Browse Items (16481 total)
Sort by:
-
Professional chat application based on natural language processing
There has been an emerging trend of a vast number of chat applications which are present in the recent years to help people to connect with each other across different mediums, like Hike, WhatsApp, Telegram, etc. The proposed network-based android chat application used for chatting purpose with remote clients or users connected to the internet, and it will not let the user send inappropriate messages. This paper proposes the mechanism of creating professional chat application that will not permit the user to send inappropriate or improper messages to the participants by incorporating base level implementation of natural language processing (NLP). Before sending the messages to the user, the typed message evaluated to find any inappropriate terms in the message that may include vulgar words, etc., using natural language processing. The user can build an own dictionary which contains vulgar or irrelevant terms. After pre-processing steps of removal of punctuations, numbers, conversion of text to lower case and NLP concepts of removing stop words, stemming, tokenization, named entity recognition and parts of speech tagging, it gives keywords from the user typed message. These derived keywords compared with the terms in the dictionary to analyze the sentiment of the message. If the context of the message is negative, then the user not permitted to send the message. 2018 IEEE. -
Professional Ethics of Teachers in Educational Institutions
Artha Journal of Social Sciences, Vol-11 (4), pp. 24-32. ISSN-0975-329X -
Professional quality of life of nurses with 6-8 years of work experience
The current research investigation was undertaken to understand the relationship of professional quality of life of nurses with self-efficacy, empathy and aggression. Professional quality of life is the quality one feels in relation to their work as a helper (Stamm 2010). It includes the positive component of compassion satisfaction and two negative components of burnout and secondary traumatic stress. Nursing professionals with 6-8 years of work experience and employed in Punjab, India were taken for the purpose of the study. Fantasy seeking was found to be a positive predictor of compassion satisfaction explaining 4% of variance. Self-efficacy and empathic concern were found to be negative predictors while verbal aggression was found to be a positive predictor of burnout. Together the three predictors accounted for 17% of variance in burnout. Personal distress was a positive predictor of secondary traumatic stress explaining 8% of variance. Implications are discussed with in light of professional quality of life of nurses who hold a considerable amount of work experience in the nursing profession. 2021 Ecological Society of India. All rights reserved. -
Proficient randomized response model based on blank card strategy to estimate the sensitive parameter under negative binomial distribution
This paper has great potential for estimating population proportion who possess stigmatized character by using Negative binomial distribution as a randomization device. The properties of the proposed estimation procedure have been examined. Measure of privacy protection for the proposed randomization device has been also quantified. Empirical studies are performed to support the theoretical results, which show the dominance of the proposed estimator over its competitors. Results are analysed and suitable recommendations are put forward for survey practitioners whenever they deal with sensitive characteristics. 2021 -
Proficient technique for satellite image enhancement using hybrid transformation with FPGA
Visual quality of images is improved by digital techniques for the improvement of photographs. The main purpose of image improvements is to process an image to make the output more desirable for a particular use than the original image. This paper proposes a new approach, which improves the picture of the satellite by the use of the SVD DWT concept, the Gaussian transformation DWT and multiwavelet transformation. This suggested approach would convert and approximate the single-colour value matrix of the low-flowing sub-band into one low-frequency and 15 high-frequency sub-bands, and then recreate the improved picture using the inverse transformation. In terms of technical criteria as PSNR, RMSE and CC, this approach can have higher quality and quantitative performance. This paper introduces strategies for improving hardware images using a programmable door array in real-time (FPGA). The suggested algorithm is implemented successfully with Xilinx ISE, MATLAB and ModelSim on different scale satellite images in Verilog HDL. In this article, these algorithms should be simulated and implemented using Verilog HDL. The Spartan-3E from Xilinx is the unit chosen here. 2021 IEEE -
Profiles of Victimized Outpatients with Severe Mental Illness in India
Persons with severe mental illness (PwSMI) are at risk of being victimized due to persistent cognitive, emotional, and behavioral symptoms, which can become potential threats for effective reintegration into the community. A total of 217 PwSMI, receiving outpatient psychiatric treatment from a tertiary hospital, were screened for abuse, and if they were identified as abuse, then information about contextual factors contributing to abuse, sociodemographic, family, and clinical and legal profiles was created. Overall, 150 PwSMI were victimized, of which 56% were females, 50.7% were married, 20.7% were educated up to middle school, and 31.4% were homemaker. The most common form of diagnosis was schizophrenia (43.3%), with a mean duration of illness of 14 years. All the victimized PwSMI were subjected to emotional abuse. PwSMI were more likely to be victimized by multiple family members due to poor knowledge and understanding about illness (24%). The majority of the PwSMI had disclosed abuse (62.7%) to nonformal sources (33.3%) with no documentation in the clinical file (82.7%). PwSMI experience ongoing abuse and are more likely to be re-victimized, which increases the need for regular screening and culturally sensitive and comprehensive community-coordinated care and support. 2023 Indian Journal of Community Medicine. -
