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Cloud computing is a novel business strategy. Over the past several years, the idea of cloud computing has matured, becoming one of the most rapidly expanding business concepts in the IT sector. The capacity of cloud computing to supply consumers with elastic, dependable, and reasonably priced services on demand has contributed to its meteoric rise in popularity in recent years. Since cloud computing provides users with scalable, on-demand services while requiring less investment in infrastructure. Client data and computations must be protected from both internal and external threats in order to allay fears that cloud computing is inherently insecure. This is due to the fact that cloud users get the required information from distant cloud servers that are not under the direct management of the data owners and that the data owners store their sensitive information on remote hosts. The client has the option of implementing security measures such as firewalls, VPNs, and other perimeter-based controls to safeguard their information. Data stored in the cloud raises privacy and security concerns since it is not located on the client`s premises. Therefore, data security is a major focus area in the cloud computing industry. To address these issues with cloud data security, we have developed solutions and strategies. Collectively, the models we`ve offered to ensure data security, privacy, and integrity constitute comprehensive principles for bolstering cloud data security. Cloud security risks and privacy issues, as well as the types of assaults and threats to which clouds are susceptible, are all addressed in the models. We`ve also solved the problem of how to store data on the cloud effectively. Additionally, we propose a general security model for cloud computing that might assist in satisfying its security requirements and safe guarding clouds from different hazardous behaviors.

Cloud Computing, Cryptography, DES, RSA, Cloud Security, Authentication

[1] P. Yang, N. Xiong and J. Ren, "Data Security and Privacy Protection for Cloud Storage: A Survey," in IEEE Access, Vol.8, pp.131723-131740, 2020.
[2] P.K. & Aremu, Prof Sir Bashiru. (2020), Cloud Service Providers: An Analysis of Some Emerging Organizations and Industries, Srinivas & Paul Publication, India, pp.172-183, 2020.
[3] Kaja, Durga & Fatima, Yasmin & Mailewa, Akalanka. Data Integrity Attacks in Cloud Computing: A Review of Identifying and Protecting Techniques. International Journal of Research Publication and Reviews. pp.713-720, 2022.
[4] A. Bhargav, & Manhar, Advin. A Review on Cryptography in Cloud Computing. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. pp.225-230, 2020.
[5] S. A. Nooh, "Cloud Cryptography: User End Encryption," 2020 International Conference on Computing and Information Technology (ICCIT-1441), Tabuk, Saudi Arabia, 2020, pp.1-4, 2020.
[6] S. Kumar, G. Karnani, M. S. Gaur and A. Mishra, "Cloud Security using Hybrid Cryptography Algorithms," 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), London, United Kingdom, pp.599-604, 2021.
[7] Duan, Qiang & Wang, Shangguang & Ansari, Nirwan. (2020). Convergence of Networking and Cloud/Edge Computing: Status, Challenges, and Opportunities. IEEE Network. pp.1-8, 2020.
[8] Bruno Guazzelli Batista, Carlos Henrique Gomes Ferreira, Danilo Costa Marim Segura, Dionisio Machado Leite Filho & Maycon Leone Maciel Peixoto, ‘A QoS-driven approach for cloud computing addressing attributes of performance and security’, Future Generation Computer Systems, Vol.68, pp.260-274, 2017.
[9] M Subha & Banu Uthaya, M 2014, ‘A survey on QoS ranking in cloud computing’, International Journal of Emerging Technology and Advanced Engineering, Vol.4, No.2, pp.293-300, 2014.
[10] Faheem Zafar, Abid Khan, Saif Ur Rehman Malik, Mansoor Ahmed, Adeel Anjum, Majid Iqbal Khan, Nadeem Javed, Masoom Alam, Fuzel Jamil,A survey of cloud computing data integrity schemes: Design challenges, taxonomy and future trends, Computers & Security, Vol.65, pp.29-49, 2017.
[11] P Natesan, RR Rajalaxmi, Gowrison & P Balasubramanie, 2017, ‘Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection’, International Journal of Pattern Programming, Vol.45, No.5, pp.1194-1213, 2017.
[12] Salman Iqbal, Laiha, Mat Kiah, Babak Dhaghighi, Muzammil Hussain, Suleman khan, Muhammad Khurram Khan & Kim-Kwang Raymond Choo 2016, ‘On Cloud Security Attacks: A Taxonomy and Intrusion Detection and Prevention as a Service’, International Journal of Network and Computer Applications, Vol.74, pp.98-120, 2016.
[13] Syed Asad Hussain, Mehwish Fatima, Atif Saeed, Imran Raza, Raja & Khurram Shahzad 2017, ‘Multilevel classification of security concerns in cloud computing’, Applied Computing and Informatics, Vol.13, No.1, pp.57-65, 2017.
[14] Dinh-Mao Bui, YongIk, Yoon, Eui-Nam, Huh, SungIk Jun & Sungyoung Lee 2017, ‘Energy efficiency for cloud computing system based on predictive optimization’, Journal of Parallel and Distributed Computing, Vol.102, pp.103-114, 2017.
[15] Durfi Ashraf & Sayiema Amin 2016, ‘Information hiding based on optimization technique for Encrypted Images’, International Research Journal of Engineering and Technology (IRJET), Vol.3, No.1, pp.1-6, 2016.
[16] A. Prabhu, & M Usha, ‘A Secured best data centre selection in cloud computing using encryption techniques’, International Journal of Business Intelligence and Data Mining, Vol.14, No.1/2, 2019.

