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There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance.

The present study focused on pharmaceutical potential of red seaweed Hypnea valentiae. The phytochemical analysis of the methanolic extract of H. valentiae found more phycocompounds which was confirmed by GC-MS analysis. The significant levels of total alkaloid, phenol, tannin and flavonoid were expressed in methanolic extract of H. valentiae. In addition, the potent antioxidant activity of the methanolic extract was efficiently expressed during the total antioxidant activity, free radical scavenging assays of DPPH and hydroxyl denoted as 72.85±0.66 activity at a concentration 50-250µg/mL, 61.44±0.36 activity at a concentration 10-160µg/mL and 83.29±0.38 activity at a concentration 25-125µg/mL respectively. The results were establishing the potential of the seaweed and antioxidant properties of H. valentiae could be a valuable marine source for developing novel pharmaceutical applications.

Thanks to the development of pertinent technology, IoT is rapidly advancing in the medical sector. IoMT is a group of healthcare IT systems that are linked to a variety of medical devices for various purposes. IoMT's development has had a significant influence on healthcare in general, but especially for elderly and handicapped patients. In today's fast-paced world, even regular people require assistance with daily tasks. Helping them take their meds consistently each day without skipping a dosage is one such crucial task. The data will be processed depending on dose access by this medication monitoring system. Additionally, scheduling is done based on the patient intake report's expected accessibility. The suggested system combines an improved notification system with event scheduling based on a priority method. Real-time monitoring and a reminder system are built into the Smart Medibox design to remind patients to take their medications on schedule. It includes a cooling module and sensor that are managed by a microprocessor. The alert has information on date, time, and medicine description and may be programmed for taking numerous medications. It is made to be a small, portable gadget that makes it easy to connect to a secure cloud storage for more research. The gadget transfers all of the data to cloud storage whenit is Wi-Fi connected.

With the advent of the Internet of Things (IoT), there have been significant advancements in the area of human activity recognition (HAR) in recent years. HAR is applicable to wider application such as elderly care, anomalous behavior detection and surveillance system. Several machine learningalgorithms have been employed to predict the activities performed by the human in an environment. However, traditional machine learning approaches have been outperformed by feature engineering methods which can select an optimal set of features. Onthe contrary, it is known that deep learning models such as Convolutional Neural Networks (CNN) can extract features and reduce the computational cost automatically. In this paper, we use CNN model to detect human activities from Image Dataset model. Specifically, we employ transfer learning to get deep image features and trained machine learning classifiers. Our experimental results showed the accuracy of 96.95% using VGG16. Our experimental results also confirmed the high performance of VGG-16 as compared to rest of the applied CNN models.

Natural language processing innovations in the past few decades have made it feasible to synthesis and comprehend coherent text in a variety of ways, turning theoretical techniques into practical implementations. Both report summarizing software and sectors like content writers have been significantly impacted by the extensive Language-model. A huge language model, however, could show evidence of social prejudice, giving moral as well as environmental hazards from negligence, according to observations. Therefore, it is necessary to develop comprehensive guidelines for responsible LLM (Large Language Models). Despite the fact that numerous empirical investigations show that sophisticated large language models has very few ethical difficulties, there isn't a thorough investigation and consumers study of the legality of present large language model use. We use a qualitative study method on OpenAI's ChatGPT3 to solution-focus the real-world ethical risks in current large language models in order to further guide ongoing efforts on responsibly constructing ethical large language models. We carefully review ChatGPT3 from the four perspectives of bias and robustness. According to our stated opinions, we objectively benchmark ChatGPT3 on a number of sample datasets. In this work, it was found that a substantial fraction of principled problems are not solved by the current benchmarks; therefore new case examples were provided to support this. Additionally discussed were the importance of the findings regarding ChatGPT3's AI ethics, potential problems in the future, and helpful design considerations for big language models. This study may provide some guidance for future investigations into and mitigation of the ethical risks offered by technology in large Language Models applications.

Pain and swelling are among the most frequently encountered complaints in an orthopedics outpatient department (OPD) relatively less common in a pediatric OPD. A high level of suspicion is required to diagnose bone diseases. Common conditions such as rickets, septic arthritis, and tuberculosis must be ruled out. Treatment such as antibiotics, anti-inflammatory drugs, or steroids may mask an underlying serious condition in which if left untreated can cause significant morbidity and mortality to the person. Here, we discuss a case of a young infant 11 months of age who had symptoms suggestive of septic arthritis and was later found to have Brodie’s abscess which is a type of subacute osteomyelitis on further evaluation which is usually mistaken as a tumor. This age at presentation is rare.

