Volume-11 , Issue-5 , May - 2023 Go Back

Pneumonia primarily affects individuals who are either older than 65 years or younger than five years. Timely identification and prompt treatment of pneumonia can significantly improve the chances of survival for individuals. Pneumonia detection often involves extensive analysis of Chest X-ray images. Recent research indicates that the utilization of deep learning technique holds significant promise in the accurate identification and diagnosis of pneumonia. A novel approach is proposed in this research, where a hybrid Convolutional Neural Network is introduced for the purpose of pneumonia detection in chest X-ray images. In this approach, initially images of Chest X-ray are gathered and preprocessed. Later feature extraction was done using VGG16 and VGG19 model. After training and testing Machine Learning (ML) classifiers, an ensemble classifier was created for classification of pneumonia. Experiment results shows that ensemble classifier outperforms existing state of art methods by exhibiting superior accuracy and recall performance.

Pneumonia,Chest X-ray, VGG16, VGG19, Ensemble Classifier, Convolutional Neural Network

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[9]. Yaseliani M, Hamadani AZ, Maghsoodi AI, Mosavi A., “Pneumonia Detection Proposing a Hybrid Deep Convolutional Neural Network Based on Two Parallel Visual Geometry Group Architectures and Machine Learning Classifiers,” Vol. 10,pp.62110-62128,2022.
[10]. Mabrouk A, Díaz Redondo RP, Dahou A, Abd Elaziz M, Kayed M,. “Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks,” Applied Science, Vol. 12, Issue 13, pp. 1-15, 2022. https://doi.org/10.3390/app12136448
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[12]. Sirish Kaushik V, Nayyar A, Kataria G, Jain R, “Pneumonia detection using convolutional neural networks (CNNs),” International Conference on Computing, Communications, and Cyber-Security, pp. 471-483,2020.
[13]. Bibek Ranjan Ghosh, Siddhartha Banerjee, Ayush Nandi, Arya Panja, Pritam Mondal, Arunava Das, “Automated Covid-19 Detection System with CNN using Chest X-Ray and CT-Scans,” International Journal of Computer Engineering, Vol.10, Issue.5, pp.47-52, 2022.
[14]. Amanze B C, Asogwa D C, Chukwuneke C I, “An Android Mobile Expert System for the Diagnosi of Pneumonia with Object-Oriented Methodology,” International Journal of Computer Engineering, Vol.7, Issue.4, pp.745-758, 2019.

Pradeep Rao K.B., H Manoj T. Gadiyar, Guruprasad, Basavaraj N., Dhanush T.M., Mugodera Madhukumara, "Pneumonia Detection on Chest X-ray Images Using Hybrid Convolution Neural Networks," International Journal of Computer Sciences and Engineering, Vol.11, Issue.5, pp.1-5, 2023.

Over the past many years of continuous research and learning from data, i.e.duplication and Extraction continues to be a spotlight of enormous research. A classification data set with skewed class proportions is referred to as imbalanced. This term originated as a debate over the skewed distributions of binary tasks. Imbalanced data are those datasets that have an uneven distribution of observations across the target class, i.e First class category will have a very higher number of observations while the other class will have less number of observations. The emergence of the massive data era, along with the growth of machine learning and data mining (Data Science), as going deeper into the field of learning with imbalanced datasets, alongside the challenges which are emerging. Data-level methods and algorithm-level methods are repeatedly used and getting improved and popularity of hybrid approaches increased due to the extraction of earlier approaches (data level and algo level) and reduced weaknesses with powerful points. In order to advance the field of addressing imbalanced datasets and compare existing approaches and methodologies, this paper attempts to discuss the open questions and challenges that need to be resolved. This essay discusses each of them and offers ideas for potential directions for further investigation. The main issue with an unbalanced class distribution is when bad training habits cause bias in favour of the majority class. Deep learning algorithms and machine learning algorithms perform training on datasets which are underrepresented in some categories. Conventional methods advise to perform undersampling on majority class category and oversampling minority class category before the learning stage.By including learning modules with clever representations of samples from majority and minority samples, this research investigates various traditional and contemporary strategies to address this issue. The works of several researchers are compiled in a very logical approach and numerical opportunities and also future difficulties for the field`s future research are discussed.

Multiclass, Classification, Imbalance, Prediction, Majority, Minority, Synthetic Minority Over-sampling Technique(smote), Simplified Swarm Optimization(SSO), Particle Swarm Optimization (PSO), Adaptive Synthetic (ADASYN), Diversified One-vs-One strategy(DOVO), Diversified Error Correcting Output Codes (DECOC).

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Shivam Kumar, Deepanshu Ahuja, Sandeep Kumar, "Tackling Imbalance Datasets: Methods, Techniques & Comparisons," International Journal of Computer Sciences and Engineering, Vol.11, Issue.5, pp.6-12, 2023.

Education is a deliberate pursuit aimed at achieving specific goals such as imparting knowledge, developing skills, and fostering moral values. These objectives encompass the advancement of comprehension, rationality, empathy, and ethics. Critical thinking plays a vital role in distinguishing education from indoctrination, as emphasized by numerous studies. Higher education is provided by academic institutions such as universities and colleges, leading to the attainment of degree certificates. Those who pursue tertiary education have better prospects of securing well-paying jobs and forging unique career paths. Furthermore, they are more likely to cultivate profound critical thinking and reasoning abilities that contribute to personal development. The quality of education varies among universities, influenced by the curriculum and teaching methods employed. Therefore, it is essential to carefully consider one`s options and aspirations before making a decision. Given the circumstances, individuals should be prepared for the demanding college admission process, which can be time-consuming.