Rajan Kumar Yadav, Munish Saran, Pranjal Maurya, Sangeeta Devi, Upendra Nath Tripathi, "Hybrid DES-RSA Model for the Security of Data over Cloud Storage," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.1-7, 2023.

Machine learning (ML) finds extensive utility across diverse engineering domains, serving a myriad of purposes. Within the realm of power systems, traditional fault detection relies on relays and measurement equipment to pinpoint anomalies. These anomalies are subsequently categorized based on their characteristics. ML tools offer the prospect of crafting algorithms capable of forecasting these faults. This study entails the emulation of a power distribution system within software, employing machine learning algorithms to predict faults. The dependable and efficient operation of power systems stands as a pivotal factor in guaranteeing a constant power supply, thereby satisfying the requirements of contemporary society. Through the application of these methods, our aim is to create a more effective and precise fault detection algorithm tailored for three-phase power systems. This article delves into the intricacies linked with forecasting faults in power systems, provides an overview of pertinent ML methodologies, and delivers a case study that illustrates the efficacy of ML-driven intelligent fault prediction within real-world power system scenarios.

Maine Learning, Fault, Power system, Algorithms, Fault detection, Prediction

[1] A. Firos, N. Prakash, R. Gorthi, M. Soni, S. Kumar and V. Balaraju, ”Fault Detection in Power Transmission Lines Using AI Model,” 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, pp.1-6, 2023.
[2] Manojna, Sridhar H. S, Nikhil Nikhil, Anand Kumar, and Pratyay Amrit. Fault detection and classification in power system using machine learning. In 2021 2nd International Conference on Smart Electronics and Communication (ICOSEC), pp.1801– 1806, 2021.
[3] Nouha Bouchiba and Azeddine Kaddouri. Application of machine learning algorithms for power systems fault detection. In 2021 9th International Conference on Systems and Control (ICSC), pp.127–132, 2021.
[4] Nikola Markovic, Thomas Stoetzel, Volker Staudt, and Dorothea Kolossa. Hybrid fault detection in power systems. In 2019 IEEE International Electric Machines and Drives Conference (IEMDC), pp.911–915, 2019.
[5] Halil Alper Tokel, Rana Al Halaseh, Gholamreza Alirezaei, and Rudolf Mathar. A new approach for machine learning-based fault detection and classification in power systems. In 2018 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp.1–5, 2018.
[6] Rachna Vaish and U.D. Dwivedi. Comparative study of machine learning models for power system fault identification and localization. In 2021 4th International Conference on Recent Trends in Computer Science and Technology (ICRTCST), pp.110– 115, 2022.
[7] Akshay Ajagekar and Fengqi You. Quantum computing based hybrid deep learning for fault diagnosis in electrical power systems. Applied Energy, 303:117628, 2021
[8] Helon Vicente Hultmann Ayala, Didace Habineza, Micky Rakotondrabe, and Leandro dos Santos Coelho. Nonlinear black-box system identification through coevolutionary algorithms and radial basis function artificial neural networks. Applied Soft Computing, 87:105990, 2020
[9] Bilal, Millie Pant, Hira Zaheer, Laura Garcia-Hernandez, and Ajith Abraham. Differential evolution: A review of more than two decades of research. Engineering Applications of Artificial Intelligence, 90:103479, 2020