Dengue is the most common arboviral infection prevalent in India. Aedes aegypti mosquito is the principal vector followed by Aedes albopictus.It presents in three clinical stages. It is diagnosed by detection of NS1 antigen, IgM antibody by ELISA and viral RNA by reverse transcriptase PCR. Early diagnosis and treatment is essential to prevent the complications of Dengue. The study was done in a Government Thiruvannamalai Medical College & Hospital about the seroprevalence from the month July 2021 to December 2021 by detection of NS1 antigen. The seroprevalence was found to be 11.4%.The highest number of patients (38.2%) belong to the age group between 21-40 years. Highest number of positive (39.5%) cases reported in October month.

Memes have become a new type of internet communication. It has the ability to instantly disseminate anger, offensiveness, and violence. Because of its regional meaning, classifying memes is difficult.. This work presents here a computational model of classifying Tamil memes using convolutional neural networks. Convolutional neural networks have the potential to learn, adapt, and rearrange themselves. As a result, it can extract features automatically applying prior knowledge of existing categories, avoiding the time-consuming feature extraction process used in older methods in images. The basic layer of MobileNet is made up of depth-wise separable filters, also referred to as depth-wise separable convolution. The network structure is another feature that boosts performance. It utilizes very less than computation power while applying transfer learning. This network has reduced parameters and computation cost. Skip connections, or shortcuts, are used by residual neural networks to jump past some layers. Residual connections allow parameter gradients to travel more easily from the output layer to the network's prior layers, allowing for the training of deeper networks. Higher accuracies on more demanding tasks may arise from the greater network depth. AlexNet is a leading architecture for any image identification task, and it could have a lot of applications in the artificial intelligence field of computer vision. In the future, AlexNet may be used for image classification jobs more than CNNs. This work aims to classify Tamil memes using Mobilenet, Resnet and hyper parameter turned AlexNet.

The fast paced lifestyle with no control over diet and lack of exercise has led to rapid rise in obesity in worldwide population. India being a developing country is no exception to this.A body mass index (BMI) ≥50 kg m, referred to as super obese necessitates optimum anaesthetic expertise during perioperative period. We faced a conundrum of challenges while managing a case of necrotizing fascitis in a superobese patient with multiple comorbidities.Peripheral nerve block was an ideal technique for this patient but for local site skin infection and deep nerve location, spinal anaesthesia with 5 inch (12.7 cm) spinal needle was administered.Regional anaesthesia in obese patients comes with a lot of difficulties including localizing the intervertebral space, narrow subarachnoid space, increased pressure, positioning the patient. Restricted fluid intake due to acute kidney injury leading to decreased preload compounds the above factors.

Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms.

The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.

Facial expression helps to communicate between the people for conveying abundant information about human emotions. Facial expression classification is applied in various fields such as remote learning education, medical care, and smart traffic. However, due to the complexity and diversity of the facial emotions, the present facial expression recognition model causes a low recognition rate and it is hard to extract the precise features that are related to facial expression changes. In order to overcome this problem, we proposed Multi-feature Integrated Concurrent Neural Network (MICNN) which is significantly different from the single neural network architectures. It aggregates the prominent features of facial expressions by integrating the three kinds of networks such as Sequential Convolutional Neural Network (SCNN), Residual Dense Network (RDN), and Attention Residual Learning Network (ARLN) to enhance the accuracy rate of facial emotions detection system. Additionally, Local Binary Pattern (LBP) and Principal Component Analysis (PCA) are applied for representing the facial features and these features are combined with the texture features identified by the Gray-level Co-occurrence Matrix (GLCM). Finally, the integrated features are fed into softmax layer to classify the facial images. The experiments are carried out on benchmark datasets by applying k-fold cross-validation and the results demonstrate the superiority of the proposed model. &nbsp

