Volume-12 , Issue-8 , Aug - 2024 Go Back

In this research paper, we present the development and implementation of a cutting-edge Workout Monitoring Robot designed to monitor and guide user’s exercises with its capability of pose estimation and correction and natural language interactions that revolutionize the way individuals engage in exercise routines. Whereas the current research that part-take in similar activities have had much difficulty in flexibility and ease of interaction. This study focuses on enhancing the effectiveness and safety of physical fitness activities by imposing advanced technologies including human pose estimation, autonomous robot navigation and a sophisticated human-computer interface driven by NLP. This research attempts to open the door to a new era of smart and responsive workout assistance, ultimately improving health and well-being.

Human Pose Estimation, Remote Photoplethysmography, Autonomous Robot Navigation, Natural Language Robot Programming

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Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit, "Workout Monitoring Robot: A Robotic Approach for Real-Time Workout Monitoring and Guidance," International Journal of Computer Sciences and Engineering, Vol.12, Issue.8, pp.1-9, 2024.

Agriculture, one of the primary and basic need for living, plays a vital role in the global economy. With growth in newer technology, plants are also more susceptible to new and divergent type of diseases. This type of disease affects the plants leaves and ultimately decreases its yield. This research paper focuses on industrial crop Cotton and food crop Tomato diseased leaf prediction by the framers. It classifies six varieties of cotton leaf diseases and ten varieties of tomato leaf diseases. The approach leverages image processing techniques, transfer learning with CNN techniques and ensemble techniques to classify images of cotton and tomato plant leaves. The main motivation of this research work is to help the farmers predict healthy and infected plant leaves in their farm land with the motivation of implementing sensors in their field. It also encourages future generations to be aware of such diseases in plant leaves and help to eradicate such fungal and viral disease in plants.

Cotton and Tomato leaves, Disease Prediction, Digital Image Processing, CNN, Transfer Learning

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[8] Ali, Anjum, et al. "A Comparative Study Of Deep Learning Techniques For Boll Rot Disease Detection In Cotton Crops." Agricultural Sciences Journal 5.1 Vol.5, Issue.1, pp.58-71, 2023.
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P. Geetha, S. Clement Virgeniya, "Prediction of Cotton and Tomato Leaf Disease using Ensemble Learning Algorithm," International Journal of Computer Sciences and Engineering, Vol.12, Issue.8, pp.10-17, 2024.

The unique characteristics of edge computing, such as limited resources and decentralized architecture, pose distinct challenges to traditional security measures. As the adoption of edge computing continues to proliferate across diverse domains, the security of edge devices becomes a paramount concern. This paper outlines a comprehensive approach for the detection of malicious activities (DDoS, Okiru and PartofHorizontalPortScan) on edge computing devices. The proposed solution leverages a combination of anomaly detection, Recurrent Neural Network (RNN) algorithm, and behaviour analysis tailored to the constraints of edge devices. By considering the specific context of edge environments, the model aims to distinguish between normal and malicious behaviour in edge computing, offering a proactive defence against emerging threats. Furthermore, the integration of threat intelligence feeds enhances the system`s ability to adapt to evolving attack vectors. The efficiency of the proposed solution ensures minimal impact on the performance of resource-constrained edge devices. This paperwork contributes to the ongoing efforts to fortify the security of edge computing ecosystems. By addressing the unique challenges associated with these devices, the proposed RNN algorithm provides a robust and adaptive framework for the detection and mitigation of malicious activities in edge computing, safeguarding the integrity and reliability of edge computing applications with an accuracy of 99.9%.

Edge Computing, Malicious packets, recurrent neural network, Random Forest Classifier

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[2] A. Anand, S. Patil, and P. Kulkarni, "A survey on edge computing security: Threats, attacks, and defenses," Journal of Ambient Intelligence and Humanized Computing, Vol.12, No.5, pp.4871-4891, 2021. https://doi.org/10.1007/s12652-021-03246-6
[3] W. G. Hatcher, J. Booz, J. McGiff, C. Lu, and W. Yu, "Edge computing based machine learning mobile malware detection," 2017.
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[5] H. M. Kim and K. H. Lee, "IIoT malware detection using edge computing and deep learning for cybersecurity in smart factories," Applied Sciences, Vol.12, No.15, pp.7679, 2022.
[6] K. J. Kim and J. H. Kim, "A survey of security threats and countermeasures in edge computing," Journal of Supercomputing, Vol.76, No.8, pp.5834-5864, 2020. https://doi.org/10.1007/s11227-020-03154-5
[7] Y. J. Kim, C. H. Park, and M. Yoon, "FILM: filtering and machine learning for malware detection in edge computing," Sensors, Vol.22, No.6, pp.2150, 2022.
[8] W. Lian, G. Nie, Y. Kang, B. Jia, and Y. Zhang, "Cryptomining malware detection based on edge computing-oriented multi-modal features deep learning," China Communications, Vol.19, No.2, pp.174-185, 2022.
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[11] Y. Shen, S. Shen, Z. Wu, H. Zhou, and S. Yu, "Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination," Journal of Information Security and Applications, Vol.66, pp.103140, 2022.
[12] Z. Tian et al., "Real-time lateral movement detection based on evidence reasoning network for edge computing environment," IEEE Transactions on Industrial Informatics, Vol.15, No.7, pp.4285-4294, 2019.
[13] Y. Wang, L. Zhang, C. Xie, and Q. Wu, "A survey of security and privacy issues in edge computing," IEEE Network, Vol.34, No.6, pp.164-172, 2020. https://doi.org/10.1109/MNET.011.2000184
[14] Y. Yang, Y. Liu, C. Wang, and X. Tang, "A dynamic malicious activity detection scheme for edge computing based on deep learning," IEEE Access, Vol.8, pp.220217-220231, 2020. https://doi.org/10.1109/ACCESS.2020.3048144

Irhirhi M., V.T. Emmah, "Development of a Model for the Detection of Malicious Activities on Edge Computing," International Journal of Computer Sciences and Engineering, Vol.12, Issue.8, pp.18-24, 2024.

In recent times, wind energy is a highly demanded source of renewable energy today. Consequently, global demand for wind energy has increased and thus the construction of wind turbines. However, wind turbines are often met with unfavorable conditions such as highly erratic and variable wind speeds or even storms. This has further consequences such as greatly reducing the efficiency of wind turbines and leaving its body damaged which is economically unfavorable. Particularly, wind speed prediction is a steady variable to consider while looking for viable options to increase the power generation from wind turbines. This paper aims to assess the performance of various machine learning models in time-series wind speed prediction. My hypothesis is that among the machine learning models tested, Random Forest Regression will outperform the others in predicting wind speed. After training and testing the data, I found out that Random Forest Regression had the best performance with a mean squared error of 5.64 and mean absolute error of 1.81. It also had the highest coefficient of determination of 0.68 and supported my hypothesis. Thus, these results show how machine learning models are reasonable tools for wind speed prediction as well as that Random Forest Regression can be used for real-time wind speed prediction after some hyper parameter tuning. This has major implementations as the model can be used to increase the efficiency of wind turbines, improve their safety and help in maintenance planning.

Machine Learning, Wind Speed, Artificial Intelligence, Wind Energy, Sustainability, Renewable Energy

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Nitesh Kothari, "An Investigation into the Applications of Machine Learning Algorithms on Wind Speed Prediction," International Journal of Computer Sciences and Engineering, Vol.12, Issue.8, pp.25-28, 2024.