2821 research outputs found
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Implementing blockchain in human resource management for enhanced data privacy and regulatory compliance
Full text linkhe suggested answer, called Blockchain-Enhanced Safe HR Management (BESHRM), updates human resource management to the modern era by using blockchain technology to make data safer and more in line with rules. Here is a brief explanation of each of BESHRM's three main tactics. Blockchain Identity Protection (BIP), CSC, and Document Asset Management (DAM) are a few examples. Using secure hashing keeps BIP workers' names safe, and smart contracts make sure that CSC is following the rules of ethics. People who work for DAM use open tracking to make sure that the information they collect about human resources is correct. We are looking into the models and flowcharts that are used for the processes that are usually thought to be standard in the field of human resource management
Performance analysis of a gas turbine engine via intercooling and regeneration- Part 2
No full textThe current study aims to amplify the predictive
ability of the numerical model developed for a gas turbine
engine-based power plants by process of regeneration and
intercooling. Artificial neural networks (ANN) and adaptive
neuro-fuzzy interface systems (ANFIS) are the two techniques
mainly concentrated in this study which were not
properly implemented previously. The performance parameters
namely, specific power (SP), thermal efficiency (η), and
enthalpy based specific fuel consumption (EBSFC) of a
Turboprop engine were predicted using thermodynamic
parameters namely, pressure ratio (PR), nozzle pressure ratio (NPR), turbine inlet temperature (TIT), for constant regeneration (R), and intercooling (E) efficiencies. The results showed that a high regression result R2 of 0.9831 and 0.9899was found for the ANFIS model for η for training and testing, respectively. Also, the ANFIS model resulted in best performance of the performance characteristics when compared to ANN
Qualitative research methods for business students : a global approach
No full textQualitative Research Methods for Business Students: A Global Approach provides a practical and accessible overview of qualitative research methods and their philosophical underpinnings. The textbook will empower you with the knowledge and skills needed to navigate the dynamic landscape of qualitative research.
Key features include:
Case Studies which showcase how qualitative research has been instrumental in shaping business decisions and strategies.
Exercises and Activities that give you the opportunity to apply your learning.
Suitable for undergraduate and postgraduate students interested in using qualitative methods in their research project or dissertation
Precision agriculture for sustainability. Use of smart sensors, actuators, and decision support systems
No full textThis book provides a comprehensive exploration of the aspects of the current state-of-the-art digital technological intervention for precision agriculture for sustainable agricultural development. It delves into how modern technologies—i.e., global positioning systems (GPS), unmanned aerial vehicles (drones), image processing methods, artificial intelligence, machine learning, and deep learning—are being used in agriculture to make it more farmer-friendly and more economically profitable.
The volume discusses the use of smart sensors, actuators, and decision support systems for precision agriculture that provide intelligent data about crop health and for monitoring for yield prediction, soil quality, and nutrition requirement prediction, etc., using machine learning, deep learning, and artificial intelligence through a globally connected system via the Internet of Things (IoT).
The book begins with a section on AI in agriculture that looks at using satellite data for vegetation studies, AI-based solutions to increase farmer income, satellite images for yield prediction using machine learning algorithms, and more. The second section presents robotic-based innovations in agriculture, including agricultural field robots, along with cobots (computer-controlled robotic devices designed to people) used in and outside farms and greenhouses, methods for continual robotic monitoring of crops, robot-based weed identification and control systems, and more.
The section on intelligent computing in agriculture looks at soft computing methodologies and frameworks for yield forecasting for crop production, machine learning techniques to classify and identify plant diseases, machine learning algorithms to analyze all factors affecting crop yield and the climatic effect on produce, deep convolutional neural networks (DCNNs) for recognizing nutrient deficiencies, etc. The last section explores IoT in agriculture and provides an overview of the research that has gone into making smart precision agriculture a reality, IoT applications for smart garden plantation condition monitoring, smart agriculture that makes use of cloud computing and IoT, and much more.
