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

Profile of confirmed H1N1 virus infected patients admitted in the swine flu isolation ward of tertiary care hospitals of Baroda district, Gujarat, India

Background: Influenza is truly an international disease. It occurs in all countries and affects millions of people every year. The Influenza A H1N1 in humans can be a mild illness or in some people it may result in serious, even life-threatening complications such as pneumonia, acute bronchitis, worsening of chronic conditions, respiratory failure and death. Objective: To study profile of confirmed H1N1 virus infected patients of Category “C” admitted in the swine flu isolation ward of tertiary care hospitals of Baroda District, Gujarat, India.Methods: This was a cross sectional observational study carried out in Baroda district of Gujarat state, India. All confirmed H1N1 virus infected 54 patients in Category “C” admitted in the swine flu isolation ward of both Government and private hospitals of Baroda district during the period of 1st January to 30th June, 2013 after taking verbal and written consent of the patients were enrolled in study. Before conducting the study approval was obtained from institutional ethical committee for human research. Data safety and confidentiality was also given due consideration. A predesigned semi-structured Performa was used. Detailed demographic and clinical data were recorded. Data was statistically analyzed using SPSS software (trial version).Results: Out of total 54 influenza A H1N1 cases, 23 patients (42.59%) were males. 4 (12.91%) female patients were pregnant. Majority (75%) of the cases were between 21-50 years of age group. Majority (90.7%) of the patients were from urban areas. Majority cases (94.4%) presented with cough, followed by  36 cases (66.7%) exhibiting high grade fever, 35 Cases (64.8%) had complain of breathlessness and 25 cases(46.3%) presented with sore throat. 19 cases (35%) had co-morbid condition with the influenza A H1N1 disease. In this study among patients with associated Comorbid condition, 16(84%) were discharged and only 3(16%) patients died. Whereas among patients without Comorbid condition, 29(83%) were discharged and 6(17%) died. This difference was not statistically significant (p=0.940).15 cases (27%) required ventilator support. Mortality of 9 cases (17%) occurred in the given duration of study and rest of cases 45(83%) were discharged from the hospital. Out of 54 cases, 4 cases had diabetes mellitus and from that 3 case were died. The difference was statistically significant (p=0.012).Conclusions: Influenza A H1N1 infection predominantly affects young age and equally affecting both genders. One fourth of total cases had severe illness and required ventilator support. Majority of patients died within 8 day of critical illness. All deaths were reported from urban area. Most common symptom in fatal cases of influenza A H1N1 was cough followed by breathlessness, high grade fever, mild fever and sore throat and the most common co morbidity was Diabetes Mellitus.

Experimental Evaluation and Analysis of LED Illumination Source for Endoscopy Imaging

The minimally invasive surgery uses a small instrument with camera and light to fit the tiny cut in the skin. The selection of the light depends on the power and driving current of the circuit. It can also help in the standardization of the camera and capture the tissues' true-colour image. This paper presents the LED source analysis used in the clinical endoscopes for surgery and the human body's medical examination. Initially, a LED source selection mechanism generating intense illuminance in a visible band is proposed. A low-cost prototype model is developed to analyze the wavelength and illuminance of three different LEDs types. An effect on variation in LED illumination is investigated by changing the distance between the Borescope and LED source. True-colour image generation and tissue contrast are more important in medical diagnostics. Therefore, a sigmoid function improving the whole contrast ratio of the captured image in real-time is presented. The results of spectrum and wavelength for a current variation are presented. Type 3 LED produces higher illumination (i.e., 395 Klux) and peak wavelength (i.e., 622.05 nm) than other LEDs, while type-2 LED has better FWHM for the blue colour spectrum. The modification in the sigmoid function enhances the image with 34.25 peak PSNR producing a true-colour image

One-pot synthesis of dihydropyrano[2,3-c]pyrazole derivatives using β-cyclodextrin-SO 3 H as a reusable catalyst in aqueous medium

Abstract: We have developed an expedient and highly efficient β-cyclodextrin-SO 3 H catalyzed approach for the eco-friendly synthesis of dihydropyrano[2,3-c]pyrazole derivatives in aqueous medium. The reaction proceeded smoothly with a range of functionalities within 15-30 min to produce the dihydropyrano[2,3-c]pyrazole scaffolds in good to excellent yields. β-cyclodextrin-SO 3 H can be recycled and reused with an insignificant loss of catalytic activity

Experimental Evaluation and Analysis of LED Illumination Source for Endoscopy Imaging

The minimally invasive surgery uses a small instrument with camera and light to fit the tiny cut in the skin. The selection of the light depends on the power and driving current of the circuit. It can also help in the standardization of the camera and capture the tissues' true-colour image. This paper presents the LED source analysis used in the clinical endoscopes for surgery and the human body's medical examination. Initially, a LED source selection mechanism generating intense illuminance in a visible band is proposed. A low-cost prototype model is developed to analyze the wavelength and illuminance of three different LEDs types. An effect on variation in LED illumination is investigated by changing the distance between the Borescope and LED source. True-colour image generation and tissue contrast are more important in medical diagnostics. Therefore, a sigmoid function improving the whole contrast ratio of the captured image in real-time is presented. The results of spectrum and wavelength for a current variation are presented. Type 3 LED produces higher illumination (i.e., 395 Klux) and peak wavelength (i.e., 622.05 nm) than other LEDs, while type-2 LED has better FWHM for the blue colour spectrum. The modification in the sigmoid function enhances the image with 34.25 peak PSNR producing a true-colour image

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