Profit function Optimization for Growing Items Industry
The economy of a country depends on many industries; growing item industries are one of them. Growing items also exhibit mortality in the growth period, which creates a complex environment for the procurement decision. A practical inventory model is required to overcome this situation, which provides the optimum solution. This work describes an economics ordering quantity model for growing items with constant demand and mortality. We also take into consideration that one of the real-life management practices for businesses is the allowance of a delay in payment. There is a solution procedure with a numerical example. We have discussed analytical results to verify the concavity of the profit function. Sensitivity analysis provides us with some very useful information. . 2023 IEEE. -
Prognosis of Diabetes Mellitus Paradigm Predictive Techniques
Human life is in the era of data, when almost everything is straped on to data wellspring more- over entire esse are digitises telerecorded. That is data is generated every milli second through several means like Agriculture, Bioinformatics, Web, Cybersecurity, Smart city data, classified in- formation, pda data, flexibility evidence, medical facts, Covid related data from official state too central government portals and a number of other sources are available in todays technological con- text. There are various forms of data like structured, semi-structured, and unstructured data, text, graphics are all feasible. Every day, week, month new genre natural-world features to be resolved, machine learning adroitness have emerged as problem resolver. As a result, data management tools and analytical methodologies capable of extricate penetrated realization related specifics felicitous methodical manner ceaselessly whereby world of nature enactment rely urgently needed. The vast majority of research is focused on machine learning prediction algorithms; thus, we focus on these. Our evaluation aims to provide newbies to the field, as well as more seasoned readers, with a thorough understanding of the primary approaches and algorithms developed over the previous two decades, with an emphasis on the most notable and continuing work. We also present a new taxonomy of state of the art Model, which highlights the many conceptual and technical approaches to training with labeled and unlabeled data. Finally, we show how the fundamental assumptions underlying most machine learning methods are linked to the well-known assumptions. Grenze Scientific Society, 2023. -
Prognosis of kidney disease on ultrasound images using machine learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images.
-
Prognosis of Kidney Disease on Ultrasound Images Using Machine Learning
Kidney diseases can affect the ability to clean the blood, filter extra water out of your blood. The kidneys failure will affect the control over blood pressure and sugar level. It can also affect red blood cell production and vitamin D metabolism which is very important for bone health. When your kidneys are damaged, waste products and fluid can build up in the body. This is harmful to the health. This damages the kidney function, can get worse over time, and when the kidneys stop working completely, this is called kidney failure or end-stage renal disease. Not all patients with kidney disease progress to kidney failure. This disease has emerged as one of the most prominent reasons of death and suffering in this century. Recent studies states that, kidney disease affects most of the population and over two million people require kidney replacement. To help prevent Chronic Kidney Diseases and lower the risk for kidney failure, control risk factors for CKD, get tested yearly, make lifestyle changes, take medicine as needed. The detection of kidney abnormalities at their early stages helps to avoid the impairment of newlinekidney. The US imaging is considered as preliminary diagnostic tool in finding various kidney diseases in the clinical imaging field. This is one of the commonly used imaging modalities due to the inexpensiveness and non-ionization nature. The presence of noise in US images, degrade newlinethe quality and clarity of the images. Also, the heterogeneous structure of kidney, makes it very difficult to detect and measure the size of stones and cysts. Hence, an automatic kidney disease detection system is highly in demand. The proposed model can assist the radiologist in accurate abnormality detection. The proposed model includes different phases such as, pre-processing, features extraction, classification and newlinesegmentation. The pre-processing phase include cropping and noise removal. Further, the GLCM and intensity-based features are extracted for the classification of abnormal kidney images. -
Prognosis of relocation disease in animals using aggregation method with optimization techniques
In the most cutting-edge setting, health data is processed by machine learning algorithms, which are used to forecast illnesses. Dementia, especially Alzheimer's disease (AD), is a leading cause of diminished quality of life in the elderly. Early diagnosis by medical professionals increases the likelihood of reducing the aggressiveness of the disease. In this study, we develop a new uncertainty-based clustering model to handle the centroid selection ambiguity and the issue of noisy instances and outliers that lower the efficiency of prediction models. This work employs an uncertainty-based optimization technique to handle the unknown pattern of AD patients, since it is relatively tough to handle unknown patterns with unsupervised learning algorithms. Converting the instances in the AD dataset to the membership value of the dependent variable allows for an accurate determination of whether they belong as AD patterns or non-AD patterns. This proposed study takes a migration-based optimization method to animal migration, where the best instances are chosen as centroids and fresh instances are evaluated for clustering; this minimizes outliers throughout the clustering process by using comparable patterns. To make sure, we check the fitness values of each instance; the ones with the highest values are called centroids. To control the unknowns when dealing with outliers, the fuzzy Euclidean distance is employed. By comparing it to current state-of-the-art clustering methods, the OASIS dataset simulation results show that the proposed uncertainty-based Animal Migration optimization method (UAMO) performs better. 2026 selection and editorial matter, Dr. Poonam Nandal, Dr. Mamta Dahiya, Dr. Meeta Singh, Dr. Arvind Dagur, Dr. Brijesh Kumar. All rights reserved. -
