Computer Science, Engineering and Technology最新文献

{"title":"Analysis of High-Performance Parallel Computing using TOPSIS Method","authors":"","doi":"10.46632/cset/2/2/3","DOIUrl":"https://doi.org/10.46632/cset/2/2/3","url":null,"abstract":"High-performance parallel computing involves the simultaneous execution of multiple tasks or processes, orchestrated to achieve improved computational speed and efficiency. This approach leverages the power of parallelism, exploiting both multi-core CPUs and GPUs, distributed computing clusters, and specialized hardware accelerators. The fundamental idea is to divide a task into smaller sub-tasks that can be executed concurrently, thereby reducing processing time and enhancing overall performance. High-performance parallel computing is a transformative approach that enables us to tackle computationally intensive tasks efficiently. This abstract highlight its significance in contemporary computing and sets the stage for further exploration of the intricacies and innovations within this dynamic field. Researchers and practitioners continue to push the boundaries of what is achievable, making high-performance parallel computing a cornerstone of modern computational science and technology. High-performance parallel computing research is of paramount significance due to its transformative impact across diverse fields. It empowers scientists to tackle complex problems that were once computationally intractable, unlocking new frontiers in scientific discovery. It drives innovation in engineering and design, optimizing product development and manufacturing processes across industries. In healthcare, it accelerates genomics research and drug discovery, offering hope for improved medical treatments. Financial institutions rely on it for data analysis and risk assessment, shaping the global economy. Weather forecasting and environmental modelling are enhanced, aiding disaster preparedness and conservation efforts. In the digital age, parallel computing underpins artificial intelligence, enabling advancements in natural language processing and machine learning. Furthermore, it has vital applications in national security, space exploration, and materials science. In essence, high-performance parallel computing research serves as the backbone of technological progress, fostering innovation, efficiency, and problem-solving across a wide spectrum of disciplines, ultimately shaping the future of our world. TOPSIS, this method involves evaluating the geometric distance between each alternative solution and two reference solutions: the positive ideal solution and the negative ideal solution. The underlying principle of TOPSIS assumes that the criteria being assessed are of an ascending nature, where larger values represent better performance. To account for disparate dimensions or scales among the criteria, normalization is often employed within the TOPSIS framework. From the result Scatter- free imaging is got the first rank and object-scatter imaging is having the lowest rank.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"2001 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707592","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":"Human Behavior Analysis Using Machine Learning","authors":"","doi":"10.46632/cset/2/2/1","DOIUrl":"https://doi.org/10.46632/cset/2/2/1","url":null,"abstract":"The study and analysis of human behavior have been longstanding challenges in various fields such as psychology, sociology, and anthropology. With the advent of advanced technologies and the proliferation of data, machine learning has emerged as a powerful tool for deciphering and understanding complex human behaviors. This research aims to explore the application of machine learning techniques in the analysis of human behavior, leveraging diverse datasets and cutting-edge algorithms to gain insights into individual and collective actions. The research methodology involves the collection of multidimensional data sources, including but not limited to social media interactions, physiological measurements, and environmental factors. These datasets are preprocessed and integrated to create a comprehensive representation of human behavior. Machine learning models, ranging from traditional statistical methods to deep learning architectures, are then applied to uncover patterns, correlations, and predictive insights from the data. Machine learning algorithms can analyze patterns in behavior to identify potential signs of mental health issues, enabling early intervention and treatment. Tracking behavioral data can help in managing chronic conditions by providing insights into lifestyle choices, adherence to treatment plans, and overall well-being. Machine learning models can identify unusual patterns of behavior, which is crucial for security purposes. This can include recognizing suspicious activities in public spaces or flagging unusual behavior in online environments. The purpose of this study is to explore the challenges of multiple attribute decision-making when dealing with intuitionist fuzzy information. In this scenario, the attribute weights are not entirely known, and the attribute values are represented by intuitionist fuzzy numbers. To determine the attribute weights, an optimization model is constructed based on the