Predictive Group Learning Behavior Approach and Inquiry Learning For Higher Education

Group work can inspire students, encourage constructive learning, and improve essential critical thinking, communication, and decision-making in the present competitive world. The risk factors in group learning include students who prefer working alone and strongly despise dealing with things created by gathering in teams. Online learners often have problems locating lasting peace times for group therapy sessions are considered an essential factor. A predictive Group Learning Behavior Approach (PGLBA) has been proposed. Students who commute to college agree to group conferences and workgroup learning and Inquiry learning for higher education. The grounded Inquiry Learning Approach is invented to strengthen students’ enjoyment of active group learning, and the students find times for group meetings that are often mutually advantageous. The simulation analysis is performed based on performance, accuracy, and efficiency proves the proposed framework’s reliability. The experimental results show that the proposed PGLBA-IL model enhances the accuracy ratio of 81.2%, an efficiency ratio of the number of students 86.4%, and the overall performance analysis ratio of 85.1% compared to others existing approaches.

Improvement of Association Algorithm and Its Application in Audit Data Mining

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.

The New Dimension to Resolution: Can it be resolved?

In the metrology community, there is an ongoing debate over which contributors to the Unit Under Test (UUT) belong in the expanded uncertainty calculation of the measurement process used for calibration. This is also known as Calibration Process Uncertainty (CPU); CPU is the denominator when calculating a Test Uncertainty Ratio (TUR). This paper presents examples that illustrate why the best practices outlined in documents such as ILAC-P14:09/2020 and the ANSI/NCSLI Z540.3 Handbook should be followed regarding the contributors for the CPU. Instead of drafting their own test protocols and standards, calibration laboratories and manufacturers are advised to correctly calculate both uncertainty and risk. Performing these calculations is part of an ethical approach to calibration that avoids shifting more risk to the Industry and ultimately mitigates global consumer's risk. Furthermore, outdated approaches to calculations, such as Test Accuracy Ratio (TAR), must be discontinued, and efforts to change the agreed-upon definition of Test Uncertainty Ratio (TUR) should cease since modern computing can provide measurements that are more accurate and reliable.

Evaluation of One- and Two-Box Models as Particle Exposure Prediction Tools at Industrial Scale

One- and two-box models have been pointed out as useful tools for modelling indoor particle exposure. However, model performance still needs further testing if they are to be implemented as trustworthy tools for exposure assessment. The objective of this work is to evaluate the performance, applicability and reproducibility of one- and two-box models on real-world industrial scenarios. A study on filling of seven materials in three filling lines with different levels of energy and mitigation strategies was used. Inhalable and respirable mass concentrations were calculated with one- and two-box models. The continuous drop and rotating drum methods were used for emission rate calculation, and ranges from a one-at-a-time methodology were applied for local exhaust ventilation efficiency and inter-zonal air flows. When using both dustiness methods, large differences were observed for modelled inhalable concentrations but not for respirable, which showed the importance to study the linkage between dustiness and processes. Higher model accuracy (ratio modelled vs. measured concentrations 0.5–5) was obtained for the two- (87%) than the one-box model (53%). Large effects on modelled concentrations were seen when local exhausts ventilation and inter-zonal variations where parametrized in the models. However, a certain degree of variation (10–20%) seems acceptable, as similar conclusions are reached.

Intelligent Security Schema for SMS Spam Message Based on Machine Learning Algorithms

<p class="0abstract">SMS spam messages represent one of the most serious threats to current traditional networks. These messages have been particularly prevalent overseas and are harmful to various types of devices. The current filtering scheme employed in conventional systems is unable to expose a large number of messages. To resolve this issue, a new intelligent security system is proposed to reduce the number of spam messages. It can detect novel spam messages that have a direct and negative impact on networks. The proposed system is heavily based on machine learning to explore various types of messages. The primary achievement of our study is the increase in the accuracy ratio as well as the reduction in the number of false alarms. According to the experimental results, it is clear that our system can realize outstanding results, detecting a massive number of massages.</p>

Assessing the Accuracy of Sales Forecasts Submitted by Pharmaceutical Companies Applying for Reimbursement in Austria

