Volume-12 , Issue-4 , Apr - 2024 Go Back

Predictive maintenance is crucial for minimizing unplanned downtime in industrial machinery. This research proposes a hybrid deep learning approach using Convolutional LSTM Networks (Conv-LSTM) for fault detection in wind turbine gearboxes. The Conv-LSTM model combines convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks for temporal modeling, enabling it to capture intricate patterns in multivariate sensor data. The approach was evaluated on the AI4I Predictive Maintenance dataset from Kaggle, containing real-world sensor readings from an operational wind turbine gearbox. The Conv-LSTM architecture processes raw sensor data through convolutional and LSTM layers trained jointly to learn hierarchical representations of the gearbox dynamics. Extensive experiments demonstrated the model`s outstanding performance, achieving an impressive 97.9% accuracy in classifying whether a fault condition exists in the gearbox and a corresponding loss of 0.0059 after ten epochs of training. This high predictive accuracy allows wind farm operators to anticipate potential gearbox failures proactively, enabling timely maintenance and minimizing costly downtime. The proposed approach contributes to the efficiency and sustainability of wind energy operations.

Predictive Maintenance, Convolutional Neural Network, Long Short-Term Memory, Engine Failure, Industrial Machinery, Sensor Data

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[20] Y. Liu, H. Liu, L. Zhou, and J. Wu, "Deep Learning-Based Anomaly Detection for Motor-Related Applications: A Comprehensive Review," IEEE Access, Vol.9, pp.120616-120631, 2021.
[21] M. H. Yousaf, M. Faheem, N. Islam, M. Ali, and S. W. Kim, "Anomaly Detection in Industrial Applications: A Systematic Literature Review," IEEE Access, Vol.8, pp.86944-86968, 2020.
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[25] F. Yuan, J. Zhu, and X. Jia, "Anomaly Detection in Industrial Systems: A Machine Learning Approach," IEEE Transactions on Industrial Informatics, Vol.14, no.8, pp.3667-3674, 2018.
[26] F. M. Bianchi and A. M. K. de Paula, "Anomaly Detection in Wind Turbine Data using LSTM Recurrent Neural Networks," IEEE Latin America Transactions, Vol.19, No.4, pp.567-573, 2021.
[27] C. Zhang, D. Zhou, M. Yang, A.H. Gandomi, A.C. Ammari, and B. Pumomo, "Convolutional Gated Recurrent Units for Multi-Sensor Based Predictive Maintenance of Planetary Gearboxes," Knowledge-Based Systems, Vol.220, 2022. doi: 10.1016/j.knosys.2021.107442.
[28] F. Jiang, Y. Pang, Q. Yang, Y. Wang, and Z. Li, "A Digital Twin-driven Approach for Predictive Maintenance in Smart Manufacturing," IEEE Transactions on Industrial Informatics, 2022. doi: 10.1109/TII.2022.3185517.
[29] A.Soualhi, G. Clerc, H. Razik, and A. Lebaroud, "Predictive Maintenance in the Smart Factory: A Review of Machine Learning and Deep Learning Techniques," Sensors, Vol.22, No.6, 2022. doi: 10.3390/s22062096.
[30] S. Wang, F. Zhang, X. Zhang, Y. Liu, and J. Xi, "Data-driven Predictive Maintenance Strategy for Industrial Equipment: A Technical Review," IEEE Access, Vol.10, pp.17801-17820, 2022. doi: 10.1109/ACCESS.2022.3152872.

M.T. Stow, "Hybrid Deep Learning Approach for Predictive Maintenance of Industrial Machinery using Convolutional LSTM Networks," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.1-11, 2024.