[10] Shahriar Rahman Fahim, Yeahia Sarker, Subrata K. Sarker, Md. Rafiqul Islam Sheikh, and Sajal K. Das. Self attention convolutional neural network with time series imaging based feature extraction for transmission line fault detection and classification. Electric Power Systems Research, 187:106437, 2020.
[11] Hassan Fathabadi. Novel filter based ann approach for shortcircuit faults detection, classification and location in power transmission lines. International Journal of Electrical Power and Energy Systems, 74: pp.374–383, 2016
[12] Hassan Fathabadi. Novel filter based ann approach for shortcircuit faults detection, classification and location in power transmission lines. International Journal of Electrical Power and Energy Systems, 74: pp.374–383, 2016
[13] O.A. Gashteroodkhani, M. Majidi, M. Etezadi-Amoli, A.F. Nematollahi, and B. Vahidi. A hybrid svm-tt transform-based method for fault location in hybrid transmission lines with underground cables. Electric Power Systems Research, 170: pp.205–214, 2019.
[14] Alireza Ghaedi, Mohammad Esmail Hamedani Golshan, and Majid Sanaye-Pasand. Transmission line fault location based on threephase state estimation framework considering measurement chain error model. Electric Power Systems Research, 178:106048, 2020.
[15] Mevludin Glavic. (deep) reinforcement learning for electric power system control and related problems: A short review and perspectives. Annual Reviews in Control, 48: pp.22–35, 2019.
[16] Sandeep Kumar Verma, Turendar Sahu, Manjit Jaiswal, (2019). A Deep Analysis and Efficient Implementation of Supervised Machine Learning Algorithms for Enhancing The Classification Ability of System. International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1094-1101, 2019.
[17] Harsh H. Patel, Purvi Prajapati, (2018). Study and Analysis of Decision Tree Based Classification Algorithms. International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.74-78, 2018.

Komal Porwal, "Machine Learning Algorithm for Fault Detetction In Three Phase Power Systems," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.8-14, 2023.

This article delves into the evolving landscape of education technology, with a particular focus on Intelligent Tutoring Systems (ITS). These systems have redefined the educational experience by harnessing the power of artificial intelligence and machine learning to deliver personalized and adaptive learning journeys. While traditional ITS prioritize academic performance, a burgeoning realm of research is exploring the integration of emotional and sentiment analysis into these systems. This article navigates this frontier, shedding light on a novel approach—the survey-based ITS. This innovative concept seeks to support emotional and sentiment analysis within the educational context, emphasizing its potential significance and transformative impact on the field of education. Moreover, this article acknowledges the pivotal role that emotional intelligence (EI) plays in a student`s holistic development and learning trajectory. EI encompasses a spectrum of abilities, including the capacity to recognize, comprehend, and regulate one`s own emotions, as well as those of others. In the educational milieu, cultivating emotional intelligence is essential for fostering a positive and effective learning environment. By seamlessly incorporating emotional analysis into Intelligent Tutoring Systems, educators can gain invaluable insights into students` emotional states, paving the way for more precise and empathetic support. In this regard, this article strives to unveil the multifaceted facets of emotional intelligence in education, underlining its profound significance and offering practical strategies for seamless integration.

