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

Volume 6 Issue 3 June 2021



1. Study on Anti-Sinking Using Alert and GPS Based Tracking

AUTHOR NAME : Prof. Pradip Balbudhe, Amisha Sahare, Mohini Shende, Sandhya Suranshe, Shubhangi Kularkar, 6Sujata Padvekar

ABSTRACT : Death due to sinking while swimming practice or due to accident kill thousands of people every year. Even the expert swimmer practicing in natural sources may need help to some extend if the swimmer reached his maximum performance level. The proposed idea is to design a novel anti-sinking system, which is based on the airbag system in vehicles. The design will be having a quick reaction system with an auto floating bag inflator and electronically controlled alert system about emergencies along with the GPS location information. This would save thousands of lives from sinking.

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2. Single To Multi-Clouds: Security System

AUTHOR NAME : Sheeldharma Mohan Wahane, Prof. Naziya Pathan

ABSTRACT : The use of cloud computing has increased rapidly in many organizations. Cloud computing provides many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may be untrusted. Dealing with “single cloud” providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards “multi-clouds”, or in other words, “inter clouds” or “cloud-of-clouds” has emerged recently. This paper surveys recent research related to single and multi-cloud security and addresses possible solutions. It is found that the research into the use of multi-cloud providers to maintain security has received less attention from the research community than has the use of single clouds. This work aims to promote the use of multi-clouds due to its ability to reduce security risks that affect the cloud computing user

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3. Study on Emotion Detection Using Machine Learning

AUTHOR NAME : Ms. Ranjana V. Gawai, Dr. V. R. Raut, Dr. S. M. Deshmukh

ABSTRACT : Researchers in psychology, computer science, linguistics, neurology, and allied disciplines are becoming interested in a human-computer interface system for autonomous face recognition or facial expression recognition. An Automatic Facial Expression Recognition System (AFERS) is proposed in this paper. Face detection, feature extraction, and facial expression identification are the three stages of the proposed method. The initial phase of face detection entails detecting skin colour using the YCbCr colour model, lighting correction to achieve homogeneity on the face, and morphological procedures to keep the required face area. Using the AAM (Active Appearance Model) approach, the output of the first phase is used to extract face features such as eyes, nose, and mouth. Automatic facial expression recognition, the third stage, is straightforward. The Euclidean Distance technique is used to calculate the distance between two points. The Euclidean distance between the feature points of the training and query images is compared in this method. The output picture expression is determined using the minimum Euclidean distance. This approach has a true recognition rate of 90 percent to 95 percent. The Artificial Neuro-Fuzzy Inference System is used to further improve this method (ANFIS). When compared to previous approaches, this non-linear recognition system achieves a recognition rate of roughly 100%.

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4. Sentiment Analysis Using Machine Learning Techniques: Systematic Study

AUTHOR NAME : Meena S. Doibale

ABSTRACT : People have always had an interest in what people think, or what their sentiments and opinion is. Since the inception of the internet, increasing numbers of people are using websites and services to express their opinion and sentiments in the form of tweets. With social media channels such as Facebook, LinkedIn, and Twitter, it is becoming feasible to automate and gauge what public opinion is on a given topic, news story, product, or brand. Sentiments that are mined from such services can be valuable. Datasets that are gathered can be analyzed and presented in such a way that it becomes easy to identify if the online mood is positive, negative, and neutral. In this paper, it is summarized about the essential need for sentiment analysis and its challenges. This also explains the sentiment analysis and the process and challenges involved in the sentiment analysis.

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