Object detection and activity recognition in video surveillance using neural networks

Purpose: This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy. Design/methodology/approach: The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods. Findings: The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus. Originality/value: This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection. © 2023, Emerald Publishing Limited.

Telogen Effluvium as Sequela of COVID-19 in Patients from Central India: A Retrospective Observational Study

Introduction: Corona virus disease has several dermatological symptoms. Telogen effluvium is one of them. The present study presents a case series of post COVID Telogen effluvium from Central India region. Material and Method: This retrospective observational study included 72 patients (61 females & 11males), aged 29 to 62 years (median 49 yrs). The patient’s demography, history of hair fall, signs and symptoms, co morbidities and the treatment received for COVID-19 infection, Psychological perceived stress score, triggering factors for Telogen effluvium, Vitamin B12, Vitamin D and Ferritin levels were recorded & analysed. Result: The included patients were suffering from at least one co morbidity. Thirty patients had severe COVID-19 infection and were hospitalized. Psychological perceived stress score was low (12) in two, moderate (16-24) in twenty and high (29-38) in fifty patients. Vitamin B12 was low in twelve and Ferritin in ten patients. Seventy patients (97.2%) had positive hair pull test and 69 (95.8 %) had diffuse loss of hair. Discussion: Post covid Telogen effluvium was seen generally in females, and in middle aged. Most, 70/72 had moderate to severe psychological stress, Psychological perceived stress score was more than 16. Patients reported 2.2 to 6 months (median 3.5 months) after COVID-19 infection. Most 69 (95.8%) had diffuse hair loss. Post Covid patients may have several triggering factors for Telogen effluvium like psychological stress, nutritional deficiency or the drugs (heparin). Conclusion: Post Covid Telogen effluvium could be triggered by psychological stress, nutritional deficiencies (Vitamin B12, Iron) or drugs (heparin). Such cases could be managed by identification of triggering factors, proper counselling, high protein diet with vitamin supplementation.

Allocation Optimization of Medical Samples For Distributed Testing

The world is witnessing the COVID-19 pandemic, which originated in the city of Wuhan, China, and has quickly spread to the whole world, with many cases having been reported in India as well. The healthcare system is going through unprecedented load on its resources while the available infrastructure is inadequate.COVID-19 samples are being tested at a massive scale and even small optimizations at this scale can save time, huge amounts of money, and resources. Particularly, the manual approach or even baseline greedy approach being used to allocate COVID-19 samples to medical labs across a state can lead to underutilization of resources. Hence, this work proposes a system to optimize the problem of allocation of medical samples to medical testing laboratories with high efficiency and minimal economic penalty. We use the Mixed Integer Programming (MIP) Model using high-performance MIP based solvers for custom applications by providing a tight integration with the branch-and-cut algorithms of the supported solvers to improve the results compared to baseline greedy approach. The system provides a transportation schedule optimized with respect to capacity of different labs and COVID-19 cases across the state of Karnataka. We tested the model on various datasets and observed significant improvement over the baseline greedy model.

Examine the Indian Tweets to Determine Society Emphasis on Novel Corona-Viruses (COVID-19)

The recent emerging coronavirus as novel corona virus (2019‐nCoV) formed viral pneumonia based emergency not only in Wuhan but also Europe, Iran, North Korea, India, and many more countries. WHO has already declared this situation as pandemic of corona virus. The world has around twenty-four lakhs cases in the whole world with around one and half lakhs deaths as on 20 April 2020. The corona virus is the family member of Nidovirales, which occur in human body from animal-human interaction. Here, we deliver the basics of corona virus and illustrate the social impact of this emergency. The review will aid the knowledge of CoV with their family and understand the person for healthy life. Here we study the Indian Tweets to determine the people’s emphasis on emerged Novel Coronaviruses (COVID-19), also compute the comparative tweets as concern of corona virus especially for Indian capital region for last six months January to June 2020 and January to March 2021 and find out the tactics of tweets for peoples concern about it. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Cases of mental health issues are increasing continuously and have sped up due to COVID-19. There are high chances of developing mental health issues such as depression, anxiety, schizophrenia, and dementia after 2–3 months of COVID-19 diagnosis. In this paper, a review and meta-analysis of machine intelligence approaches—namely, machine learning, deep learning (deep learning with hybrid boosting), and machine vision methods—for mental health issues and depression detection were presented. Meta-analysis was performed in four parts. The first part focused on the publication trends, criteria for inclusion and exclusion, and the current methodological scenario. The second part was intended for the methods and their advantages and limitations. It covered mental health issues and depression detection techniques along with the challenges. The third part focused on the discussion and applicability of datasets. The fourth part focused on the complete analysis and discussion along with suggestive measures;moreover, it covered the overall analysis, including the methodological impact, result impact, current trends, and some suggestions based on the limitations and challenges. © The Authors.

A case of severe thrombocytopenia in a patient with COVID-19 receiving continuous venovenous hemodialysis

Introduction: Thrombocytopenia is a rare complication of renal replacement therapy with most of the cases reported in intermittent hemodialysis patients. There is limited data regarding the incidence of thrombocytopenia caused by continuous renal replacement therapy (CRRT). We report a case of thrombocytopenia in patient treated with CRRT for severe AKI from COVID-19 sepsis unrelated to heparin. Case Description: A 73-year female with history of type 2 diabetes mellitus was admitted for Coronavirus Disease 2019 (COVID-19) pneumonia. Patient developed acute hypoxic respiratory failure requiring mechanical ventilation despite treatment with hydroxychloroquine, azithromycin and convalescent plasma. Hospital course was complicated by septic shock and acute kidney injury with serum creatinine rising from a baseline of 0.8 mg/dl. Continuous veno-venous hemodialysis (CVVHD) without any anticoagulation was initiated due to severe fluid overload. Significant thrombocytopenia below 50,000/mm3 was noted 2 days after CVVHD treatment. Patient received multiple antibiotics and heparin drip before CVVHD and platelet counts were above 150,000/ mm3. Heparin induced thrombocytopenia (HIT) was ruled out with negative serotonin release assay and platelet counts remain low despite the discontinuation of all potential agents. Disseminated intravascular coagulopathy was excluded based on coagulation tests. Platelet counts finally went up to 160,000/mm3 on subsequent CVVHD holidays and again dropped to 70,000/mm3 after CVVHD was resumed. Discussion: The rate of rise in platelet count more than 150,000/mm3 in 2 days after cessation of CVVHD supports the diagnosis of thrombocytopenia caused by CVVHD. Although the exact mechanisms remain unclear, previous studies suggested that the mechanical destruction of platelets by the hemofilter or allergic reaction to dialyzer membrane as some of the reasons. Some studies have found that severe decline (more than 50%) in platelet count was associated with increased mortality and decreased rate of renal recovery. Thrombocytopenia on CVVHD unrelated to HIT is an under-acknowledged complication. Understanding the multiple etiologies of thrombocytopenia will help prevent the excessive use of blood products, fluid overload state and the potential clotting issue of CVVHD due to transfusion.

Global COVID 19 and agnihotra

Agnihotra, the basic healing fire of HOMA Therapy, is a small fire prepared in a copper pyramid exactly at sunrise and sunset each day. Agnihotra can neutralize the effects of pollution on plants, animals and human beings and at the same time give nourishment. As this is about Covid-19, a threat to all of humanity, and Agnihotra may be a means to alleviate the ramifications. Keywords: Agnihotra, copper pyramid, Mantra, Rice, Dried cow dung, Ghee.