Higher Education, Linear Regression, Random Forest, Education, Decision Tree, College Prediction, Artificial Neural Network.

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Udayan Iyer, Bhavesh Sonje, Gaurav Suvarna, Bushra Shaikh, "Comprehensive Guide and Assistance System for Future Studies and Academic Pursuits," International Journal of Computer Sciences and Engineering, Vol.11, Issue.5, pp.13-19, 2023.

The oil and gas industries must adapt and modernize their processes to stay competitive, reduce environmental impact, and promote sustainability. This involves investing in artificial intelligence, automation, and data analytics to streamline operations, improve efficiency, and reduce costs. The focus of this paper is the revolutionary impact of Artificial Intelligence technology on the oil and gas industries. However, the technology of Artificial Intelligence has revolutionized the oil and gas industries by automating processes like drilling and production, improving equipment reliability and safety, and providing valuable insights into reservoir management and supply chain management. Besides, there are risks and constraints, such as limited model generalizability and the need for new resources to train new datasets. Thus, using artificial intelligence in engineering can reduce costs and improve quality, but ethical considerations must be taken into account such as job displacement and ensuring the decisions by the artificial intelligence technology align with societal values. Integration of artificial intelligence requires careful examination of technical and ethical factors too. Accordingly, this study also highlights the immense potential of artificial intelligence in the oil and gas industry, while also acknowledging the need for careful consideration and implementation. By leveraging the power of artificial intelligence, we can unlock new discoveries and streamline processes in this vital industry.

Artificial Intelligence, Oil and gas industry, Machine Learning, Intelligent Automation, Algorithm, Maintenance

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Abdulhamid Musa, "Revolutionizing Oil and Gas Industries with Artificial Intelligence Technology," International Journal of Computer Sciences and Engineering, Vol.11, Issue.5, pp.20-30, 2023.

Digital images are an important part of the digital world. Majority of transactions are handled through digital images in place of physical image. Electronic security is also the concerned task for digital images. Data hiding techniques are available through which security can be provided to the images. Digital Image Tampering is one of the issues where the actual content of the original image is lost. To hide the data, Integer Wavelet Transform places an important role which can help in hiding data without loss of content. But during tampering the image content may loss the data which can affect the sub-bands which are generated through IWT transform. If during self-recovery stage, if the LL sub-band is retrieved properly then using Interpolation technique, the image can be enlarged. This paper demonstrates the comparison of Interpolation techniques with respect to LL sub-band of IWT transform. As outcome, it is found that Lancoz3 Interpolation technique is better to use as compare to Nearest Neighbor, Bilinear, Bicubic, Lancoz2 interpolation techniques. The outcomes are measured using PSNR, Sum of Absolute Difference and Average of Absolute Difference.

Interpolation, IWT transform, Lancozs, Bicubic, Bilinear, Nearest neighbor

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Hiral Patel, "Comparison of Interpolation Techniques for enlarging image with LL Sub-band of IWT transform," International Journal of Computer Sciences and Engineering, Vol.11, Issue.5, pp.31-33, 2023.

The current standard of diabetes management depends upon invasive blood pricking techniques. In recent times, continuous glucose monitoring devices have made some improvements in the life of diabetic patients. These proposed techniques have the potential to evolve into a wearable device for non-invasive diabetes management. The conventional Blood Glucose Monitoring (BGM) techniques currently used for the collection of blood samples through finger pricks make the process painful with the risk of infection. In recent years, researchers focus more on making BGM non-invasive under near infrared (NIR) rays. In Intensive Care Unit (ICU) the patients who are critically ill are admitted for the treatment and the Doctors need an all-time update patient’s health related parameters like heart pulse and temperature. Manually doing this is a tedious task for the multiple patients. For this, IOT based system can bring about an automation that keep the Doctors updated all time over the internet. IOT Based ICU Patient Monitoring System is arduino based system which have collects patient’s information with the help of few sensors and uses Wifi module to communicate the information to the internet where the heart beat pulse sensor and heart beat monitor module are electrically connected to the system and physically to be worn by the user. Thus, the doctor can get access to these vital parameters of the patient’s health over the IOT Gecko web interface from anywhere in the world.

ICU- Intensive Care Unit , BGM-Blood Glucose Monitoring, BGL-Blood Glucose Level, CGM-Continuous Glucose Monitoring, mg/dl-Milligrams per deciliter.

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V. Gunavardini, B. Vinodhini, K. Sangeetha, "Blood Glucose Monitoring Using Non-Invasive Method Based On IOT," International Journal of Computer Sciences and Engineering, Vol.11, Issue.5, pp.34-40, 2023.

Now, Communication network (Network may be a wired or wireless network. In wireless network it may be an infrastructure oriented or infrastructure less) plays vital role in the world. . At present without network people cannot do their work easily. Communication Network described as two or more device connecting together and share its resources. If a resource is accessed by more than one person. Network faces lot of issues in its Qualitative and Quantitative of Service. This paper is try to provide solution for infrastructure oriented and less network QoS (Quality of Service) and QoE (Quality of Experience) problems using AI and ML.

Communication Network, Wireless Network, QoS, QoE, AI, ML

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