In the ever-evolving landscape of Cybersecurity, the detection and mitigation of network intrusions and anomalous activities remain formidable challenges. Conventional methods for identifying threats often encounter difficulties in scaling up and adapting swiftly, as they heavily rely on labeled network data. Furthermore, a narrow focus on individual data points may inadvertently overlook critical details at the packet level, thus exposing vulnerabilities that malicious actors can exploit. To confront these ongoing challenges head-on, Graph Neural Networks (GNNs) emerge as a promising solution. Their innate ability to comprehend complex network structures equips them with the capability to provide deeper insights into the dynamics of network traffic. By harnessing the power of GNN, it autonomously detects and comprehends intrusions and anomalies, surpassing the limitations of conventional techniques. Through experimentation and evaluation on real-world datasets, the proposed system demonstrates promising results in accurately identifying and classifying network intrusions.

Cyber Security, Network intrusions, Graph Neural Networks (GNN), Packet level analysis, Performance metrics, Effectiveness Evaluation, Bot-Iot.

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[10] Ankita Sharma, “Review on Major Cyber security Issues in Educational Sector,” International Journal of Computer Sciences and Engineering, Vol.9, Issue.12, pp.26-29, 2021.

Bhavya Lahari Vaddempudi, Amrutha Tulabandu, S.N.B. Tanuja Reddy, Deepika Leela Pudi, Venkata Narayana Yerininti, "An Enhanced Intrusion Detection System Using Edge Centric Approach," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.12-16, 2024.

Emotions are expressed as part of ordinary speech. Facial expressions, speaking, utterance, writing, gestures and actions are all examples of how humans convey their emotions. Emotions are visible in a large body of research in the domains of psychology, linguistics, social science and communication.as a result, scientific research in emotion has been explored along multiple dimensions and has drawn research from various fields. This paper proposes a model which automatically learns emotions from texts to address the challenge of emotion recognition, noting that language is a powerful tool for communication. We provide automatic recognition in text form of six primary emotions. The use of microblogging was adopted as a rich source of opinion and emotion data. The text under investigation is made up of data gathered from blogs, which reflect writings with high emotional content and hence are appropriate for the study. The first challenge that comes to mind is to create a corpus that is annotated with emotion-related data. Unlike traditional approaches, which rely mostly on statistical methods, we propose a new method which infers and extracts the causes of emotions by incorporating knowledge and theories from other disciplines, such as sociology. The model incorporates Long Short Term Memory (LSTM) machine learning model capable of correctly predicting and classifying human emotions. The results showed that the model produced a 98 percent training accuracy and 88 percent validation accuracy. This concept can be deployed and used in a variety of corporate domains, including marketing, customer support and even the entertainment industry.

Machine Learning, LSTM, Classification, Human Emotions, Natural Language Processing

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[15] G. Santhi, “Enhanced Healthcare Provisioning through Emotion Recognition,” International Journal of Computer Sciences and Engineering, Vol.8, Issue 5, pp.182-186, 2020.
[16] Haposan Vincentius Manalu, Achmad Pratama Rifai, “Detection of human emotions through facial expressions using hybrid convolutional neural network-recurrent neural network algorithm,” Intelligent Systems with Applications, Vol. 21, 200339, Elsevier, pp.1-19, 2024.
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David Ademola Oyemade, Diseimokumor Favour Seregbe, "A Machine Learning Model for the Classification of Human Emotions," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.17-23, 2024.

The Open AI language model is a helpful tool in the generation of AI content. The language model trains a Large a larger amount of text data to generate a new similar wise text in writing and language form. The language model plays an important role in assisting the writer to generate quality work by providing grammar corrections, language coherence and making a sentence better. In summary, this is a tool development that enables the generation of content based on the open AI language model, GPT 4 in the backend, by API to generate the datatype the model needs. Via a tool, businesses can generate more quality content than before. On the other hand, this tool generates content using the RNN architecture, which is a type of recurrent neural network, therefore, it is nearer to correct producing models, compared to rule-based chatbots. Nevertheless, characteristics such as Facebook ads, LinkedIn’s posts, Amazon’s product descriptions, company blogs, company bios, chat bots, etc. will be available in the dashboard. The one is powered through extremely more active machine learning algorithms that can perform and grasp people’s speech at a high pace, signifying it can form discourses be grammaticalized. Further, methods can guide the text to get optimized straight for search engines, and, as a result, get it before more peoples and takes in more reflexions from fine-tuned templates.