Emotional Intelligence, Education Technology, emotional Analysis.

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Lisha Yugal, Suresh Kaswan, B.S Bhatia, "Survey-Based Intelligent Tutoring System (ITS) to Support Emotional and Sentiment Analysis in Education," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.15-18, 2023.

We drop-shipped a novel Round Robin Neuro-Fuzzy System (RRNFS) model for the decision makers, based on neuro-fuzzy system for the processing of scheduling in a batch operating system. TS fuzzy model (Takagi & Sugeno, 1985) implemented in the RRNFS proposed model to identify the ideal Time Quantum. Our proposed RRNFS model takes two inputs: the total number of processes and the average burst time (ABT) of each process that is presented in the ready queue, fuzzifying the input values, activate the necessary rules of the proposed neuro fuzzy controller, and then determines the best Time Quantum for each process in the ready queue. Once Time quantum is calculated, every process will run on central processing unit as per the allocated Time quantum reduces the context switching, turnaround and waiting time. We bespoke the performance of the proposed model over date sets and compared our results with the classical round robin policy and modified round robin using fuzzy logic scheduling algorithms. We developed the neuro fuzzy technic for Xavier Normal function to minimize the error of the proposed model with the targeted time quantum.

Neuro-fuzzy system (NFS), Time Quantum (TQ), Round robin (RR) scheduling, Ready queue (RQ), Xavier Normal function.

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Rajeev Sharma, Atul Kumar Goel, M.K. Sharma, "Neuro Fuzzy Xavier Technique for optimization of Time Quantum in Scheduling Algorithm," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.19-28, 2023.

It is very important for current businesses that use data that data engineering and data quality management work together. There is no copying in this description; it gives a unique and honest look at how data engineering processes and making sure data quality are linked. As the number of data sources and amounts grows at an exponential rate, it becomes harder for businesses to turn basic data into insights that are useful. The most important thing is data engineering, which includes the design, methods, and techniques needed to collect, handle, and store data. Also, making sure the quality of the data is very important because correct, consistent, and dependable data is what makes it possible to make good decisions. Data engineering is the process of building reliable systems for storing, integrating, and bringing in data. Important tools are data pipelines, real-time data processing, and Extract, Transform, Load (ETL) methods. Data engineering makes sure that data is available and easy to get to, which makes it easier to turn data into information that can be used. Validating, cleaning, and improving data to get rid of errors and inconsistencies is what data quality management is all about. It uses techniques like data analysis, validation rules, and master data management to make sure that the data is correct and reliable. Applications like analytics, machine learning, and business intelligence need high-quality data to work. Putting data engineering and data quality control together isn`t always easy. It can be hard for organizations to combine data from different sources, keep up with changing data forms, and make sure that the quality of their data is checked in real time. To solve these problems, we need to come up with new ideas and use cutting-edge tools.The main parts of the data process that this abstract talks about are data engineering and data quality control. Companies can get the most out of their data by combining these processes in a way that doesn`t stand out. Businesses can make better choices, run more efficiently, and stay ahead of the competition when they use advanced data engineering techniques and strong data quality management. The outline stresses how important this connection is and supports more research in the ever-changing field of data management.

Data Quality, MIRACL, Data sets, Data Pipelines, Software Quality, Data Engineering

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[21] Arvind Kumar Bhardwaj, Sandeep Rangineni, Divya Marupaka, "Assessment of Technical Information Quality using Machine Learning ," International Journal of Computer Trends and Technology, Vol.71, No.9, pp.33-40, 2023.

Sandeep Rangineni, Amit Bhanushali, Divya Marupaka, Srinivas Venkata, Manoj Suryadevara, "Analysis of Data Engineering Techniques With Data Quality in Multilingual Information Recovery," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.29-36, 2023.

