IJRCIT- International Journal of Research in Computer & Information Technology

Archives of International Journal of Research in Computer & Information Technology(IJRCIT)

Volume 7 Issue 2 March 2022

1. Exploitation of Different Network Services Using Metasploit Framework

AUTHOR NAME : Arvind Nain, Sandeep B Ukey, Anuradha Bhat, Sneha Chobitkar, Govinda Rajle, Mr. Bishwa Ghosh

ABSTRACT : This paper gives an overview of the methodology of penetration testing and the tools used. This authorized attempt to evaluate the security of a network or an infrastructure by safely attempting to exploit the vulnerabilities helps in finding the loopholes in the network. These loopholes may allow an attacker to intrude and exploit the vulnerabilities. Penetration tests can have serious consequences for the network on which they are run. If it is being badly conducted it can cause congestion and systems crashing. In the worst-case scenario, it can result in exactly the thing it is intended to prevent. The exploitation of different network services, this authorized attempt to evaluate the security of a network or an infrastructure by safely attempting to exploit the vulnerabilities helps in finding the loopholes in the network. These loopholes may allow an attacker to intrude and exploit the vulnerabilities. Penetration testing is a well-known method for actively evaluating and assessing the security of a network or an information system by simulating an attack from an attacker’s perspective. A penetration tester must necessarily follow a certain methodology so as to successfully identify the threats faced by an organization’s network or information assets from a hacker and reduce an organizations IT security costs by providing a better return on security investments.

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2. Comparative Study on Plant Leaf Disease Detection and Classification, Based on Machine Learning Techniques

AUTHOR NAME : Prasad W. Bhombe, Dr. Shirish V. Pattalwar

ABSTRACT : Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help reduce the adverse effects of diseases and overcome the shortcomings of continuous human monitoring. To identify the recent advancements in the development of plant disease detection and classification system based on Machine Learning (ML) and Deep Learning (DL) models. An organized way of analysis of various plant disease classification models has been shown in well-formed tables. In this paper, we have conducted a systematic literature study on the applications of the state-of-the-art ML and DL algorithms such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), K-Nearest Neighbor (KNN), Naïve Bayes (NB), other few popular ML algorithms and AlexNet, GoogLeNet, VGGNet, and other few popular DL algorithms respectively for plant disease categorization. Each stated algorithm is characterized through the corresponding processing methods such as image segmentation, and feature extraction, along with the standardized experimental-setup metrics such as total number of training/testing datasets employed, number of diseases under consideration, type of classifier utilized, and the percentage of classification accuracy

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3. Classification of Brain Tumor in MRI Images Based on Artificial Intelligence

AUTHOR NAME : Pooja J. Shinde, Prachi. V. Kale

ABSTRACT : Structural Magnetic Resonance Image (MRI) is a useful technique to examine the internal structure of the brain. MRI is widely used for brain tumor detection as it gives a clear picture of brain soft tissues. Brain tumor identification and classification is a critical and time-consuming task, generally performed by radiologists. Brain tumors of different sizes and shapes can occur in any person. Extraction of exact tumor region and analysis of minute differences is difficult for humans. Digital image processing methodologies like preprocessing, segmentation, and classification are useful to clinical experts for the proper diagnosis of brain tumor types. This paper focuses on current trends in brain tumor detection using MRI images. Analysis of various state-of-the-art machine learning and deep learning-based methods is given. Available datasets and challenges are discussed. This extensive survey will be helpful for future research to develop a better decision support system, beneficial to radiologists for accurate brain tumor diagnosis.

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4. Rise of Rebel

AUTHOR NAME : Rohan Ukey, Vipul Jambhulkar, Sameer Gomkar, Sameera Naaz Warsi, Afreen Kausar, Anukul R. Sharma

ABSTRACT : The “Rise of Rebel” is an open-world RPG (role-playing game) game. Players will be able to play our protagonist “RON” similar to all RPG games, this game is also set in a virtual world full of mysteries. Rise of Rebel is an action-adventure game played from a third-person perspective. The main objective of the game is to tell the stories of our protagonist as well as provide players an experience of all his adventures. As the game is story-driven the objectives and mission in the game will be based on the problems and difficulties faced by our protagonist on his adventure, the player will have to solve the problems to see and experience how the journey unfolds. Games are now considered a useful tool for learning and educational practices. This research will facilitate the developers in understanding the game mechanics of an RPG open-world game. The player may run, jump and fight, etc.

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5. Crop Disease Detection Using Machine Learning

AUTHOR NAME : Vishakha Katole, Rohini khairwar, Avshali Nagpure, Parag Ganorkar, Shilpa D. Chindamwar

ABSTRACT : This project makes use of purpose a large area covered thickly with trees in consider to be the same between sensible and illness flat green part growing from the stem of a plant from the data sets created. The suggested project built in various parts of the application namely dataset, design, feature extraction, instruction of the classifier, and classification. The design datasets of illness and the state of being free from illness leaves are collectively instructed under the purpose of a large area covered thickly with trees to classify the illness and the state of being free from illness picture. To remove with care or effort an important part in a picture we use the Histogram of an Oriented Gradient (HOG). Overall, using machine learning to instruct the big data sets obtained publicly gives us an understanding of how to find the illness existing or occurring now in a living that absorbs substance through its roots and makes nutrients in its leaves by photosynthesis on a colossal scale. Crop disease detection is the main task, particularly for the area with few specialists.

