Deep Features and Clustering Based Keyframes Selection with Security

The digital world is developing more quickly than ever. Multimedia processing and distribution, however become vulnerable issues due to the enormous quantity and significance of vital information. Therefore, extensive technologies and algorithms are required for the safe transmission of messages, images, and video files. This paper proposes a secure framework by acute integration of video summarization and image encryption. Three parts comprise the proposed cryptosystem framework. The informative frames are first extracted using an efficient and lightweight technique that make use of the color histogram-clustering (RGB-HSV) approach's processing capabilities. Each frame of a video is represented by deep features, which are based on an enhanced pre-trained Inception-v3 network. After that summary is obtain using the K-means optimal clustering algorithm. The representative keyframes then extracted using the clusters highest possible entropy nodes. Experimental validation on two well-known standard datasets demonstrates the proposed methods superiority to numerous state-of-the-art approaches. Finally, the proposed framework performs an efficient image encryption and decryption algorithm by employing a general linear group function GLn (F). The analysis and testing outcomes prove the superiority of the proposed adaptive RSA

Factors affecting the ecological habitat of Benthic macro-invertebrate assemblages in Asan wetland, Dehradun in Garhwal Himalaya

The Himalayan region has several freshwater resources, such as rivers, lakes, and wetlands. These freshwater resources have been adversely affected by environmental factors. Freshwater biological systems are defenseless against outcomes of environmental changes that might prompt the irreversible disintegration of these natural surroundings. The present study aimed to investigate the effect of biotic and abiotic factors on the Benthic macro-invertebrate assemblages of the Asan wetland, Dehradun in Garhwal Himalaya. A determination of the physico-chemical health status of the Asan wetland viz., electrical conductivity (EC), total dissolved solids (TDS), biochemical oxygen demand (BOD), and nutrients parameters like nitrogen, potassium, and phosphorus were investigated during this study. Three sampling sites (Site 1, Site 2 and Site 3) of wetland were selected and the water samples were collected seasonally, i.e., summer, winter, and monsoon from April 2021-March 2022. Maximum values of EC(163.75 µS/cm), TDS (232.78 (mg/l),  alkalinity (141.20 mg/l)  and pH(7.8)  were recorded in the monsoon season (June-September) and minimum values of EC( 135.80µS/cm), TDS (196.80 (mg/l),  alkalinity (119.80mg/l)  and pH(7.2) were recorded in the winter season (November–February). An overall total of 18 macrobenthos genera belonging to four classes was identified. Maximum communities of macrobenthos were observed during the winter and minimum communities during the monsoon season. Canonical correspondence analysis (CCA) was used to determine whether microbenthic genera and habitat ecological parameters and showed a positive or negative correlation. Thus, the present study contributed to the status of various factors and their impacts on the Benthic macro-invertebrate structure of the Asan wetland.        

Role of Terpenoids Active Ingredients Targeting for Neuroprotective Agents

Neuroinflammation is a characteristic sign of a wide variety of neurodegenerative diseases, including Alzheimer\u27s and Parkinson\u27s, amongst others. Microglia, which are native immune cells found in the brain, become activated very quickly in response to a brain infection or injury. When microglia become overactivated, their production of pro-inflammatory and cytotoxic chemicals can become unregulated and uncontrolled, which is the primary cause of neuroinflammation. Microglia are principally responsible for neuroinflammation. As a result, the investigation of novel approaches to reduce neuroinflammatory reactions is an essential component of neurodegenerative disease treatment. In the research of brain inflammation, bacterial lipopolysaccharide is frequently used. This compound is responsible for the initiation of a number of significant cellular processes that significantly contribute to the pathophysiology of neuroinflammation

Two-Stage Human Activity Recognition Using 2D-ConvNet

There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences

Deep Features and Clustering Based Keyframes Selection with Security

The digital world is developing more quickly than ever. Multimedia processing and distribution, however become vulnerable issues due to the enormous quantity and significance of vital information. Therefore, extensive technologies and algorithms are required for the safe transmission of messages, images, and video files. This paper proposes a secure framework by acute integration of video summarization and image encryption. Three parts comprise the proposed cryptosystem framework. The informative frames are first extracted using an efficient and lightweight technique that make use of the color histogram-clustering (RGB-HSV) approach's processing capabilities. Each frame of a video is represented by deep features, which are based on an enhanced pre-trained Inception-v3 network. After that summary is obtain using the K-means optimal clustering algorithm. The representative keyframes then extracted using the clusters highest possible entropy nodes. Experimental validation on two well-known standard datasets demonstrates the proposed methods superiority to numerous state-of-the-art approaches. Finally, the proposed framework performs an efficient image encryption and decryption algorithm by employing a general linear group function GLn (F). The analysis and testing outcomes prove the superiority of the proposed adaptive RSA

