Multi-Person Tracking Based on Faster R-CNN and Deep Appearance Features

Mostly computer vision problems related to crowd analytics are highly dependent upon multi-object tracking (MOT) systems. There are two major steps involved in the design of MOT system: object detection and association. In the first step, desired objects are detected in every frame of video stream. Detection quality directly influences the performance of tracking. The second step involves the correspondence of detected objects in current frame with the previous to obtain their trajectories. High accuracy in object detection system results in less number of missing detection and finally produces less fragmented tracks. Better object association increases the affinity between objects in different frames. This paper presents a novel algorithm for improved object detection followed by enhanced object tracking. Object detection accuracy has been increased by employing deep learning-based Faster region convolutional neural network (Faster R-CNN) algorithm. Object association is carried out by using appearance and improved motion features. Evaluation results show that we have enhanced the performance of current state-of-the-art work by reducing identity switches and fragmentation

A Unified Integration Model and Database Management System for Genomic Data

Genomic databases are heterogeneous in nature. This means, they have different notations, formats and terminologies for the same kind of data. To overcome the problems due to the heterogeneous and disperse nature of these databases, two main data integration approaches are used i.e. Mediator-based approach and Data warehouse approach. Systems developed using these approaches allow biologists read-only access of data. At times, they need to submit their own generated data during experiments to databases which is not possible with existing systems. In this paper, we design and develop data warehouse based system which not only allows biologists and researchers to access data but also to submit data they generated during lab experiment

Personalized Medicine; a Potential Therapy for Cystic Fibrosis

Cystic Fibrosis (CF) is an inherited disorder caused by mutations in CFTR gene that codes for Cystic Fibrosis Transmembrane-conductance Receptor anion channel. It is an autosomal recessive disease which affects the cells that secrete sweat, mucous and digestive juice, making these fluids thick and sticky, thus plugging ducts and tubes of various organs. The CF mutations are classified into various classes (class I, II, III, IV, V and VI) based on the cellular phenotype and complexity of mutants. The knowledge and understanding of biology and mechanisms of defects that underlie Cystic fibrosis paved a way to the development of different therapeutic approaches for these mutation classes. Ivacaftor first CFTR potentiator (FDA approved in 2012) is mostly used for Class III and IV mutations. Trials in patients with homozygous F508del mutation, a most common type of CF mutation that involves protein processing defects, showed no improvement with Ivacaftor alone, therefore, a double-combination therapy involving potentiator-corrector i.e., Ivacaftor-Lumacaftor got approval in 2015 to treat patients homozygous for F508del mutation. Then Ivacaftor-Tezacaftor (corrector) combination therapy was approved in 2018 which showed improved tolerability as compared to lumacaftor. In 2019, Trikfta, a triple combination therapy, came into light. It increases CFTR activity and is substantially considered to work more effectively in patients homozygous for F508del mutation. Studies and clinical trials reveal the outperformance of Trikafta in other available therapies in terms of respiratory symptoms, lungs functionality and quality of life on a whole.Keywords: Cystic Fibrosis (CF); Cystic Fibrosis Transmembrane Conductance regulator (CFTR); Ivacaftor; Lumacaftor; Tezacaftor; Trikafta    

Management of Chili leaf curl disease (ChiLCD) through resistant germplasm and Nutrients in relation to Environmental Factors

