http://localhost:8080/xmlui/handle/123456789/3420 Tue, 23 Jun 2026 06:27:55 GMT 2026-06-23T06:27:55Z http://localhost:8080/xmlui/handle/123456789/3425 Title: Detection of Anomalies using Local Outlier Factor and Isolation Forest algorithm Authors: Nimani, Sunny; Khairnar, Mahesh; Khele, Prathamesh; Patil, Vishal; Banait, S.S. Abstract: : Data generated from smart devices or applications are in time-series format, in which information is recorded for each specific time. Anomalies in log data refer to certain patterns or points in data that deviate from average data. Anomaly detection is concerned with identifying data patterns that deviate remarkably from the expected behavior. This is an important research problem, due to its broad set of application domains, from data analysis to e-health, cybersecurity, predictive maintenance, financial fault prevention, and industrial automation. Efficiency of Local Outlier Factor Algorithm, Isolation Forest Algorithm is compared. Testing dataset is obtained from Indian Council of Medical Research (ICMR) and credit card company transactional data. Sun, 05 Jun 2022 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3425 2022-06-05T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3424 Title: Underwater Marine Life Detection Using Image Processing Authors: Joshi, Yash; Desale, Rutik; Dixit, Sairaj; Jadhav, Malhar; Mahajan, Monali Abstract: Marine life research and computer technology have been utilized in tandem for the study of aquatic ecosystems and the analysis of ocean floors throughout the last few decades. Few modern solutions have been offered in this field in recent years. The work in object detection and recognition based on machine learning models have given good information about the surroundings and behavior of marine ecosystems. These models are complex in usage, they often rely on the information source from multiple data forms. The major task is to remove the high impurities in underwater images as the noise removal process is difficult. The image extraction is carried out using darknet which helps in proper object detection. Due to this, the actual applications and study of marine life is realized easily. A suitable environment will be created so that machine learning algorithms such as YOLO will be used to detect and recognize the animals under the ocean with the help of image processing Mon, 20 Jun 2022 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3424 2022-06-20T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3423 Title: Speech Emotion Recognition using MLP Classifier Authors: Wagh, Roshan; Gade, Yash; Wagh, Abhishek; Bansod, Amon Abstract: As human beings speech is natural way to express ourselves. Humans depend so much on it. Emotions play a important role in communication . Detection and analysis of emotion is very important in today’s digital world.Emotion detection is a challenging task. There is not a general agreement on how to measure or categorize them. Speech Emotion Recognition process and classify speech signals to detect emotions embedded in them. Speech Emotion Recognition system can be used in various areas.The application area are like interactive voice based-assistant , caller agent conversation analysis,security and other fields. This System attempts to detect emotions in audio file passed by analysing the acoustic features. System uses MLP Classifier to classify the emotions from the given wave signal. RAVDESS dataset will be used .The features to be extracted from the audio input provided will be attracted by these five parameters which are as follows, MFCC, Contrast, Mel Spectrograph Frequency, Chroma and Tonnetz. Wed, 15 Jun 2022 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3423 2022-06-15T00:00:00Z http://localhost:8080/xmlui/handle/123456789/3422 Title: Waste Classification Authors: Patil, Digambar; Rathi, Ashutosh; Banait, S.S.; Ugale, Rutuja; Bhutkar, Sakshi Abstract: A large amount of solid waste is generated in urban areas with a variety of types like plastic, garden waste, paper, glass, etc. For efficient waste management it is necessary to treat different types of waste in a different manner. In order to achieve this, waste must be separated into various categories. Thus, the concept of segregating wet and dry waste has been introduced by the government. By following the guidelines given by the government, a huge amount of budget for waste segregation is saved and can be used for further waste management. Keeping all of this in mind, the proposed system aims to classify wet and dry waste based on the captured image of the waste. The captured image of waste is passed through the system to classify the type of waste. This can help us get data relating to a variety of waste types. Furthermore, it can help analyze the waste disposal habits of people at different locations, which can help create awareness in places where improvement is required Mon, 20 Jun 2022 00:00:00 GMT http://localhost:8080/xmlui/handle/123456789/3422 2022-06-20T00:00:00Z