Real-World Anomaly Detection in Video Using Spatio-Temporal Features Analysis for Weakly Labelled Data with Auto Label Generation

Detecting anomalies in videos is a complex task due to diverse content, noisy labeling, and a lack of frame-level labeling. To address these challenges in weakly labeled datasets, we propose a novel custom loss function in conjunction with the multi-instance learning (MIL) algorithm. Our approach utilizes the UCF Crime and ShanghaiTech datasets for anomaly detection. The UCF Crime dataset includes labeled videos depicting a range of incidents such as explosions, assaults, and burglaries, while the ShanghaiTech dataset is one of the largest anomaly datasets, with over 400 video clips featuring three different scenes and 130 abnormal events. We generated pseudo labels for videos using the MIL technique to detect frame-level anomalies from video-level annotations, and to train the network to distinguish between normal and abnormal classes. We conducted extensive experiments on the UCF Crime dataset using C3D and I3D features to test our model\u27s performance. For the ShanghaiTech dataset, we used I3D features for training and testing. Our results show that with I3D features, we achieve an 84.6% frame-level AUC score for the UCF Crime dataset and a 92.27% frame-level AUC score for the ShanghaiTech dataset, which are comparable to other methods used for similar datasets

Automated bacteria genera classification using histogram-oriented optimized capsule network

Identifying the nature and type of bacteria is essential in diagnosing various fatal diseases and their treatments. Biologists classify bacteria using morphological characterization from microscopic images according to their color and shape information. Therefore, an automated bacterial recognition and classification approach is required compared to a challenging and time-consuming manual process. Much research has been carried out on bacteria classification using machine learning algorithms. However, the major weakness of these conventional machine learning algorithms is that they cannot differentiate pose and deformation, have significant trainable parameters, require extensive training time, and use trial-and-error-based hyperparameter selection. In addition, the choice of optimization function to reduce the loss is also equally important. This paper presents an efficient capsule network that encodes information from orientation as an alternative to these machine learning models. The proposed model is designed with histogram-based feature sets requiring minimal parameters. Various optimization algorithms are tested to find an appropriate optimizer. 33 categories of bacteria species are classified using the proposed method. A comprehensive analysis of popular gradient descent optimizers is presented with a capsule network to strengthen testing and validation and benchmark the performance. The extensive empirical study on DiBAS datasets demonstrates that the proposed network performs 95.08% efficiency among various machine learning algorithms, including KNN, Decision Trees, Naïve Bayes and SVM. Furthermore, the proposed model achieves better accuracy with the least training parameters of 6.9 million

Temperature control of shell & tube type Heat exchanger By using Twin CAT PLC

Abstract:-In many industrial process and operations Heat Exchanger is one of the important unit for the transfer of thermal energy. There are different types of heat exchangers used in industries, the shell and tube type heat exchanger is most common. The main purpose of heat exchanger is to maintain fluid at specific temperature conditions, which is achieved by controlling the exit temperature of one of the fluids. Here we have presented an automation system that is composed of Heat exchanger, Twin CAT PLC and sensors. To control the tube outlet temperature of the fluid of heat exchanger system Twin CAT PLC used. The designed controller regulates the shell outlet temperature of the fluid to a desired set point in the shortest possible time.SCADA system is used to give a virtual display of the proposed process. The end system is a fully automated for the transfer of energy in industrial applications

Abstracts of Scientifica 2022

This book contains the abstracts of the papers presented at Scientifica 2022, Organized by the Sancheti Institute College of Physiotherapy, Pune, Maharashtra, India, held on 12–13 March 2022. This conference helps bring researchers together across the globe on one platform to help benefit the young researchers. There were six invited talks from different fields of Physiotherapy and seven panel discussions including over thirty speakers across the globe which made the conference interesting due to the diversity of topics covered during the conference. Conference Title:  Scientifica 2022Conference Date: 12–13 March 2022Conference Location: Sancheti Institute College of PhysiotherapyConference Organizer: Sancheti Institute College of Physiotherapy, Pune, Maharashtra, Indi