000healthcare data analysis promises to transform healthcare by empowering more precise diagnoses,\u0000personalized treatments, and improved health outcomes, and this work offers a complete\u0000overview of the current trends.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141388369","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":"Recent Advances in Artificial Intelligence & Machine Learning:\u0000A Practical Approach","authors":"Vikash Yadav","doi":"10.2174/266625581703240502163544","DOIUrl":"https://doi.org/10.2174/266625581703240502163544","url":null,"abstract":"<jats:sec>\u0000<jats:title/>\u0000<jats:p/>\u0000</jats:sec>","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"13 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141024011","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":"Artificial Intelligence (AI) driven Smart World","authors":"Sarika Jain","doi":"10.2174/266625581702240417140438","DOIUrl":"https://doi.org/10.2174/266625581702240417140438","url":null,"abstract":"<jats:sec>\u0000<jats:title />\u0000<jats:p />\u0000</jats:sec>","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140692076","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 Cost-Minimized Task Migration Assignment Mechanism in Blockchain Based Edge Computing System","authors":"Binghua Xu, Yan Jin, Lei Yu","doi":"10.2174/0126662558292891240409050246","DOIUrl":"https://doi.org/10.2174/0126662558292891240409050246","url":null,"abstract":"\u0000\u0000Cloud computing is usually introduced to execute computing intensive\u0000tasks for data processing and data mining. As a supplement to cloud computing, edge\u0000computing is provided as a new paradigm to effectively reduce processing latency, energy consumption\u0000cost and bandwidth consumption for time-sensitive tasks or resource-sensitive tasks.\u0000To better meet such requirements during task assignment in edge computing systems, an intelligent\u0000task migration assignment mechanism based on blockchain is proposed, which jointly\u0000considers the factors of resource allocation, resource control and credit degree.\u0000\u0000\u0000\u0000Cloud computing are usually introduced to execute computing intensive tasks for data processing and data mining. However, this paradigm may not be effective to execute latency sensitive or dynamic interactive tasks. As a supplement to the cloud computing, edge computing has attracted much attention because it can effectively reduce task processing latency, energy consumption cost and bandwidth consumption.\u0000\u0000\u0000\u0000In this paper, an optimization problem is firstly constructed to minimize the total\u0000cost of completing all tasks under constraints of delay, energy consumption, communication,\u0000and credit degree. Here, the terminal node mines computing resources from edge nodes to\u0000complete task migration. An incentive method based on blockchain is provided to mobilize the\u0000activity of terminal nodes and edge nodes, and to ensure the security of the transaction during\u0000migration. The designed allocation rules ensure the fairness of rewards for successfully mining\u0000resource. To solve the optimization problem, an intelligent migration algorithm that utilizes a\u0000dual “actor-reviewer” neural network on inverse gradient update is proposed which makes the\u0000training process more stable and easier to converge.\u0000\u0000\u0000\u0000To better meet requirements of the latency, energy consumption and security for computing intensive tasks, an intelligent computing migration mechanism based on blockchain applications is proposed, which considers the factors of resource allocation, resource control and credit degree.\u0000\u0000\u0000\u0000Compared to the existing two benchmark mechanisms, the extensive simulation results\u0000indicate that the proposed mechanism based on neural network can converge at a faster\u0000speed and achieve the minimal total cost.\u0000\u0000\u0000\u0000To satisfy the requirements of delay and energy consumption for computing intensive\u0000tasks in edge computing scenarios, an intelligent, blockchain based task migration assignment\u0000mechanism with joint resource allocation and control is proposed. To realize this\u0000mechanism effectively, a dual “actor-reviewer” neural network algorithm is designed and executed.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"298 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140703769","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":"Extensive Review of Literature on Explainable AI (XAI) in Healthcare\u0000Applications","authors":"Ramasamy Mariappan","doi":"10.2174/0126662558296699240314055348","DOIUrl":"https://doi.org/10.2174/0126662558296699240314055348","url":null,"abstract":"\u0000\u0000Artificial Intelligence (AI) techniques are widely being used in the medical fields or\u0000various applications including diagnosis of diseases, prediction and classification of diseases,\u0000drug discovery, etc. However, these AI techniques are lacking in the transparency of the predictions\u0000or decisions made due to their black box-type operations. The explainable AI (XAI)\u0000addresses such issues faced by AI to make better interpretations or decisions by physicians.\u0000This article explores XAI techniques in the field of healthcare applications, including the Internet\u0000of Medical Things (IoMT). XAI aims to provide transparency, accountability, and traceability\u0000in AI-based systems in healthcare applications. It can help in interpreting the predictions\u0000or decisions made in medical diagnosis systems, medical decision support systems, smart\u0000wearable healthcare devices, etc. Nowadays, XAI methods have been utilized in numerous\u0000medical applications over the Internet of Things (IOT), such as medical diagnosis, prognosis,\u0000and explanations of the AI models, and hence, XAI in the context of IoMT and healthcare has\u0000the potential to enhance the reliability and trustworthiness of AI systems.