traditional grey relational analysis (GRA) fundamental principles. The proposed method involves calculating the grey relation degree between each alternative and the positive-ideal solution and negative-ideal solution. This degree is then used to define a relative relational degree, which enables the ranking of all alternatives simultaneously with respect to both the positive-ideal solution (PIS) and negative-ideal solution (NIS). From the result 22 is ranked at first position and 45 is ranked at fifth position","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141847911","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":"Evaluation of Explainable Artificial Intelligence using TOPSIS Method","authors":"","doi":"10.46632/cset/2/2/2","DOIUrl":"https://doi.org/10.46632/cset/2/2/2","url":null,"abstract":"Explainable Artificial Intelligence (XAI) refers to the development of AI systems are transparent, explainable and their comprehensible for results can provide explanations or predictions. As AI technologies, particularly machine learning models, become more complex and sophisticated, there is a growing need to ensure that their decisions can be comprehended and trusted by humans, especially health, Finance and such as criminal justice in important domains. Evaluating Explainable Artificial Intelligence (XAI) is essential to ensure transparency, accountability, and user trust in AI systems. Interpretability is a key factor, examining how easily the model's internal mechanisms can be understood. Model transparency, feature importance, and the clarity of visualizations contribute to this aspect. Differentiate between post-hoc and intrinsic explanations, considering whether the model inherently provides interpretable insights. The distinction between local and global explanations is crucial, as it determines whether explanations focus on individual predictions or the overall model behavior. Robustness and consistency are assessed through stability and sensitivity analysis, ensuring that explanations remain reliable across similar instances. Additionally, ethical considerations, such as fairness and transparency in decision-making, must be addressed to uncover and mitigate biases. User feedback and the relevance of explanations to the specific use case contribute to a comprehensive evaluation, fostering the development of XAI systems that are not only technically robust but also ethically sound and user-friendly. The significance of research in Explainable Artificial Intelligence (XAI) lies in addressing critical challenges associated with the adoption and deployment of AI systems in various domains. As AI technologies, particularly complex machine learning models, become integral to decision-making processes in areas such as healthcare, finance, and criminal justice, the need for transparency and interpretability becomes paramount. Topsis involves optimizing from an advantageous standpoint by simultaneously minimizing the distance to and maximizing the distance from a reference point, which is defined in relation to solutions within a set of alternative options and numerous identification criteria. The importance of Topsis criteria lies in the potential to integrate comparative weights. This study conducts a comprehensive review of Topsis, exploring various weighing schemes and employing different distance measurements. Numerous applications of Topsis are examined, particularly its utilization in comparing results for a diverse set of multiple criteria data with varying weights. Interpretable Machine Learning Models, Human-Centric Design in XAI, Ethical Implications of XAI, Industry-specific Applications of XAI and Hybrid Approaches for Model Interpretability. Interpretability Metrics, Human-Subjective Evaluation, Algorithmic Robustness and Real-wor","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"25 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141693607","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 Natural Language Processing (NLP) through VIKOR Method: A Comprehensive Approach for Improved Computational Linguistics","authors":"","doi":"10.46632/cset/2/1/4","DOIUrl":"https://doi.org/10.46632/cset/2/1/4","url":null,"abstract":"Natural Language Processing (NLP) is a revolutionary discipline that resides at the crossroads of computer science and linguistics, with its primary emphasis on the interaction between computers and human language. With roots in artificial intelligence, NLP seeks to equip machines with the ability to comprehend, interpret, and respond to natural language, enabling more intuitive and meaningful communication between humans and computers. This multidisciplinary domain leverages advanced algorithms and models to tackle a range of linguistic challenges, from language translation and sentiment analysis to speech recognition. Recent breakthroughs, particularly in deep learning and neural networks, have propelled NLP to new heights, with applications spanning diverse sectors such as healthcare, finance, and education. As NLP continues to advance, its potential impact on enhancing human-machine interaction and information processing is increasingly evident, promising a future where technology seamlessly integrates with our natural modes of communication. The significance of Natural Language Processing (NLP) in research lies in its capacity to revolutionize human-computer interaction. NLP empowers machines to understand and generate human language, enabling advancements in sentiment analysis, language translation, and conversational AI. This transformative capability has profound implications across various industries, including healthcare, finance, and education. NLP research not only enhances the efficiency of information retrieval and processing but also holds the key to developing more intuitive and user-friendly technologies. As the frontier of NLP research expands, its potential to bridge the communication gap between humans and machines continues to shape the future of technology and information access. VIKOR functions as a multi-criteria decision-making technique that determines the best option by comparing it to the ideal option. The ranking procedure comprises determining distances concerning the optimal solution. VIKOR uses linear normalization to get the best possible outcomes. This technique was first presented by Opricovic in 1998 and was intended to optimize multi-attribute complex systems. Its main focus is on ranking lists that allow for flexibility in the strategy weight interval and incorporate compromise alternatives to obtain desired results. Alternative taken as Open AI GPT-4, Google BERT, Microsoft Azure Text Analytics, IBM Watson Natural Language Understanding, spaCy, NLTK (Natural Language Toolkit), Amazon Comprehend. Evaluation Parameter taken as Sentiment Analysis (F1 Score), NER Performance, Language Support, Processing Speed (Response Time) (ms), Translation Accuracy (BLEU Score), Data Privacy. The result it is seen that Amazon Comprehend is got the first rank where as is the IBM Watson Natural Language Understanding is having the lowest rank.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"26 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140240159","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":"Exploration of Facial Emotion Detection Systems Utilizing Convolutional Neural Networks: A Comprehensive Review","authors":"","doi":"10.46632/cset/2/1/3","DOIUrl":"https://doi.org/10.46632/cset/2/1/3","url":null,"abstract":"Facial emotion detection systems have witnessed significant advancements, particularly with the utilization of convolutional neural networks (CNNs). This paper provides a thorough survey of such systems, beginning with an introduction to artificial intelligence and the evolutionary trajectory of neural networks, including artificial neural networks (ANNs), recurrent neural networks (RNNs), and CNNs. The paper elaborates on CNNs' architecture and functionality, elucidating key components such as convolutional layers, pooling layers, and fully connected layers, while also spotlighting prominent CNN architectures like AlexNet and ResNet. It delineates the broad scope and diverse applications of facial emotion detection systems across various domains, including marketing research, crowd testing, AI robots, banking, and entertainment. In the literature review section, recent research papers on CNN models for facial expression recognition are synthesized, highlighting variances in datasets, methodologies, and accuracy levels. The paper concludes that CNNs represent the current pinnacle of facial emotion classification techniques, surpassing previous methodologies such as eigenfaces. It underscores the efficacy of deep CNN architectures trained on extensive facial image datasets in proficiently identifying emotions from facial expressions. Moreover, the paper emphasizes the necessity for ongoing endeavors to enhance accuracy, particularly concerning complex emotions like disgust. In essence, CNNs exhibit substantial promise for the development of real-world facial emotion detection systems, heralding a new era of sophisticated emotion recognition technology.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"48 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140259078","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":"Rural Edurevamp","authors":"","doi":"10.46632/cset/2/1/2","DOIUrl":"https://doi.org/10.46632/cset/2/1/2","url":null,"abstract":"In today's digital age, access to quality education remains a pressing issue, especially for students in rural areas who face numerous challengesin pursuing their academic aspirations. This paper endeavors to address this educational divide by developing a comprehensive application “Rural EduRevamp” tailored to the unique needs of rural students. The core objectiveis to provide these students with essential study materials, a learning environment and a platform for participating in assessments. This multifaceted application is designed to level the educational playing field, ensuring that rural students have accessto the same resources and opportunities as their urbancounterparts. By granting students access to study materials through a user-friendly interface, we aim tobreak down geographical barriers and enhance the learning experience. Furthermore, the application incorporates a virtual assistant to\u0000maintain a supportive and engaging learning environment. This real-time oversight in the form of a virtual assistant helps students stay on track and receive the aid they need to excel in their studies. Additionally, the application facilitates assessments, enabling students to demonstrate their knowledge and progress, therebyfostering a culture of continuous learning andimprovement. In summary, this application for rural\u0000education seeks to empower students by providing them with study materials and assessment tools, all aimed at improving educational outcomes and offering a path to a brighter future. This paper represents a significant step towards achieving equityin education.