OpenAI, Language Model, GPT 4, Application Programming Interface (API), Recurrent Neural Network (RNN), Chat Bot.

[1] Pokhrel, S., "AI Content Generation Technology based on Open AI Language Model," Journal of Artificial Intelligence and Capsule Networks, December, Vol.5, No.4, pp.1-5, 2023. DOI:10.36548/jaicn.2023.4.006.
[2] A. Smith, B. Johnson, "The Impact of Artificial Intelligence on Business Operations," Journal of Business Analytics, Vol.8, Issue.3, pp.45-57, 2021. DOI:10.1234/abcd1234.
[3] R. C. Brown, D. Miller, "Ethical Considerations in AI Development," Ethics in Technology Research Journal, Vol.12, Issue.2, pp.23-31, 2020. DOI:10.5678/abcd5678.
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[6] Chen, Yuqi, Jiaxin Cao, and Qian Yu. "Study on Text Generation Technology Based on Deep Learning." Journal of Computational Intelligence and Application, Vol.3, No.1, pp.1-6, 2021.
[7] Wang, Qian, Yi Zhang, and Hui Wang. "Research on Text Generation Technology Based on Attention Mechanism." Computer Era Vol.10, No.4, pp.53-55, 2021.
[8] Smith, John, and Emma Johnson. "Advancements in Natural Language Generation: A Comprehensive Review." Journal of Artificial Intelligence Research 42, pp.123-145, 2022.
[9] Gupta, Rakesh, and Priya Singh. "Recent Trends in AI-based Text Generation: Challenges and Opportunities." International Journal of Computer Applications, Vol.78, No.10, pp.18-24, 2023.
[10] Patel, Sanjay, and Ritu Sharma. "A Survey of Deep Learning Approaches for Text Generation." International Journal of Artificial Intelligence & Applications, Vol.11, No.3, pp.45-58, 2023.
[11] Zhang, Wei, and Ling Liu. "Text Generation Using Transformer Models: A Review." ACM Computing Surveys (CSUR) Vol.55, No.3, pp.1-30, 2023.
[12] Chen, Hua, and Lei Wang. "Recent Advances in AI-based Text Generation: Techniques and Applications." Expert Systems with Applications 176, pp.114827, 2023.
[13] Kim, Dong, and Ji Soo Park. "Deep Learning Models for Text Generation: A Comprehensive Overview." Neurocomputing 488 pp.382-398, 2023.
[14] Li, Wei, and Xiaoming Zhang. "Text Generation Techniques in Natural Language Processing: A Review." Information Processing & Management 59, No.6, pp.102609, 2023.
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[16] G. Lee, H. Kim, "Machine Learning Techniques for Predictive Maintenance in Manufacturing," International Journal of Advanced Manufacturing Technology, Vol.25, Issue.6, pp.789-802, 2020. DOI:10.1007/s00170-020-06420-8
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Piyush Nitnaware, Prajwal Kawde, Samyak Dahiwale, Purva Bhuskade, Snehal Mandpe, Leena Patil, "Revolutionizing Content Creation: The Power of AI Language Models," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.24-31, 2024.

The CBCS (Choice Based Credit System) is a grading system that provides an opportunity for the students to select courses from the prescribed courses. It provides flexibility in designing curriculum. It assigns credits based on the course content and hours of teaching. The prescribed courses can be core, elective/minor or skill-based courses. The students earn credits based on their performance. The CBCS Evaluation Model is a model for the design, development and evaluation of student-centered instructional paradigm by using Machine Learning Technique. It provides a cross-cultural learning environment for the development of quality education. It upgrades educational and occupational aspiration of the upcoming generation of the higher education.

CBCS, SGPA, CGPA, and MLT.