ImportanceExtended-interval dosing of pembrolizumab (400 mg every 6 weeks) was approved by US Food and Drug Administration (FDA) in April 2020 as an alternative to standard-interval dosing (200 mg every 3 weeks). Extended-interval dosing may enhance access, alleviate patient and health system financial toxicity, and improve patient quality of life, particularly during the COVID-19 pandemic. Neither adoption nor effectiveness of extended interval in the US has been adequately described.ObjectiveTo describe adoption of extended-interval dosing of pembrolizumab since its FDA approval and to measure its preliminary real-world effectiveness compared with standard-interval dosing.Design, Setting, and ParticipantsThis was a retrospective cohort study that used data from the Veterans Health Administration (VHA), a US-based, nationwide single-payer health system. Participants were veterans who were prescribed single-agent pembrolizumab within the VHA between April 1, 2020, and July 1, 2021. Patients receiving combinations of pembrolizumab and cytotoxic chemotherapy or tyrosine kinase inhibitors were excluded. A subcohort of veterans with non−small cell lung cancer (NSCLC) was also identified using claims-based codes.ExposuresSingle-agent pembrolizumab at extended or standard intervals.Main Outcomes and MeasuresThe number and proportion of single-agent pembrolizumab prescriptions that were extended compared with standard interval. Effectiveness was described in terms of time-to-treatment discontinuation (TTD) and extended- to standard-interval pembrolizumab prescriptions were compared using Cox proportional hazards regression.ResultsA total of 835 veterans (mean age [SD], 70.9 [8.7] years; 809 [96.9%] men) began single-agent pembrolizumab during the study period (all-diseases cohort), and of these, 234 (mean [SD] age, 71.6 [7.3] years; 225 [96.2%] men) had NSCLC (NSCLC cohort). Extended-interval adoption reached its steady state plateau of approximately 35% by January 2021; 65% of participants who began standard-interval single-agent pembrolizumab received only standard-interval dosing during the treatment course. In analysis consistent with the intention-to-treat principle, no differences in TTD were observed between standard- and extended-interval dosing in either the all-diseases cohort (HR, 1.00; 95% CI, 1.00-1.00) or the NSCLC cohort (HR, 1.00; 95% CI, 1.00-1.00).Conclusions and RelevanceThis retrospective cohort study found that extended-interval dosing comprised a minority of single-agent pembrolizumab prescriptions despite the FDA approval and its potential health system and public health benefits. The findings support the TTD equivalence of standard- and extended-interval pembrolizumab across indications, complementing clinical pharmacology and single-arm clinical trial data in melanoma. This study provides further support for extended-interval pembrolizumab dosing.

We are living in an age where tasks and systems are fusing together with the power of IoT to have a more efficient system of working and to execute jobs quickly. In the recent decades, Urbanization has increased tremendously. At the same phase there is an increase in waste production. Waste management has been a crucial issue to be considered. This paper is a way to achieve this good cause, due to negligence of authorities and carelessness of the public may lead to long term problems. Breeding of insects and mosquitoes can create nuisance around promoting an unclean environment. This may even cause dreadful diseases. The main objective of the project Trash Bot will keep the environment clean and also eco friendly. If the dustbin is not maintained then these can cause an unhealthy environment and can cause pollution that affects our health. Since the technologies are getting smarter day by day to clean the environment, we are designing a smart dustbin by using Arduino Opens and closes its lid if it sees any trash in front of it and the lid of the can will open automatically and will wait for you to feed it more than after a certain delay it will automatically close. This will help toward health and hygiene. In this project we have designed a smart dustbin using ARDUINO UNO, along with an ultrasonic sensor, servo motor, and battery jumper wire. After all hardware and software connection, now Smart Dustbin program will be run. Dustbin lid will open automatically when someone comes near. Then will wait for the user to put garbage and close it. The main advantage in terms of society is that it will help toward health and hygiene.

The sentinel lymph node (SLN) procedure for vulva cancer is a safe alternative to a radical inguino-femoral lymphadenectomy (IFLN) for small unifocal tumours. SLN evaluation through biopsy and ultra-staging has helped gynaecological oncology surgeons improve operative morbidity with no cost to oncologic safety. Established techniques for groin SLN detection and excision in vulvar cancer use92% of patients had a successful SLN procedure. The per-groin detection rate was 84%. All successfully mapped SLN were identified with the combination of ICG-NIR andIncorporation of ICG-NIR into standard SLN mapping protocols may allow for the abandonment of routine use of BD and its poor side effect profile.

Martynia annua commonly known as Thael kodukkai belonging to the Martyniaceae family is an herbaceous plant with many medicinal properties. It is a weedy peculiar species native to tropical and subtropical regions of Mexico. The entire herb is found to exhibit potential pharmacological activities. The various parts of this plant have been reported to show many therapeutic indications like anticancer, antibacterial, anticonvulsant, anti-inflammatory, wound healing, analgesic, antipyretic activity, antioxidant, anti-diabetic, anti-nociceptive, immunomodulatory, hepatoprotective, anti-hemolytic, anti-thrombotic, anti-arthritic, anti-haemorrhoidal, anti-alopecia, anti-muscular, anti-helminthic, gastroprotective and anti-fertility activities. This review aims to provide the compilation of existing knowledge on the phytochemistry and pharmacological properties of M. annua from the reported studies. The phytochemical screening results of various extracts of stem, root, leaves and fruits of M. annua were discussed in this review. A greater number of phytoconstituents were reported to be present in leaves. This review suggests that this plant contains various phytoconstituents like anthraquinones, terpenoids, flavonoids, tannins and phenolic compounds that are responsible for various pharmacological activities. In the future, this plant provides an immense scope for pharmacological investigations.

