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Image denoising is essential in digital image processing to improve the visual quality of images corrupted by noise. This study introduces a hybrid model combining fuzzy logic`s adaptive capabilities with genetic algorithms` optimization power for effective denoising. The model leverages fuzzy logic to handle uncertainty and genetic algorithms to optimize denoising parameters. The hybrid model processes images in stages: a fuzzy logic-based filter preprocesses the noise-affected image, guided by fuzzy rules from a knowledge base. Concurrently, a genetic algorithm optimizes the filter`s parameters through evolutionary techniques like crossover, mutation, and selection. The fuzzy logic and genetic algorithm components work together, with the fuzzy logic module using a Mamdani inference system and the genetic algorithm refining the denoised image Experimental results show the hybrid model outperforms traditional methods and standalone fuzzy logic or genetic algorithm approaches. Its adaptability allows it to handle varying noise levels and image content effectively, demonstrating robustness against different noise distributions. This makes it suitable for diverse denoising scenarios. The hybrid model represents a significant advancement in image denoising, highlighting the synergy between fuzzy logic and genetic algorithms. Future work will focus on further optimizations and extensions to improve applicability in real-world scenarios. Overall, this approach enhances noise reduction performance, image quality, and the preservation of important image features.

Image Denoising, Fuzzy Logic, Genetic Algorithm, Datamining and Peak Signal to Noise Ratio.

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Cookey I.B., Bennett E.O., Anireh V.I.E., Matthias D., "A Hybrid Model for denoising in Data Mining and Exploration," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.1-12, 2024.

Machine learning can play an important role in changing the dynamics of the modern healthcare system. In terms of the diagnosis field, Machine learning algorithms have offered tremendous support to Radiologists, healthcare workers, and other decision-makers. Early diagnosis of TB can stop the further spread and eventually mortality rate due to TB will fall. Currently, the standard method that is used for the diagnosis of TB takes one to four weeks while the rapid test takes 24 hours, so using Radiological images has an advantage over the existing standard method. In this paper, we have proposed a Novel Framework based on the application of Deep Learning to detect Tuberculosis (TB) using Chest X-ray images. In this work, 4200 images have been used to train the deep learning model. The model has achieved an accuracy of 99.41% in classifying Normal Chest X-rays and Tuberculosis (TB) Chest X-rays.

Machine learning, Tuberculosis, Deep learning, Chest X-ray, Radiological images.

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Sourabh Shastri, Shiwalika Sambyal, Sachin Kumar, Vibhakar Mansotra, "A Novel Deep Learning Framework for the Detection of Tuberculosis using Chest X-ray Images," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.13-20, 2024.

A great number of individuals are anxious to exploit the internet`s wealth of information. It can be employed to further enhance the existing data. However, the primary challenge lies in uncovering the valuable information that is concealed within HTML elements. This study proposes a framework for web usage mining that examines web server log files using sequential pattern mining approaches. Web log patterns reveal information about user behavior, preferences, and website interactions. Preprocessing of the web data was carried out. The primary objective of preprocessing is to enhance data integrity while decreasing the volume of information that requires evaluation. Prior to inputting the data into the pattern discovery phase, it is necessary to eliminate noise by resolving the challenge of distinguishing between different users and sessions. To identify frequent sequential access in large, low-support data sets, a method for mining sequential patterns is developed. A sequential pattern mining technique identifies recurring sequential patterns in multidimensional web log files with minimum support provided. Multidimensional sequential pattern mining is primarily concerned with enhancing the standard of the patterns the user received back. PrefixSpan algorithm has been used to extract tabular as well as unstructured data from HTML tag. Prefix prunes some web info by calculating the support value at different nodes in the represented projected sub-database and snipe away huge portions of the representation that are guaranteed not to create any outcomes. The system is implemented in Matlab programming language. In the domain of web mining, Matlab has been employed to extract valuable information from the web, including user records and content. When mining extensive sequences containing numerous records, in particular, the method substantially reduces execution time and eliminates enormous memory access costs. The PrefixSpan algorithm enhanced with the starting position and innertagcount parameters has better performance than Markov model and GSP algorithm with execution time of 2.35seconds.

Web Usage Mining, Sequential Patterns, Web Access Pattern, Prefixspan, Web Server logs, Preprocessing

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[16] L. Choudhary, L & S. Swami. Exploring the Landscape of Web Data Mining: An In-depth Research Analysis. Current Journal of Applied Science and Technology, 42(24), pp.32-42, 2023.
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Elliot S.J., Bennett E.O., "Extraction of Sequential Patterns Using PREFIXSPAN," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.21-29, 2024.

This research presents a comprehensive study on the application of Convolutional Neural Networks (CNNs) for precision agriculture, with a focus on the classification of crop and weed species. By leveraging deep learning techniques, we aim to optimize resource management in agriculture, thereby reducing environmental impact and maximizing crop yield. Our study addresses the challenges inherent in current agricultural practices, particularly the need for more efficient methods of classification and population density estimation to optimize fertilizer and pesticide application. We developed a CNN model that demonstrates high accuracy in identifying key crop and weed species, providing a robust tool for data-driven agricultural decision-making. The paper outlines the methodology, experimental setup, and model evaluation, and discusses the interpretation of results, which underscore the model`s potential to revolutionize agricultural practices. The implications for agricultural sustainability are significant, as our automated system facilitates precise and efficient crop and weed identification, contributing to more informed and sustainable farming practices.