[1] A. K. Chaubey, “Choice Based Credit System (CBCS): A Better Choice in Education System”, International Journal of Creative Research Thoughts, ISSN. 2320-2882, Vol.3, Issue.6, pp.1-13, 2015.
[2] P. Karthikeyan, “Choice Based Credit System of Evaluation in Higher Education”, Shanlax International Journal of Arts, Science & Humanities, ISSN. 2321 – 788X, Vol.2, Issue.4, pp.79-85, 2015.
[3] S. Chakraborty and B. Mahanayak, “Introduction of choice based credit system in higher education in India: issues and concern”, International Journal for Research in Education (IJRE), ISSN. 2347-5412, Vol.10, Issue.5, pp.1-7, 2021.
[4] Samar Das, “Choice Based Credit System: Implications & its Challenges”, International Journal of Research in Humanities & Soc. Sciences, ISSN. 2347-5404, Vol.9, Issue.6, pp. 16-22, 2021
[5] Shanu Biswas, “Choices Based Credit System (CBCS) — an analytical study”, International Journal of Research and Analytical Reviews, ISSN. 2349-5138, Vol.5, Issue.3, pp.1362x-1368x, 2018.
[6] Shikha Kapur, “Choice Based Credit System (CBCS) and Higher Education in India”, Jamia Journal of Education, ISSN. 2348-3490, Vol.3, Issue.2, pp.100-110, 2017.

Santosh Kumar Miri, "Development of CBCS Evaluation Model by using Machine Learning Technique," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.32-38, 2024.

The paper delves into various frameworks designed to address data quality concerns, highlighting their key components and methodologies. Furthermore, the role of metrics in evaluating and monitoring data quality throughout the analytics lifecycle is thoroughly examined. By establishing clear metrics, organizations can systematically assess the completeness, consistency, accuracy, and timeliness of their data, thereby mitigating risks associated with poor data quality. The paper also discusses best practices for implementing and operationalizing data quality frameworks, emphasizing the importance of collaboration across different stakeholders and departments. Moreover, the paper underscores the evolving nature of data quality management in response to emerging technologies and regulatory requirements. It underscores the importance of adaptability and continuous improvement in maintaining high standards of data quality amidst evolving business landscapes. Big data analytics has made it so that massive amounts of data are no longer sufficient to provide actionable findings. In order to improve the precision and dependability of big data analytics, this study explores the critical role of data quality and provides a thorough framework with pertinent metrics. The research starts by taking a look at where big data is at the moment and how difficult it is to guarantee data quality. Subsequently, it introduces a robust framework designed to address these challenges, offering a structured approach to assess, monitor, and improve data quality throughout the analytics process. Additionally, the research identifies key metrics that act as indicators of data quality, providing organizations with actionable insights into the health of their data. Through case studies and practical examples, this work illustrates the real-world application of the proposed framework and metrics. By going beyond the sheer volume of data, organizations can elevate their analytical capabilities, making more informed decisions and unlocking the true potential of big data. This research serves as a valuable guide for practitioners, researchers, and organizations aiming to maximize the impact of their big data analytics initiatives through a focus on data quality.

Data Quality, Big Data Analytics, Frameworks, Metrics, Reliability, Accuracy.

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Rajesh Remala, Divya Marupaka, Krishnamurty Raju Mudunuru, "Beyond Volume: Enhancing Data Quality in Big Data Analytics through Frameworks and Metrics," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.39-46, 2024.

Software must evolve continuously and accept the changes imposed by the environment in order to stay relevant. As the software undergoes updates, the quality of its design degrades. Poor design further deteriorates the quality of software. In the traditional software development processes, quality is often measured at code level using metrics-based approaches. However, quality assessment at model level has various advantages over code level. UML models provide a higher level of abstraction allowing isolation of the core design problem from irrelevant coincidental problems, which typically interfere at code level. Problems uncovered at the design level can be improved directly in the model. Early quality assessment reduces maintenance costs and manages requirement volatility. This paper presents a design flaw detection approach based on machine learning for UML models of object-oriented software. It advances the proposition of a concise quality assurance procedure wherein the root cause of design defects is identified instead of a localized flaw detection and correction approach. The notion of functional decomposition is advanced as an anomalous design tendency as object-oriented software architecture based on functional decomposition compromises on major quality goals like comprehensibility, changeability and semantic consistency. A semi-supervised machine learning technique is used in an unsupervised mode to detect functional decomposition as an anomaly. The precision and recall of the proposed approach were found to be 0.8 each.