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"60 S278","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140224184","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":"Face Recognition Using LBPH and CNN","authors":"R. Shukla, A. Tiwari, Ashish Ranjan Mishra","doi":"10.2174/0126662558282684240213062932","DOIUrl":"https://doi.org/10.2174/0126662558282684240213062932","url":null,"abstract":"\u0000\u0000The purpose of this paper was to use Machine Learning (ML) techniques\u0000to extract facial features from images. Accurate face detection and recognition has long been a\u0000problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior\u0000facial descriptor for face recognition. A person's face may make their identity, feelings,\u0000and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized\u0000authentication. Face detection and recognition help increase security; however, the most difficult\u0000challenge is to accurately recognise faces without creating any false identities.\u0000\u0000\u0000\u0000The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution\u0000Neural Network (CNN) to preprocess face images with equalized histograms.\u0000\u0000\u0000\u0000LBPH in the proposed technique is used to extract and join the histogram values into a\u0000single vector. The technique has been found to result in a reduction in training loss and an increase\u0000in validation accuracy of over 96.5%. Prior algorithms have been reported with lower\u0000accuracy when compared to LBPH using CNN.\u0000\u0000\u0000\u0000This study demonstrates how studying characteristics produces more precise results,\u0000as the number of epochs increases. By comparing facial similarities, the vector has generated\u0000the best result.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"21 52","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240419","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 Prospective Metaverse Paradigm Based on the Reality-Virtuality Continuum and Digital Twins","authors":"Abolfazl Zare, Aliakbar Jalali","doi":"10.2174/0126662558294125240307094426","DOIUrl":"https://doi.org/10.2174/0126662558294125240307094426","url":null,"abstract":"\u0000\u0000After decades of introducing the concept of virtual reality, the expansion, and significant\u0000advances of technologies and innovations, such as 6g, edge computing, the internet of\u0000things, robotics, artificial intelligence, blockchain, quantum computing, and digital twins, the\u0000world is on the cusp of a new revolution. By moving through the three stages of the digital\u0000twin, digital native, and finally surrealist, the metaverse has created a new vision of the future\u0000of human and societal life so that we are likely to face the next generation of societies (perhaps\u0000society 6) in the not too distant future. However, until then, the reality has been that the\u0000metaverse is still in its infancy, perhaps where the internet was in 1990. There is still no single\u0000definition, few studies have been conducted, there is no comprehensive and complete paradigm\u0000or clear framework, and due to the high financial volume of technology giants, most of these\u0000studies have focused on profitable areas such as gaming and entertainment. The motivation and\u0000purpose of this article are to introduce a prospective metaverse paradigm based on the revised\u0000reality-virtuality continuum and provide a new supporting taxonomy with the three dimensions\u0000of interaction, immersion, and extent of world knowledge to develop and strengthen the theoretical\u0000foundations of the metaverse and help researchers. Furthermore, there is still no comprehensive\u0000and agreed-upon conceptual framework for the metaverse. To this end, by reviewing\u0000the research literature, discovering the important components of technological building\u0000blocks, especially digital twins, and presenting a new concept called meta-twins, a prospective\u0000conceptual framework based on the revised reality-virtuality continuum with a new supporting\u0000taxonomy was presented.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":"55 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257298","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 Security Analysis Model for IoT-ecosystem Using Machine Learning-\u0000(ML) Approach","authors":"Pradeep Kumar N.S, M. P. Kantipudi, Praveen N, Suresh S, Dr Rajanikanth Aluvalu, Jayant Jagtap","doi":"10.2174/0126662558286885240223093414","DOIUrl":"https://doi.org/10.2174/0126662558286885240223093414","url":null,"abstract":"\u0000\u0000The attacks on IoT systems are increasing as the devices and communication\u0000networks are progressively integrated. If no attacks are found in IoT for a long time, it\u0000will affect the availability of services that can result in data leaks and can create a significant\u0000impact on the associated costs and quality of services. Therefore, the attacks and security vulnerability\u0000in the IoT ecosystem must be detected to provide robust security and defensive\u0000mechanisms for real-time applications.\u0000\u0000\u0000\u0000This paper proposes an analytical design of an intelligent attack detection framework\u0000using multiple machine learning techniques to provide cost-effective and efficient security\u0000analysis services in the IoT ecosystem.\u0000\u0000\u0000\u0000The performance validation of the proposed framework is carried out by multiple performance\u0000indicators.\u0000\u0000\u0000\u0000The simulation outcome exhibits the effectiveness of the proposed system in\u0000terms of accuracy and F1-score for the detection of various types of attacking scenarios.\u0000","PeriodicalId":506582,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091041","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}