Objectives: Reimbursement decisions on new medicines require an assessment of their value. In Austria, when applying for reimbursement of new medicines, pharmaceutical companies are also obliged to submit forecasts of future sales. We systematically examined the accuracy of these pharmaceutical sales forecasts and hence the usefulness of these forecasts for reimbursement evaluations. Methods: We retrospectively analyzed reimbursement applications of 102 new drugs submitted between 2005 and 2014, which were accepted for reimbursement outside of hospitals, and for which actual reimbursed sales were available for at least 3 years. The main outcome variable was the accuracy ratio, defined as the ratio of forecasted sales submitted by pharmaceutical companies when applying for reimbursement to actual sales from reimbursement data. Results: The median accuracy ratio [95% confidence interval] was 1.33 [1.03; 1.74, range 0.15–37.5], corresponding to a median overestimation of actual sales by 33%. Forecasts of actual sales for 55.9% of all examined products either overestimated actual sales by more than 100% or underestimated them by more than 50%. The accuracy of sales forecasts did not show systematic change over the analyzed decade nor was it discernibly influenced by reimbursement status (restricted or unrestricted), the degree of therapeutic benefit, or the therapeutic area of the pharmaceutical product. Sales forecasts of drugs with a higher degree of innovation and those within a dynamic market tended to be slightly more accurate. Conclusions: The majority of sales forecasts provided by applicants for reimbursement evaluations in Austria were highly inaccurate and were on average too optimistic. This is in line with published results for other jurisdictions and highlights the need for caution when using such forecasts for reimbursement procedures.

A State of the Art Technology with Machine Intelligence in Human Emotion Recognition

Emotion plays a significant role in human understanding and generally connected with rational decision making, attitudes, human activity, and human intelligence. To create realistic emotional relations between human beings and machines, the research community’s increasing interests need reliable and deployable solutions to recognize human emotional conditions. Automatic emotion detection is one of the main obstacles in providing innovative methods for more comfortable and more objective diagnosis, communication and analysis. Hence in this paper, an Artificial intelligence-assisted emotion prediction model (AIEPM) has been proposed to evaluate the probability of digital representation, identification and estimation of feelings, their state-of-the-art methods and primary research guidance. The proposed AIEPM analyses the effect on multimodal detection of emotional models. This paper presents emerging works based on language, sound, image, film, and physiological signals using current methods such as machine intelligence for recognizing human emotion. The proposed emphasis on this cutting-edge analysis reflects elements like the form and presentation of emotional stimulation, the sample’s scale. The numerical outcome suggested AIEPM, according to age, behaviour classification according to age (95.2%), emotions state recognition high probability in satisfaction compared to the proposed method, accuracy ratio (89.6%), performance ratio (94.5%) and recognition outcomes of existing and proposed work (98.2%) compared to other existing approaches improve behaviour classification.

Smart Face Detection and Recognition in Low Resolution Images Using Alexnet CNN Compare Accuracy with SVM

Aim: Machine learning algorithm plays a vital role in various biometric applications due to its admirable result in detection, recognition and classification. The main objective of this work is to perform comparative analysis on two different machine learning algorithms to recognize the person from low resolution images with high accuracy. Materials & Methods: AlexNet Convolutional Neural Network (ACNN) and Support Vector Machine (SVM) classifiers are implemented to recognize the face in a low resolution image dataset with 20 samples each. Results: Simulation result shows that ACNN achieves a significant recognition rate with 98% accuracy over SVM (89%). Attained significant accuracy ratio (p=0.002) in SPSS statistical analysis as well. Conclusion: For the considered low resolution images ACNN classifier provides better accuracy than SVM Classifier.

Pervasive intelligent multi-node health monitoring system for monitoring basketball players health and energy using IoT and 6G technology

Effective health monitoring of players in team sports like basketball allows for understanding external requirements and internal response concerning exercise and competition phases. The explosive growth of wireless devices stimulates the advancement of the internet-of-things (IoT) and 6G technologies, capable of connecting enormous and various “things” through wireless communications. Players face health issues while playing basketball are severe lower body lesions like ankle sprains, shortness of breath, teeth, head, fingers, and hand. To overcome these issues, in this paper, the Pervasive Intelligent Multi-node Health Monitoring System (PIMN-HMS) has been proposed for basketball player’s continuous health tracking based on IoT and 6G communication. With the aid of wearable monitoring sensors to gathers health information and monitor exercise records. The system consists of several sensor nodes, a network coordinator, which monitors physical movements and heart rate, and a personal server on a personal digital assistant using 6G networks. The numerical results have been performed, and the suggested PIMN-HMS model enhances the accuracy ratio of 96.7%, prediction ratio of 97.3%, low latency ratio of 11.2%, delay rate of 22.3%, and efficiency ratio of 98.7% compared to other existing models.