Prognostic value of somatosensory-evoked potentials in neurology: A critical review in hypoxic encephalopathy
Prediction of prognosis in comatose patients surviving a cardiac arrest is still one of the intractable problems in critical care neurology because of lack of fool-proof ways to assess the outcome. Of all these measures, somatosensory-evoked potential (SSEP) has been perhaps the most evaluated and heavily relied-upon tool over the past several decades for assessing coma. Recent studies have given rise to concerns regarding the 'absoluteness' of SSEP signals for the prognostic evaluation of coma. In this critical review, we searched the literature to focus on studies conducted so far on the prognostic evaluation of postanoxic coma using SSEPs. All those studies published on the use of SSEP as a prognostication tool in postanoxic coma were reviewed. A narrative review was created that included the strengths as well as limitations of the use of SSEP in postanoxic coma. The use of SSEP in coma has been universal for the purpose of prognostication. However, it has its own advantages as well as limitations. The limitations include challenges in performing and getting SSEP signals during coma as well as the challenges involved in reading and interpreting the signals. The recent usage of therapeutic hypothermia has become another factor that often interferes with the SSEP recording. Finally, based on these study results, some recommendations are generated for the effective use of SSEPs in comatose patients for further prognostication. We advocate that SSEP should be an integral component for the assessment of postanoxic comatose patients due to its several advantages over other assessment tools. However, SSEP recorDings should follow certain standards. One should be aware that its interpretation may be biased by several factors. The bias created by the concept of 'self-fulfilling hypothesis' should always be borne in mind before discontinuation of life support systems in terminal patients. -
Progress in bio-based biodegradable polymer as the effective replacement for the engineering applicators
The development of biopolymers has significantly touched each and every sphere of human life due to their eco friendliness and biodegradability. In recent decades, the production of biopolymers gained profound attention due to the serious environmental concerns and threat to the non-renewable resources. The increased use of synthetic plastic in biomedical and engineering applications stays as a major threat to environment when these xenobiotics enter the food chain and soil upon their careless discharge after use. The significant material properties of plastic has made it as an inevitable part in our day to day life, but the concern over the environment directs the research focus on searching and developing biopolymers and bio composites as sustainable alternatives for their synthetic counterparts. Biopolymers of commercial interest can be majorly produced intracellularly by microbes or can be extracted through chemical or biological methods from plant and animal based substrates. The potential candidates with high market value with specific reference to biomedical engineering and tissue engineering include as polyhydroxyalkanoates, cellulose, chitosan and chitin, hydroxyapatite, and pectin. Despite of having high degree of biocompatibility, the major hurdle that retracts their widespread use commercially is attributed to the cost of production. This can be tackled out by exploiting cheap raw materials like agro waste as substrate and by employing green approaches over solvent based conventional extraction methods. The reduction in the material properties of purified biopolymers restricts their widespread application especially in the fabrication of thermoplastic blends. This can be resolved by production of bio composites with improved properties than their parent biopolymers. The current review focuses on the recent developments in biopolymer science especially with regard to its application in engineering majorly biomedical and tissue engineering. This study throws light on the biosynthetic pathways, extraction methods and applications of commercially important biopolymers. Furthermore, the challenges, limitations, and future prospects in the production and commercialization of biopolymers is briefly discussed in this review. 2022 Elsevier Ltd -
Progress in psycho-oncology with special reference to developing countries
Purpose of reviewPsycho-oncology has completed 25 years. There is growing recognition of the psychosocial needs of persons living with cancer and the role of sociocultural factors in addressing the needs. This review addresses the research in developing countries relating to distress associated with living with cancer and psychosocial care.Recent findingsThere is growing recognition of the emotional needs, understanding of the sociocultural aspects of the emotional responses of persons, caregivers, role of resilience and posttraumatic growth and spirituality in cancer care. Psychosocial aspects of cancer are largely influenced by social, economic, cultural, religious and health systems. A number of innovative approaches to care like use of yoga, financial and material support and involvement of caregivers have been implemented. A positive development is the increasing professional attention to document and develop innovative care programmes.SummaryA significant proportion of the general population are living with cancer. There are significant psychosocial needs largely influenced by social, economic, cultural, religious aspects of the communities. There are a wide range of interventions from self-care to professional care to address the needs. In developing countries, there is need for longitudinal studies of psycho-social experiences, develop interventions that are culturally appropriate, along with enhanced use of information technology along with evaluation of interventions. 2019 Wolters Kluwer Health, Inc. All rights reserved. -