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"10 S12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140266391","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":"Classifying Abnormalities In Heart Beat Sound","authors":"Y. Befina, A. Chandrasekar","doi":"10.46632/cset/2/1/1","DOIUrl":"https://doi.org/10.46632/cset/2/1/1","url":null,"abstract":"Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure. in an early stage. In the proposed method various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (Melfrequency cepstral coefficients). Then, a Deep Learning Approach is applied in which the MFCC audio signals are sent to the CNN (Convolutional Neural Network) + LSTM (Long Short-Term Memory) model to extract and classify the abnormal heartbeat sounds with its associated disease. Thus, a system for classifying abnormal heartbeat sounds is proposed by leading a way to identify and diagnose diseases that are identified using heartbeat sounds in an early stage.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"31 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140265780","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":"Breast Cancer Metastasis: Markers and MCDM Models","authors":"","doi":"10.46632/cset/1/2/5","DOIUrl":"https://doi.org/10.46632/cset/1/2/5","url":null,"abstract":"Breast Cancer, The most common type of disease and related to cancer in women the main cause of death is a major concern. Breast cancer metastasis, rather than the primary tumour, is what kills most people with breast cancer. In 2017, there were over 250,000 new cases of breast cancer found in the country, and roughly 12% of American women will be diagnosed with the disease at some point in their lifetime. Breast cancer may spread to neighbouring lymph nodes and distant organs after developing locally at first. Prognostic indicators are especially utilised to analyse community breast cancer screening and to evaluate systemic disease progression at primary diagnosis. The topic of organ involvement in metastasis and current and new approaches for identifying it are reviewed.The goal of this thesis is to address Scale weights relate to GRA numerals with interval values. The Gray Relational Analysis (GRA) method is an extension of MCDM problems using unknowable information. It simply establishes several optimization models based on the fundamental principle of the conventional GRA approach, the determination of scale weights. Non-specific invasive ductal carcinoma, aggressive lobular cancer, lobular and ductal characteristics, mixed type, Mucinous carcinoma, and Medullary carcinoma taken this alternative in this method and evaluation parameters is lung, pleura, liver, bone, adrenal glands, gastrointestinal tract, skin, brain, pancreas and kidney. Traditional from this analysis Basic idea of GRA method Determines the long-range solution from the short-range and negative-best solution, but the comparison of these distances is not considered significant. As a result, Medullary carcinoma has been ranked first; Similarly, Ladakh is ranked low. This paper showing, Mucinous carcinoma is low affect diseases and Invasive ductal carcinoma, not otherwise specified is most affect in breast cancer.The hypothesis that breast cancers may naturally possess the ability to metastasize is supported by new molecular tools like DNA microarrays. These findings have significant ramifications for metastatic research and prognosis prediction.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"44 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533374","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":"Radio resource management Satellite Communication Networks MCDM Method","authors":"","doi":"10.46632/cset/1/2/4","DOIUrl":"https://doi.org/10.46632/cset/1/2/4","url":null,"abstract":"A communications satellite would be an artificial satellite that creates a channel here for the transmission of a communication through a transponder between a sender and a recipient located at various locations on Earth. Communications via satellite are used in telecommunications, radio, broadcast, computer, and military applications. Satellites offer three different kinds of communication services: data communication, broadcasting, and telephone. Lower Geostationary satellite, Networks for Low-Earth-orbiting (LEO) and Medium Eccentricity Orbit (MEO) are the three different types of satellite networks. A satellite system or a portion of a satellite system with only one satellite in it and a working ground station is known as a satellite network. Models 1, 2, and 3. A satellite network is a system that uses satellite technology to transmit media services to recipients. The two main components of communication systems are the ground component, which consists of stationary or mobility transmissions, receptions, and