Challenges in the implementation of the NeoOBS study, a global pragmatic observational cohort study, to investigate the aetiology and management of neonatal sepsis in the hospital setting

Neonatal sepsis is a significant cause of mortality and morbidity in low- and middle-income countries. To deliver high-quality data studies and inform future trials, it is crucial to understand the challenges encountered when managing global multi-centre research studies and to identify solutions that can feasibly be implemented in these settings. This paper provides an overview of the complexities faced by diverse research teams in different countries and regions, together with actions implemented to achieve pragmatic study management of a large multi-centre observational study of neonatal sepsis. We discuss specific considerations for enrolling sites with different approval processes and varied research experience, structures, and training. Implementing a flexible recruitment strategy and providing ongoing training were necessary to overcome these challenges. We emphasize the attention that must be given to designing the database and monitoring plans. Extensive data collection tools, complex databases, tight timelines, and stringent monitoring arrangements can be problematic and might put the study at risk. Finally, we discuss the complexities added when collecting and shipping isolates and the importance of having a robust central management team and interdisciplinary collaborators able to adapt easily and make swift decisions to deliver the study on time and to target. With pragmatic approaches, appropriate training, and good communication, these challenges can be overcome to deliver high-quality data from a complex study in challenging settings through a collaborative research network

Challenges in the Implementation of the NeoOBS Study, a Global Pragmatic Observational Cohort Study, to Investigate the Aetiology and Management of Neonatal Sepsis in the Hospital Setting

Neonatal sepsis is a significant cause of mortality and morbidity in low- and middle-income countries. To deliver high-quality data studies and inform future trials, it is crucial to understand the challenges encountered when managing global multi-centre research studies and to identify solutions that can feasibly be implemented in these settings. This paper provides an overview of the complexities faced by diverse research teams in different countries and regions, together with actions implemented to achieve pragmatic study management of a large multi-centre observational study of neonatal sepsis. We discuss specific considerations for enrolling sites with different approval processes and varied research experience, structures, and training. Implementing a flexible recruitment strategy and providing ongoing training were necessary to overcome these challenges. We emphasize the attention that must be given to designing the database and monitoring plans. Extensive data collection tools, complex databases, tight timelines, and stringent monitoring arrangements can be problematic and might put the study at risk. Finally, we discuss the complexities added when collecting and shipping isolates and the importance of having a robust central management team and interdisciplinary collaborators able to adapt easily and make swift decisions to deliver the study on time and to target. With pragmatic approaches, appropriate training, and good communication, these challenges can be overcome to deliver high-quality data from a complex study in challenging settings through a collaborative research network

Medicinal plants of Muzaffarnagar district used in treatment of urinary tract and kidney stones

191-195A floristic survey of ethnomedicinal plants was conducted at Muzaffarnagar district of Uttar Pradesh to assess the potentiality of plant resources. The study revealed that 15 plant species belonging to 13 families are used as anti-urolithiatic agents in local remedies. The information on medicinal uses is based on the exhaustive interviews with local healers and herbalists, practicing traditional system of medicine. Details of the plants, parts used, method of preparation, dosage and mode of administration have been reported. Equisetum debile Roxb. and Gomphrena celosioides Mart. are most effective and commonly used in treatment of urinary tract and kidney stones. These may prove precious potential source of bioactive compounds of therapeutic value against uro- and nephro-lithiasis and hence need further critical scientific testing, phytochemical examination and clinical evaluation for the purpose

Haematological toxicity in cancer cervix patients treated with concurrent chemoradiation by conventional technique- correlation with bone marrow radiation dose

Introduction: The standard of care for treatment of cancer cervix is concurrent chemoradiation followed by brachytherapy in the majority of cases. Conventional radiotherapy with chemotherapy causes haematological toxicities which may be related to radiation to pelvic bone marrow. The present study aims to study the haematological toxicities and correlate with the mean dose to the bone marrow. Material and Methods: Retrospective data of cancer patients treated in the institute in the year 2019 was retrieved. Haematological toxicities were analyzed in terms of CTCAE criteria. Mean dose to bone marrow was calculated after the delineation in the CT scan. The correlation between haematological toxicity and mean bone marrow was done using a paired t-test for statistical significance. Results: The data of 20 patients were retrieved. Anaemia Grade, I and Grade II-IV was seen in 65% and 35% respectively. Leukopenia Grade I and Grade II-IV were seen in 85% and 15% respectively and Lymphopenia Grade I and Grade II-Iv were seen in 55% and 45% respectively. The mean dose to bone marrow did not show any statistical significance with the severity of haematological toxicity. There was no Grade II-IV toxicity of neutropenia and thrombocytopenia. Conclusion: Conventional radiotherapy can safely be practice for patients with cancer cervix with acceptable haematological toxicities