Background: A wide-ranging scrutiny and impost of the precincts and variability of the physical activities on body mass index (BMI) relationship are crucial whether we are to comprehend how to best exploit and explore sports programming and participation for improved health. Aim: We are aiming to observe and understand the medical students’ BMI by doing physical activities. Besides, we will identify the obstacles and challenges faced by the students to balance their time after physical activities. Methods: Accordingly, screened the socio-demographic status; assessed involvement of medical students in physical activities; determined BMI among medical students and finally, correlated the physical activities with BMI. The study design used for this research is a cross-sectional study. Results: Results obtained include socio-demographic characteristics, sports participation and BMI category among medical students with physical activities. Conclusion: In general, our research revealed that only 35% of the MBBS students from UniSZA are doing physical activities. Students who are overweight and obese does not involve in any kind of physical activity but students who are having normal BMI involve themselves actively in physical activities. It shows that there is a relationship between physical activities and BMI.

Background: The second wave of the COVID-19 pandemic in India was associated with an increased incidence of rhino-orbital-cerebral mucormycosis. The objective of this paper was to prospectively explore the epidemiology, management, and results of 18 months follow-up of patients presenting with COVID associated mucormycosis at a tertiary referral centre in India. Methods: Patients presenting with symptoms suggestive of COVID-associated mucormycosis over two months were included in the study. Patients were staged based on the extent of the disease. Surgery was the primary modality of treatment except in those with intracranial spread, altered sensorium, and poor prognosis. A combination of liposomal amphotericin B and posaconazole was used as adjunct medical treatment. Patients were followed up and outcomes at one year of treatment were recorded. Results: Out of a total of 26 patients who were diagnosed with COVID associated mucormycosis, 21 patients underwent bimodality treatment (medical and surgical). The extent of surgery was based on the stage of the disease. Six eyes received retrobulbar injections of Amphotericin B to salvage vision. The overall mortality was 38.46% and 23.8% in those where the intent of treatment was curative. At the end of one year, 16 of 21 operated patients survived with mild to severe sequelae. Conclusions: Mucormycosis is a deadly fungal infection with high mortality. Early diagnosis and prompt, aggressive treatment is paramount in preventing mortality. A multidisciplinary approach is useful for effective management. Continuous follow up is paramount to identifying and treating complications.

Background In May 2020, WHO recognized the role of extensive immunization for interrupting the transmission of the SARS-CoV-2 virus. The development of such vaccines in clinical trials relies upon participants who are expected to be vested in the research process. Assessment of participant factors such as motivation and satisfaction are hence important to gauge perspective and ensure successful conduct and completion of these trials. Methods We administered a validated three-domain questionnaire to and documented the binary categorical responses (yes/no) of participants (after informed consent) who had taken both doses of COVOVAX™ in a phase 3 trial at our institute. Association of the dependent variables (participant responses) with the independent variables (participant demographics and socioeconomic strata) was computed using Chi-square test at 5% significance. In case of a significant association, Bonferroni post-hoc test was applied for multiple comparisons. Results Of the 78 participants who were administered the questionnaire, two-thirds were highly satisfied with their experience at our site. Gaining access to a new vaccine was a primary motivation overall (74%) and also in graduates (p=0.03) and middle-class population (p=0.002), whereas the lower-middle class population (p Conclusion Participants in a Covid-19 vaccine trial at Mumbai were largely satisfied with the care given to them though altruism did not feature as a primary reason for participation.

The study revolves around the aspects of value proposition and customer relationship building in the Electronic Business. The current topic highlights the Management Information Systems, Marketing aspects of E-Commerce in Business. Though it is a relatively new concept of steering business over the Internet, it also has the latent to alter the traditional form of economic and business activities. Already it affects such large sectors as communications, finance, and retail trade and holds promises in areas such as education, health, and government. The largest effects may be associated not with many of the impacts that command the most attention but with less visible, but potentially more pervasive, effects on routine business activities. The integration of Electronic Commerce and Business will bring a renaissance in the marketing function. As it presents opportunities to get close to the customer to bring the customer inside the company, explore new product ideas, and pretest them against real customers. The paradigm shift happened in online sales and marketing from fewer value goods to durable and esteemed goods like a diamond with exotic varieties at competitive prices.

Natural disasters occur often around the globe and result in significant loss of life and property. Over the last two decades, there has been a remarkable rise in extreme weather events that have the potential to wreak devastation on the planet. Frequently, disaster-affected regions needed to enhance their capacity for rebuilding, requiring more experienced and competent personnel to oversee the projects. Post-disaster reconstruction (PDR) is a complicated and difficult procedure requiring a variety of distinct and well-coordinated actions. The purpose of this research is to examine the effect of disasters, analyse the problems associated with post-disaster housing reconstruction projects in India, and provide potential solutions. A standardised questionnaire was used to gather data from a purposeful sample as part of a quantitative methodology. The analysed data reveal that institutional procedures, rebuilding techniques, project execution, and stakeholder management all contribute to the effective implementation of post-disaster home reconstruction projects.