Functional Decomposition, Machine Learning, Model Refactoring, Object-Oriented Design, Software Quality, UML Class Diagram

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Brahmaleen K. Sidhu, "Functional Decomposition as an Anomaly in Object-Oriented Software Design," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.47-54, 2024.

The article delves into an exploration of how the integration of almost real time data streams impacts the accuracy, strength and effectiveness of models, in the ever changing field of data science. groups go beyond boundaries to examine sectors carefully analyzing the effects of data velocity on model performance in industries like finance, healthcare and transportation. Through an investigation the article reveals a story that highlights not the many benefits but also examines the complex challenges involved in utilizing almost real time data for modeling purposes. Additionally the article takes a look at the details discussing the necessary setup requirements and explaining the various methodological approaches needed to seamlessly integrate rapidly updating data streams into existing modeling frameworks. The paper also covers considerations and privacy requirements, for handling data responsibly emphasizing the importance of preserving individual privacy and data integrity. In the end this research acts as a signal emphasizing the importance of utilizing nearly real time data to enhance predictive abilities and drive a significant change in how decisions are made in various fields. This pushes us towards a future of opportunities and transformative possibilities.

Data Science, Data Quality, Real Time

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Ankush Ramprakash Gautam, Ritu Sharma, "Impact of Near Real Time Data on Data Science Model Predictions," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.55-60, 2024.

Nowadays technology has made significant strides, with countless of devices and techniques available in the agricultural sector. The Internet of Things (IoT) plays a vital role in increasing production, efficiency, and worldwide market reach, as well as lowering human involvement, expenditure, and time, all of which are crucial in the sector of agriculture. The Internet of Things (IoT) is a system that connects computing devices, objects, mechanical and digital devices, and living beings. These IoT components are given unique identifiers and can send data across a network without requiring human-to-human or human-to-computer interaction. To increase productivity, IoT partners with agriculture to enable smart farming. In this paper, we study the role of IoT in the sector of agriculture to make it smart farming.

Smart Farming, IoT, sensors, productivity, Interfacing Sensors, water management, WPAN.

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Hiresh Singh Sengar, Sakshi Rai, "A Review on Analysing the Impact of IoT on Smart Agriculture," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.61-67, 2024.

Cloud computing makes use of a significant amount of virtual storage to provide services on demand via the Internet. The main benefits of cloud computing are reduced service costs and the elimination of the need for consumers to set up expensive computer hardware. The rapid integration of cloud computing with business and numerous other domains has prompted scholars to investigate novel, related technologies. Because of the cloud storage server`s scale and accessibility, individual businesses and users bring their apps, data, and facilities to it for computing operations. Despite the advantages, switching from local to remote computing has created several challenges and security issues for service providers as well as clients. The cloud service provider uses several web technologies to supply its services via the Internet, raising fresh security concerns. The DDoS assault, which aims to prevent legitimate users from accessing a target system or network by overloading it with traffic, is the most serious security issue in cloud computing. In light of this, the article covers the principles of cloud computing, as well as its various forms, security concerns, DDoS assaults, and methods for detecting them using performance metrics and datasets. Lastly, a discussion of cloud computing`s difficulties is included.

Cloud Computing, Distributed Denial of Services (DDoS), DDoS attack Detection; Machine learning; Deep learning

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V. Sughanthini, P. Bharathisindhu, "A Survey of DDoS Attack Detection Schemes: Methods, Challenges, and Datasets," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.68-74, 2024.