Precision Agriculture, Convolutional Neural Networks, YOLO, Transfer Learning, Deep Learning, Crop Classification, Weed Detection, Transfer Learning, Image Processing, Resource Management, Sustainable Agriculture.

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[17] H. Lyu, “Research on Corrosion Recognition Method of Steel Based on Convolutional Neural Network,” 2023 IEEE 6th International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, pp.456-462, 2023.
[18] S. K. Gupta, S. K. Yadav, S. K. Soni, U. Shanker, and P. K. Singh, “Multiclass weed identification using semantic segmentation: An automated approach for precision agriculture,” Ecological Informatics, Vol.78, pp.102366, 2023.
[19] L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning—A Review,” IEEE Access, Vol.9, pp.56683-56698, 2021.
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[21] J. Mendoza-Bernal, A. González-Vidal, and A. F. Skarmeta, “A Convolutional Neural Network approach for image-based anomaly detection in smart agriculture,” Expert Systems with Applications, Vol.247, pp.123210, 2024.
[22] S. M, D. P. Vaideeswar, C. V. R. Reddy, and M. B. Tavares, “Weed Detection: A Vision Transformer Approach For Soybean Crops,” 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, pp.1-8, 2023.
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[30] K. Vayadande, U. Shaikh, R. Ner, S. Patil, O. Nimase, and T. Shinde, “Mood Detection and Emoji Classification using Tokenization and Convolutional Neural Network,” 2023 7th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp.653-663, 2023. doi: 10.1109/ICICCS56967.2023.10142472.
[31] K. Vayadande, T. Adsare, T. Dharmik, N. Agrawal, A. Patil, and S. Zod, “Cyclone Intensity Estimation on INSAT 3D IR Imagery Using Deep Learning,” 2023 International Conference on Innovative Data Communication Technologies and Application (ICIDCA), Uttarakhand, India, pp.592-599, 2023. doi: 10.1109/ICIDCA56705.2023.10099964.

Sachin B. Takmare, Mukesh Shrimali, Rahul Ambekar, "A Deep Learning Approach to Efficient Crop and Weed Classification for Precision Farming," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.30-43, 2024.

The development of a Statistically Verifiable Ideal Standard (SVIS) is achieved with the assistance of the Neyman Pearson speculation testing outline function, where we have built SVIS for different toxins given P(X) (or quantile of request ) where X is the convergence of specific contamination. By an exceedance, we imply that the level of a toxin is more prominent than a given edge esteem put somewhere near the controller. As such, if irregular variable T is the contamination level and U is the given edge esteem then the occasion (T > U) is called an exceedance. With the assistance of this SVIS rule, we will check the consistency status of different observing locales in Dehradun city for which information is gathered by the Uttarakhand Pollution Control Board (UPCB). Locales are Ghanta Ghar, Ballupur Flyover, Prem Nagar Chowk, Raipur Road, Mussoorie Road, Dharampur Haridwar Road

Development of SVIS, Construct SVIS, Construct Power Function, Confidence Interval

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Praveen Kumar Bhatt, Sudesh Kumar, Ankur Nehra, "A Statistical Study of Verifiable Ideal Standard Based on the Expected Number of Exceedances in Dehradun," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.44-49, 2024.

This paper analyzes data acquisition and monitoring techniques used with negative-metal-oxide semiconductor (NMOS) linear image sensors. NMOS sensors have gained significant attention in various fields due to their advantages in terms of sensitivity, dynamic range, and low noise characteristics. Through a systematic review, the paper explores the fundamental characteristics of NMOS sensors, including their operation, sensitivity, and limitations. Data acquisition techniques will cover readout circuits, sensor resolution, timing characteristics and monitoring stratergies. A microcontroller can receive the gathered data and store it for later use

NMOS, Acquisition and Monitoring.

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Abhishek Manoj, Akshay A., Jiya Jamal, Reshmi S., "NMOS Linear Image Sensors: A Review of Data Acquisition and Monitoring," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.50-54, 2024.

Sentiment analysis, a pivotal area within natural language processing, has witnessed significant advancements with the advent of deep learning methodologies. This survey provides a comprehensive overview of the state-of-the-art in sentiment analysis, focusing specifically on the application of deep learning techniques. The aim is to present a thorough exploration of the existing literature, methodologies, and challenges associated with leveraging deep neural networks for sentiment analysis tasks.

Text analysis, Natural language processing, sentiment analysis, prediction, machine learning, and random forests.

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Lakhan Singh, Chetan Agrawal, Pawan Meena, "Deep Learning Based Sentiment Analysis: A Survey," International Journal of Computer Sciences and Engineering, Vol.12, Issue.6, pp.55-63, 2024.