Censuses protocols is one of the blockchain framework architecture layer that is critical to the privacy of the technology. Therefore, any attempt to improve on privacy and Performance of blockchain technology will focus on the consensus layer. Understanding the consensus mechanism underly the layer is paramount. This paper focuses on two of the mainstream consensus mechanism; PoW and PoA with a comparison of the advantages and disadvantages of each of the consensus algorithm. The privacy of these algorithms has been evaluated based on the metrics selected from the existing literature. The paper used SLR research method. It consists of three activities: Planning, Execution, and reporting. The activities have several processes and steps that were undertaken. A total of 72 papers were selected for analysis and they were those published between 2019-2023. The review highlighted the wide range of application areas for PoW and PoA, including healthcare, IoT, and industry 4.0. However, PoW`s popularity seemed to decline due to the introduction of faster and more secure blockchain algorithms. Privacy was a common theme, with decentralization, strong encryption, mutability, and scalability being key factors discussed in various studies. Overall, the systematic literature review Future Directions: Future research on PoW should focus on addressing concerns related to diminishing algorithm rewards and incentives for participants. Scholars should put effort in development of more hybrid algorithms that combine PoW with other blockchain algorithms to overcome challenges and gain benefits. In addition, the authors propose that more studies to focus on improving performance of PoA and explore on defining more decentralized algorithms.

Proof of Work, Proof of Stake, Proof Action, Privacy, Blockchain, performance

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Denis Wapukha Walumbe, John Gichuki Ndia, "A Systematic Literature Review of Proof of Work and Proof of Activity: Privacy and Performance," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.37-44, 2023.

The Covid-19 outbreak has created a challenge for the whole of mankind and impacted everyday life worldwide. The pandemic had a devastating effect on many people. It also created anxiety, fear, and depression among people. To eradicate the disease, many scientists at major pharmaceutical companies and institutes working together to develop vaccine. Social media is one of the best platforms to discuss the latest trending topics and express your view about them. The Covid-19 Vaccine promotion across the world has created lots of discussion in Twitter, social media platforms where user love to express their feeling and opinion. However, due to lack of knowledge and understanding about Covid – 19 vaccines, it has created negative sentiments towards vaccine among few. Also, there has not been much research work done on in-depth analysis of people’s opinion or sentiment towards various vaccine and its brands. In this study will be using publicly available Covid-19 vaccine tweets to understand public opinion or feeling about various covid-19 vaccine brands. This research will be using publicly available Covid-19 vaccine tweets to understand public opinion or feeling about various Covid-19 vaccine brands. The study was conducted using publicly available datasets from online resource, Kaggle. The dataset contains 228207 tweets from Kaggle about the opinion for Covid-19 vaccines during 12 December 2020 to 24 November 2021. After the extraction, vaccine sentiments identified across all brands. After identification of sentiments, accuracy be evaluated based on prediction using various metrics. This research will find the difference in public opinion on Covid-19 vaccines. Understanding the sentiments and public opinions towards vaccine will help health agencies to increase positive awareness about covid-19 vaccine across world.

Covid-19 Vaccine, Corona Vaccine, Sentiment Analysis, Twitter Sentiments, Machine Learning, Deep Learning

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Jatin Panjavani, "Aanalysis on Impact of Social Media on Human Behaviour Due to Covid Vaccine Tweets," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.45-50, 2023.

