Volume-10 , Issue-5 , May - 2022 Go Back

The present paper is based on Machine Learning Activities using Python programming language. There are various types of Machine Learning Algorithms such as Supervised Learning, Unsupervised Learning and Reinforcement Learning. These already exist in the field of computer programming. Besides these algorithms there is another Deep Learning algorithm which plays a significant role in machine learning devices and is part of Machine Learning methods. The Deep Learning can be used to intelligently analyze the data on a large scale. The paper explores that how Python can be applied in the ML methods? A comprehensive overview on the concerned issues has been illustrated in the study. The present research paper explores the history of machine learning, the methods used in machine learning, its application in different fields of AI. The aim of this study is to transmit the knowledge of machine learning in various fields of AI. In Machine Learning (ML) the knowledge of Artificial Intelligence (AI) is very much essential.

Python, Machine Learning (ML), Machine Learning Algorithms (MLA), Artificial Intelligence (AI), Supervised Learning, Unsupervised Learning, Reinforcement Learning, Framework, Django.

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Meghna Chandel, Sanjay Silakari, Rajeev Pandey, Smita Sharma, "A Study on Machine Learning and Python’s Framework," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.58-64, 2022.

Since, covid19 is affecting many countries in the world therefore, to take the necessary steps in order to control the outbreak or incidence of covid19, which is possible if we know the outbreak or incidence of covid19 which is possible with machine learning. Therefore, in this study we are analyzing the covid19 data of India and performing EDA (exploratory data analysis) and proposing various machine learning algorithm in order to predict the outbreak of covid19. We are using various machine learning algorithms like Linear regression, Gaussian naïve bayes, Decision tree and ensemble learning like random forest, gradient boosting and then finding the best algorithm by comparing their accuracy score. With the help of best algorithm, the outbreak of covid19 to manage the health crisis in each country can be controlled by taking the essential steps.

Covid19,Machine Learning, EDA (Exploratory data analysis), Linear Regression, Gaussian Naïve Bayes, Decision Tree, Ensemble learning, Random forest, Gradient boosting

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Rachana Yadav, Amit Kumar Manjhwar, "Data Based Model for Predicting COVID-19 Incidence Using Data Mining," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.65-73, 2022.

Medicine is a major part of healthcare system and availability of these medicine is a task which all government struggle with, to tackle this selling and buying of medicine was introduced in Indian Market. Online marketing is growing at a great pace and with growth in online marketing more and more online pharmacies are getting in the business. Other than this this industry frequently faces the issue of shortage and wastage of medicine and to tackle this problem an idea could be developed. Online medicine trade is worldwide popular due to the cost cutting it offer and easiness. Though online pharma chains provide everything which include prescribed drugs to machines such as bp monitor, glucometer, etc. India’s health sector is expanding for both online and offline supply stores, both are working together to expands their profit. Scarcity of resuscitative medicines have been turned up in most of country around world. Medicines scarcity cause risks for patient well-being and being as a consequence of no-medication, under-medication and possible medicine errors from attempts to substitute missing medicine. While medicine scarcity is a not something new, they have been climbing alarmingly in recent years. Accessible stats from various healthcare facilities and local non governmental organizations specify that products in shortage include many regularly used medicines such as antibiotics, tb(tuberculosis) medicines, cardiovascular(heart related) medicines and many more.

Medicine, Pharmacies, Shortage, Sell, NGO, Online, Offline, Stats

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Radhika Singhal, Amita Goel, Nidhi Sengar, Vasudha Bahl, "Rahaat – A Web Application Based Donation Platform For Unused/Near To Exprity Medicine," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.74-78, 2022.

A considerably high amount of fruit produced is wasted due to improper management and utilization during harvesting, storing, transporting, and in the food processing industry. Fruit will get rotten easily if not stored properly due to bacteria accumulation. It is known to all that rotten or defective fruits are harmful to health. It may damage the fresh fruits which are in surface contact with the rotten fruits in the inventory. These rotten fruits should be detected and sorted as early as possible. The problem that comes across in manual checking by humans is less uniformity and accuracy as the manual examination by humans’ eyes will consume time and energy. This research proposes a method involving the deep learning technique which is CNN (Convolutional Neural Networks) for feature extraction and classification of rotten fruits. It is one of the applications of image classification problems. This approach uses an RGB channel image of the fruit under examination. The image will be evaluated by the trained model as fresh if the percentage of rotten part detected is under the threshold value. The types of fruits that will be identified and classified in this paper are apple, banana and orange. Transfer learning technique is used, which minimizes training time and resources and aids to achieve higher accuracy. The dataset is divided into two parts, for (70%) training and (30%) validation. The raw image set used for training is first pre-processed and then fed into the model. The validation accuracy obtained in this paper is 98.47%. The total duration of the training stage is 210.37 minutes. Hence, the required time to classify a single fruit image is approximately 0.2 second. Our model can be adopted by industries closely related to the fruit cultivation and retailing or processing chain for automatic fruit identification and classifications in the future.

Deep Learning, Convolution Neural Network, Rotten Fruit Detection, Image Processing, Classification, Inception v3.

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Chandra Prakash Patidar, "Fruit Quality Determination using Image Processing and Deep Learning," International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.79-86, 2022.