The book covers artificial intelligence in agriculture, robotic-based innovations in agriculture, intelligent computing in agriculture, and the Internet of Things in agriculture, providing a rich resource on this exciting and developing area
A machine learning model for Alzheimer’s disease prediction
Full text linkAlzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged
people. Its symptoms are initially mild, but they get worse over time. Although this health
disease has no cure, its early diagnosis can help to reduce its impacts. In this paper, a
methodology SMOTE-RF is proposed for AD prediction. Alzheimer’s is predicted using
machine learning (ML) algorithms. Performance of three algorithms decision tree (DT),
extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open
Access Series of Imaging Studies (OASIS) longitudinal dataset available on Kaggle is used
for experiments. Dataset is balanced using synthetic minority oversampling technique
(SMOTE). Experiments are done on both imbalanced and balanced datasets. DT obtained
73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum 87.84%
accuracy on the imbalanced dataset. DT obtained 83.15% accuracy, XGB obtained 91.05%
accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. Maximum
accuracy of 95.03% is achieved with SMOTE-R
Ensemble approach and enhanced features for precise Bank Churn prediction analysis
No full textNumerous studies and research work has been undertaken in the area of creating predictive models for studying Bank Churn. In these studies, the end goal was to create a high accuracy predictive model; while this is commendable, this research focuses on creating an architecture for a predictive model by aggregating the power of various predictive models. The architecture and model proposed in this paper achieved an accuracy of 91% in the test data (35% of the original data set), and an AUC of 96% - confirming the generalized nature of the model. Also, various feature extrapolation techniques were introduced which provide valuable insights to the banking sector
Pixels to pathogens: a deep learning approach to plant pathology detection
Full text linkIt is known that accurately identifying, early and
timely treatment and elimination of the plant diseases is
essential for crop protection and healthy crop growth. In
traditional or conventional methods, identification and
classification were done by testing in laboratories or through
visual inspection by farmers. Now going through the testing in
labs is very time consuming, while the visual inspection requires
enough experience and knowledge. To solve this problem, our
study proposes a robust plant pathogen detection method based
on a Deep Learning approach on a large dataset containing
about 38 categories of different species like Maize, Potatoes,
Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases
like rust , molds, blight (late and early). This crop disease
detection model leverages the power of the EfficientNetB3
architecture, a state-of-art convolutional neural
network(CNN). The main backbone is served by
EfficientNetB3and then it is fine-tuned using different
hyperparameters and other regularization techniques like
weight decay, dropout method and optimizers like RAdam,to
enhance the overall accuracy coupled with dynamic learning
rate adjustment. In the testing set of the dataset, the proposed
model shows encouraging accuracy of about 99.25%, high
precision of about 97.35%. A thorough evaluation of the
model’s functionality is given by the help of training and
validation line chart and loss chart that gives the in-depth
information on the prediction. And then we implemented the
detection model in our mobile application whose interface
screen shots are given below. In the application the image can
be taken by camera or fed from folders and it will detect the
type of disease
Impact of involvement in mental health professional education on patient educators: a qualitative systematic review.
Full text linkObjectives. Patient involvement in mental health professional education is required by policy but lacks a robust evidence base. The impact of involvement in education on patients with mental health conditions may differ from that of patients with other conditions. This study aims to review the impact of involvement in mental health professional education on the patients with mental health conditions involved.
Setting. Electronic databases MEDLINE, PubMed, AMED, EMBASE, PsycINFO, Emcare, BNI, HMIC and CINAHL were systematically searched to find articles reporting on health professional teaching interventions involving patients with mental health conditions and the psychological, social or physical impact of involvement. The search took place in August 2023.
Results. Findings from 20 articles were amalgamated into four synthesised findings: (1) Impact of general involvement (2) impact of making a difference through teaching, (3) impact of new relationships and (4) impact of talking about experiences.
Conclusions. Patient involvement in mental health professional education can be beneficial for patients with mental health conditions when their experiences are respected and valued as expertise by students and academic staff. The experiences of patient educators in the mental health field are unique in that teaching activities interact with their mental health. Future research should evaluate patient involvement in the mental health field separately and report research findings according to reporting guidelines
Heat transfer of Ca (NO3)2‑KNO3 molten salt mixtures for austempering and martempering processes of steels
Full text linkMolten salts are highly effective as a quenching medium for
austempering and martempering processes, enabling precise control of
cooling rates to achieve the desired microstructures and mechanical
characteristics in steel components. One such promising molten salt is a
multicomponent Ca (NO3)2-KNO3 molten salt. The current work explores
the cooling severity of molten Ca (NO3)2-KNO3 mixtures, which are
commonly used for such purposes. The said mixture, with varying
concentrations and bath temperatures was used for quenching the Inconel
probe with thermocouples. The temperature data extracted was used to
determine the transient heat flux developed at the metal−quenchant
interface. A set of critical points were assessed against the peak heat
extraction rates. Additionally, the fluctuation of mean heat flux and surface
temperature in relation to these crucial points were plotted, along with
changes in composition and bath temperature of the quench media. The cooling intensity of these quench solutions, as measured by
Inconel probes, correlated well with the average hardness values observed in steel probes. The level of homogeneity in heat
transmission, as measured by the spatial variance of the normalized heat energy, decreased as the percentage of KNO3 in the quench
medium increased