Chili (Capsicum annuum L.) is an important commercial spicy crop grown in many regions across the world. Six chili varieties such as Zinia F1, Desi (Jalalpuri), Desi (Jalalpuri 2), Wonder Hot, Summer Queen F1, and Faisalabad Selection was evaluated against the most devastating and catastrophic Chili’s leaf curl disease caused by Begomovirus Chili leaf curl virus (ChiLCV). Out of all six varieties, Wonder Hot and Summer Queen F1 showed moderately resistant (MR) response whereas three varieties including Zinia F1, Desi (Jalalpuri) and Desi (Jalalpuri 2) had a moderately susceptible (MS) response, and only one variety namely Faisalabad Selection, expressed resistant (R) response. Four varieties; Desi (Jalalpuri), Desi (Jalalpuri 2), Zinia F1, and Wonder Hot, were used for management purposes with four micronutrients in combinations, namely T1 (ZnSO4 + CuSO4), T2 (MnSO4 + CuSO4), T3 (Boric Acid + CuSO4) and T4 (ZnSO4 + CuSO4 + MnSO4 + Boric Acid). When compared to control (58.14%), only T4 demonstrated minimum disease severity (11.63%). In the case of disease incidence, T4 gave the best results with minimum disease incidence (35.65%) as compared to control (92.59%). Treatments were able to decrease the disease progression even in the existing of favorable environmental factors.There was significant (p˂0.05) but positive correlation between wind speed and disease severity. Wind speed was highly correlated with the disease severity of variety Wonder Hot (r=0.91). We concluded that the application of micronutrient activate the plant defense system and at the same time suppress the vector populations

Visual Speaker Identification Using Lip and Body Movements

Speaker identification has been studied in many fields such as image processing, audio processing, artificial intelligence and speech recognition. Two of these areas are integrated together in order to identify the speaker. This research will focus on two main approaches which are lip movements and body movements. We will work on the two approaches to achieve the speaker identification. The expected outcome of this study will be to identify the speaker in different scenarios, if there is a single speaker or if there is multiple speakers in the video or if the speaker’s lips are not in view

Evaluation of stress and its clinical correlation among the students of Allied Health Sciences

Background: Stress is a key indicator affecting the psychological and physical health of students throughout the world. The current study was planned to find out depression, stress, and anxiety and their impact on obesity and different clinical markers among the students of Allied Health Sciences.Methods: To assess stress, anxiety, and depression levels; the Depression, Anxiety, and Stress Scale (DASS- 21) was utilized. A total of 1446 individuals participated in the current research project. After assessing the BMI of students, a blood sample of two hundred and two (202) obese students were collected. Spectrophotometry, latex agglutination, and flow cytometry were employed to determine the different clinical markers.Results: Stress was found in 63.10% population, while depression was 59.70% and the prevalence of anxiety was 61.90%. After analysis of the blood samples of students, a significant increase was seen in cholesterol and HDL, while VLDL was moderately decreased. LDL, triglycerides, and CBC showed no significant change. Random blood glucose was normal, and CRP was also found negative in all the participants. There was no significant correlation between serum lipid profile and CBC parameters during stress, anxiety, and depression in students.Conclusion: A significant stress level was found among the students of Allied Health Sciences. It is a dire need of time to be focused on the mental health of students. Psychological counseling should be provided in the institutes for the mental well-being of the students.Keywords: Stress, Depression, Anxiety, Dass-21, Health sciences students     

Security Techniques and Solutions for Preventing the Cross-Site Scripting Web Vulnerabilities: A General Approach

The growth of social networking sites across the World Wide Web is directly proportional to the complex user- created HTML content and this habit is rapidly becoming the norm rather than exception. Complex user created web message is a threat for cross site scripting (XSS) attacks that hits various websites and confidential user data. In this state, processes that prevent web applications to XSS attacks have been of recent interest for researchers. Most of the web applications and confidential user data have security problem with XSS attacks. Using this method an attacker embeds his malicious script into application’s output. This contaminated response of server is sent to a user’s web browser where it is executed and user’s sensitive data is transmitted to a third party. Recently XSS attack is prevented on server side, by thoroughly examining, filtering and removing malicious content inserted by hacker. For social networking sites the criticality of XSS attacks gets even higher because the hackers can try more socially engineered attacks where the target user can be fooled by thinking that an attack link is coming from his ‘friend’. The presented solution focuses on prevention techniques for cross-site (XSS) attacks both on server side and on the client side by keeping a track of all user requests and information. We have also discussed various recent XSS attacks in real world and have done analysis that why filtering mechanisms are so abortive and being failed in defending these attacks