Progression of Metamaterial for Microstrip Antenna Applications: A Review
This article provides an overview of the evolution of metamaterials (MTM) and all the aspects related to metamaterial development for antenna applications. It will be a useful collection of information for antenna researchers working in metamaterials applications. It gives an insight into the various metamaterial structures utilized along with miniature antenna designs. Different types of design parameters studied by the previous researchers are showcased to understand better perception of the metamaterial usage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Progressive crude oil distillation: An energy-efficient alternative to conventional distillation process
Distillation, the major process in crude oil refineries as of now. In this work we focused the attention to energy saving with respect to an industrial crude oil distillation unit. An alternative to the conventional crude oil distillation model present in the Bharat Petroleum Corporation, Kochi Refinery is proposed and simulated. The theoretical predictions as well as the simulated results indicate that the Progressive crude oil distillation reduces the utility burden as well as increase the extraction of more valuable light components. The simulation was carried out using Aspen HYSYS V8.8.2. Different crudes are taken into account and their properties and amount of distillate are analyzed. The optimization is done in an easy manner rather than the conventional mathematical method, together with the advanced process control tools; make it profitable in the operation in real time. 2018 Elsevier Ltd -
Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection
Modern technology like Artificial Intelligence (AI) must be used in the agricultural sectorif sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Prominent label identification and multi-label classification for cancer prognosis prediction
Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels from cancer databases and use them in a multi-class environment. The implementation consist of three phases namely, pre-processing, prominent label identification and multi-label classification. Breast, Colorectal and Respiratory Cancer Data sets have been used for the experimentation. Also random samples from all three data sets are generated to form a mixed cancer data. Patient survival, number of primaries and age at diagnosis are the prominent labels identified from others using the Decision tree, Nae Bayes and KNN algorithms. The three prominent labels have been tested using multi-label RAkEL algorithm to find the relations between them. The results of the empirical study are comparatively better than the traditional way of cancer prediction. 2012 IEEE. -
Promoting clean air and water for environmental sustainability in sundarbans
The Sundarbans, a UNESCO World Heritage Site, is crucial for biodiversity and the livelihoods of millions. To promote environmental sustainability in this unique mangrove ecosystem, a focus on clean air and water is paramount. Addressing pollution from industrial discharges and agricultural runoff is essential to protect the region's fragile ecology. Community awareness programs can educate locals about the importance of maintaining air and water quality. Implementing eco- friendly agricultural practices, such as organic farming, can reduce chemical pollutants. Collaboration with NGOs and governmental bodies can facilitate sustainable development initiatives and stricter regulations on waste disposal. Restoration projects for mangroves also play a vital role in improving water quality and sequestering carbon. By prioritizing clean air and water, we can ensure the health of the Sundarbans for future generations while supporting the diverse wildlife that thrives in this remarkable habitat. 2025, IGI Global Scientific Publishing. All rights reserved. -
Promoting Emotional Well-being and Mental Health through Student Mentorship During Human Emergencies
The aim of this chapter is to elucidate the factors that are important in maintaining emotional well-being and promoting mental health through student mentorship in higher education in times of a pandemic. The COVID-19 pandemic has prompted academic institutions to go online prompting a profound change in the pedagogical experience of students and their mentors. It has been a challenge to adapt to this new normal for many, and the socially distant lifestyle has procured novel shortcomings. The lack of focus on awareness of mental health and well-being among academic mentors has been proven to be detrimental to the students. The mental health and well-being of mentors are also a matter of concern in the present situation. Spreading awareness about emotional well-being, imparting the knowledge of positive psychology, and psychoeducation of mental health issues among students will facilitate better coping. Motivating mentors to enhance communication and arrange for outreach programmes can be beneficial to their students. The chapter focuses on these pressing needs in the path of pedagogical experience and aims to help mentors, in turn, help themselves and their students by promoting better mental health. 2025 selection and editorial matter, Kennedy Andrew Thomas and Joseph Varghese Kureethara; individuals, the contributors.