ancillary equipment, and indeed the space component, which primarily consists of the satellite. In addition to offering in-flight communications, satellites are frequently the primary voice communication method in rural areas and places where telephone connections have been disrupted during a catastrophe. Satellites are also a major source of time for cell phones and pagers. With nine operating communications satellites in geo-stationary orbit, one of the largest domestic satellite communications networks in the Asia-Pacific region is the Indian National Television (INSAT) system. Radio and cable television are provided to us. They allow us to use our mobile phones to make long distance calls. So that we can find ourselves and get directions, they offer us the Global Positioning System (GPS) our destinations. Satellites offer data on Earth's atmosphere, seas, land, and clouds. They can also see smoke, volcanoes, and forest fires. All of this data aids in weather and climate prediction. Farmers benefit from knowing what crops to plant. PROMETHEE (Priority Ranking System Method for Enrichment Assessments) About PROMETHEE methods and usage to uncover current research to classify and explain A classification scheme and a comprehensive literature review is presented. Alternative is Laser communication, optical networks, satellite optical communication, vibrations and satellite network. Evaluation parameters are Solar Radiation Pressure, Thermal Bending, Micro Meteorite impacts, Solar and Lunar Gravity and Earth oblations effects. Vibrations are got the first rank whereas is the Optical networks are having the Lowest rank. Satellite Communication Networks for using the analysis of PROMETHEE Method. Vibrations is got the first rank whereas is the Optical networks is having the Lowest rank.","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139532648","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":"The survey of GSM Wireless Data Communication System using the SPSS Method","authors":"","doi":"10.46632/cset/1/1/3","DOIUrl":"https://doi.org/10.46632/cset/1/1/3","url":null,"abstract":"Wireless data communication using the radio spectrum, a technique called transmits signals into the environment. It can be used in one- or two-way networks using Ethernet cables or vast area networks, which may carry analogue or digital communications Infrared and radio frequency electromagnetic waves that resemble satellites are used in wireless communication technologies to send data over the air. As an illustration, consider Bluetooth, Wi-Fi, GPS, television broadcasting, wireless computer parts, and wireless phone networks like 3G and 4G. In order Wireless communications are crucial for billions of people to connect to the Internet and benefit from the contemporary digital economy. Similar to this, universally recognized guidelines for mobile phones enable individuals to use their gadgets wherever in the world. Faster information flow within firms and between customers and suppliers is a result of improved data communications. For instance, during sales calls, salespeople can immediately check inventory level and prices. In order for billions of people to use Wireless connections are crucial if you want to use the Internet and benefit from the current digital economy. Similar to how customers can also use cellphones anytime over the world according to established standards. Introduction throughout the last few decades, information and communication have changed quickly, and these innovations will have an impact on society over the coming five years. Personal lifestyles, which include an increase in cellphone use also social network usage, are one result. The topic of patient-health monitoring, which has drawn a lot of interest from researchers lately, is one in which wireless personal area networks (WBANs) are anticipated to play a significant role in the future. Creating a highly secured architecture amongst sensors and users while addressing pervasive privacy and security worries is one of the problems. In this paper, we suggest a framework for BAN communication and develop a method to protect implanted or wearable data transmission. Data security is ensured by sensors and data sinks/consumers (such as doctors or nurses) utilizing cipher text-policy attribute-based encryption. To save data in symmetric encryption format at the data sink, use (CP ABE) and a signature. O modifications enable the development of cloud infrastructure and the application of big data management. One more result of being in a classroom. In the twenty-first century, the classroom is virtual and collaborates to let both learners and instructors to attend courses utilizing online devices. Wang, Y., Lin, H., and Li, C. (2016). Due to its quick growth and popularity, digital literacy is becoming more and more significant in the educational setting. SPSS statistics is a multivariate analytics, business intelligence, and criminal investigation data management, advanced analytics, developed by IBM for a statistical software package. A long time, spa inc. Was created by, IB","PeriodicalId":515267,"journal":{"name":"Computer Science, Engineering and Technology","volume":" 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139626211","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}