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6. A Study on Different Techniques for Skin Cancer Detection

AUTHOR NAME : Nilesh S. Jadhao, Dr. Ms. S. W. Ahmed

ABSTRACT : The number of cases of melanoma skin cancer has been increasing year by year. Skin cancer is one of the most dangerous types of cancer because its much more likely to spread to other parts of the body if not diagnosed and treated early. About three million people are diagnosed with the disease every year in the United States alone. Early detection of Melanoma skin cancer is very much necessary for the patient for it to be curable. Todays technological advancements can make possible the early detection of skin cancer. As per the literature, the lesion characteristics such as shape, color, structure, etc. are the important parameters for the detection of skin cancer. In this paper, we review the various soft computing and artificial intelligence techniques for early-stage melanoma skin cancer detection.

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7. Planer Object Detection Using Sift and Surf in Image Processing

AUTHOR NAME : Prajakta H. Umale, Chanchal H. Sahani, Aboli S. Patil, 4Anisha A. Gedam, Kajal V. Kawale, Prof. Aditya Turankar

ABSTRACT : Object Detection refers to the capability of computers and software to locate objects in an image/scene and identify each object. Object detection is a computer vision technique that works to identify and locate objects within an image or video. In this study, we compare and analyze Scale-invariant feature transform (SIFT) and speeded-up robust features (SURF) and propose various geometric transformations. To increase the accuracy, the proposed system firstly performs the separation of the image by reducing the pixel size, using the Scale-invariant feature transform (SIFT). Then the key points are picked around feature description regions. After that, we perform one more geometric transformation which is rotation, and is used to improve the visual appearance of an image. By using this, we perform Speeded Up Robust Features (SURF) feature which highlights the high pixel value of the image. After that, we compare two different images and by comparing all features of that object from the image, the desired object is detected in a scene.

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8. Detection Of Medicine Information with Optical Character Recognition Using Android

AUTHOR NAME : Anjali Sawarkar, Ankit Singh, 3Arhat Notey, Monika Dhole, Shubham Shinde, Prof. Aditya Turankar

ABSTRACT : Generally, people dont understand medical terms but need medicines, in this situation OCR is helpful and solves the problem. Healthcare Management is one of the most vital and fastest expanding sectors in the world today. People demand more medicine to deal with stress and other illnesses as lifestyles change. As a result, a huge amount of money is spent on medicines. The resulting medical waste is also enormous. According to the World Health Organization (WHO), the global medical waste rate (HCWGR) is 2.5 kg/bed/day. In India, this rate is 1.55 kg / bed / day. Most of this waste is the medicine we throw away because we dont have any data about them. This medical waste is growing day by day, endangering the planet. With Covid19, the economic situation is more serious than ever. People need to search for paths to save money to improve their financial situation. Our project describes the same issue. We developed an application that identifies medicines using Optical Character Recognition (OCR) in this project. The application will include information such as drug names, available illnesses, side effects, generics, prices, and simple tips.

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9. Government Scheme Monitoring System

AUTHOR NAME : Mr. Noman Khan, Mrs. Afrin Diwan, Mr. Maroof Khan, Mr. Mohammad Saad, Prof. Nazish Khan

ABSTRACT : Rural development generally refers to the process of improving the quality of life and economic well-being of people living in relatively isolated and sparsely populated areas. Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) is considered a "Silver Bullet" for eradicating rural poverty and unemployment, by way of generating demand for a productive labor force in villages. It provides an alternative source of livelihood which will have an impact on reducing migration, restricting child labor, alleviating poverty, and making villages self-sustaining through productive assets creation such as road construction, cleaning up of water tanks, soil, and water conservation work, etc. Which has been considered the largest anti-poverty program in the world. In this paper, based on the secondary data, an attempt has been made to comprehensively understand the development effort to rebuild rural life and livelihood on the basis of various secondary data.

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10. Study on Diagnosis and Prediction of Liver Disease Based on Machine Learning

AUTHOR NAME : Miss M. G. Muthal, Dr. A. B. Gadicha

ABSTRACT : In the healthcare industry, machine learning is critical. Its crucial in computer-assisted treatment. Computer-Aided Diagnosis is a rapidly growing and active research area in the pharmaceutical business. The present generation of computer vision experts guarantees improved disease detection and analysis precision. Systems are given the ability to think through learning and producing data. This approach allows computers to learn on their own without having to be explicitly trained by a developer. Machine Learning Algorithms are used to categorize data sets using a variety of methods. They are Reinforcement, Deep Learning, Supervised, Unsupervised, and Semi-Supervised techniques. The main aim of this paper is to give a comparative analysis of supervised learning algorithms in the medicinal area and a few of the techniques utilized as a part of liver disease prediction.

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