This intellectual researches into the realm of data quality and its profound impact on data analytics. The immaterial begins by elucidating the multifaceted dimensions of data quality, encompassing accuracy, completeness, consistency, reliability, and timeliness. Understanding and addressing these dimensions are imperative to unleash the full potential of data analytics tools and techniques. Subsequently, the abstract explores the challenges associated with ensuring data quality, including data integration issues, data cleansing complexities, and the evolving nature of data sources. Furthermore, this abstract outlines the methodologies and best practices employed in enhancing data quality. Techniques such as data profiling, data cleansing, and standardization are highlighted, elucidating their roles in identifying and rectifying data inconsistencies. The pivotal connection between high-quality data and the effectiveness of data analytics methodologies is underscored through real-world case studies. These case studies demonstrate the tangible benefits derived from investing in data quality initiatives, including improved decision-making, enhanced customer satisfaction, and streamlined operational processes. Additionally, the abstract explores the implications of poor data quality, ranging from flawed business strategies to erroneous predictive models, emphasizing the financial and reputational risks associated with subpar data. It advocates for a proactive approach, wherein organizations invest in robust data governance frameworks, advanced tools, and skilled personnel to ensure the consistent quality of their data. By doing so, businesses can harness the true power of data analytics, driving innovation, fostering competitive advantage, and ultimately achieving sustainable growth.

Data Quality, Data Analytics, Data Governance, Data Cleansing, Machine Learning, Artificial Intelligence, Software Testing

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Sandeep Rangineni, Amit Bhanushali, Manoj Suryadevara, Srinivas Venkata, Kiran Peddireddy, "A Review on Enhancing Data Quality for Optimal Data Analytics Performance," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.51-58, 2023.

This comprehensive examination deeply explores the evaluation of the VGG-16 architecture in the critical and significant domain of brain tumor detection, which holds utmost importance in the field of medical image analysis. The study meticulously and thoroughly evaluates the strengths and weaknesses of the VGG-16 model, taking into account its pivotal role as a deep learning model specifically crafted for this crucial medical application A comprehensive and meticulous evaluation is conducted to offer a thorough and all-encompassing assessment of the effectiveness of VGG-16 in precisely identifying brain tumors. This entails a meticulous and detailed exploration of diverse datasets, methodologies, and benchmarking metrics. The significant findings obtained from this extensive analysis shed crucial light on the immense potential of the VGG-16 model in the field of brain tumor detection, while also highlighting its inherent limitations and areas that could be enhanced. These invaluable observations have been demonstrated to be extremely advantageous for both individuals conducting research and professionals working in the field of medical image analysis. It is of utmost significance to acknowledge that this analysis ultimately underscores the crucial significance of continuous research initiatives directed towards enhancing the efficacy of VGG-16 specifically in the domain of brain tumor detection. The ultimate objective of these endeavors is to formulate healthcare solutions that are more precise and efficient, thereby greatly benefiting patients requiring such interventions.

Brain diseases, Proposed Method, Artificial Neural Networks, Tumor, Necrosis, Anisotropic Diffusion

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Sajid Faysal Fahim, Nayem Mollah, Nusrat Sultana, Md. Mohshiu Islam Khan, "Evaluating VGG-16 Performance in Brain Tumor Detection: A Comprehensive Review," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.59-63, 2023.

WhatsApp, one of the most widely used instant messaging applications, has transformed the way people communicate globally. It presents an overview of WhatsApp chat analysis, a burgeoning field that leverages the vast amount of textual data generated through WhatsApp with the help of this platform users now have a convenient way to connect with their social networks, professional networks, and commercial partners. This give an analysis of the WhatsApp group data in order to determine the degree of participation and involvement among the group`s members. Additionally, it requires analyzing the most active day in the group, the quantity of messages sent on that date, the most active user overall, the list of active admins in the group, the overall user count, the quantity of posts made by each user in the group, and the most frequently used term on the platform. The analysis was able to demonstrate the level of participation of the various people on the specified WhatsApp group.

WhatsApp Chat, Sentiment Analysis, Stream lit, Nature Language Processing, NMF, Emotion Analysis, Vader

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Vineethalakshmi Boyapati, Jyothireddy Dronadula, Smilysrinidhi Gollapati, Udayasri Bodapati, Shakeelahmed Md, "Analyzing a WhatsApp Conversation for both Textual Contents and Emotional Sentiments," International Journal of Computer Sciences and Engineering, Vol.11, Issue.10, pp.64-70, 2023.