bluuebunny/crossref_metadata_2025 huggingface dataset:
Survey on Insurance Claim analysis using Natural Language Processing and Machine Learning
Authors: Sapana Kolambe Et al.
Published: December 2023

In the insurance industry nowadays, data is carrying the major asset and playing a key role. There is a wealth of information available to insurance transporters nowadays. We can identify three major eras in the insurance industry's more than 700-year history. The industry follows the manual era from the 15th century to 1960, the systems era from 1960 to 2000, and the current digital era, i.e., 2001-20X0. The core insurance sector has been decided by trusting data analytics and implementing new technologies to improve and maintain existing practices and maintain capital together. This has been the highest corporate object in all three periods.AI techniques have been progressively utilized for a variety of insurance activities in recent years. In this study, we give a comprehensive general assessment of the existing research that incorporates multiple artificial intelligence (AI) methods into all essential insurance jobs. Our work provides a more comprehensive review of this research, even if there have already been a number of them published on the topic of using artificial intelligence for certain insurance jobs. We study algorithms for learning, big data, block chain, data mining, and conversational theory, and their applications in insurance policy, claim prediction, risk estimation, and other fields in order to comprehensively integrate existing work in the insurance sector using AI approaches.
Image Retrieval Using Auto Encoding Features In Deep Learning
Authors: Syed Qamrul Kazmi Et al.
Published: December 2023

The latest technologies and growth in availability of image storage in day to day life has made a vast storage place for the images in the database. Several devices which help in capturing the image contribute to a huge repository of images. Keeping in mind the daily input in the database, one must think of retrieving those images according to certain criteria mentioned. Several techniques such as shape of the object, Discrete Wavelet transform (DWT), texture features etc. were used in determining the type of image and classifying them. Segmentation also plays a vital role in image retrieval but the robustness is lacking in most of the cases. The process of retrieval mainly depends on the special characteristics possessed by an image rather than the whole image. Two types of image retrieval can be seen. One with a general object and the other which may be specific to some type of application. Modern deep neural networks for unsupervised feature learning like Deep Autoencoder (AE) learn embedded representations by stacking layers on top of each other. These learnt embedded-representations, however, may degrade as the AE network deepens due to vanishing gradient, resulting in decreased performance. We have introduced here the ResNet Autoencoder (RAE) and its convolutional version (C-RAE) for unsupervised feature based learning. The proposed model is tested on three distinct databases Corel1K, Cifar-10, Cifar-100 which differ in size. The presented algorithm have significantly reduced computation time and provided very high image retrieval levels of accuracy.
Shielding against Web Application Attacks - Detection Techniques and Classification
Authors: Sathya Priya.S Et al.
Published: December 2023

The field of IoT web applications is facing a range of security risks and system attacks due to the increasing complexity and size of home automation datasets. One of the primary concerns is the identification of Distributed Denial of Service (DDoS) attacks in home automation systems. Attackers can easily access various IoT web application assets by entering a home automation dataset or clicking a link, making them vulnerable to different types of web attacks. To address these challenges, the cloud has introduced the Edge of Things paradigm, which uses multiple concurrent deep models to enhance system stability and enable easy data revelation updates. Therefore, identifying malicious attacks is crucial for improving the reliability and security of IoT web applications. This paper uses a Machine Learning algorithm that can accurately identify web attacks using unique keywords. Smart home devices are classified into four classes based on their traffic predictability levels, and a neural system recognition model is proposed to classify these attacks with a high degree of accuracy, outperforming other classification models. The application of deep learning in identifying and classifying attacks has significant theoretical and scientific value for web security investigations. It also provides innovative ideas for intelligent security detection by classifying web visitors, making it possible to identify and prevent potential security threats.
Hast Mudra: Hand Sign Gesture Recognition Using LSTM
Authors: Riddhi Mirajkar Et al.
Published: December 2023

Even though using the most natural way of communication is sign language, deaf and mute people find it challenging to socialize. A language barrier is erected between regular people and D&M individuals due to the structure of sign language, which is distinct from text. They converse by using vision-based communication as a result. The gestures can be easily understood by others if there is a standard interface that transforms sign language to visible text. As a result, R&D has been done on a vision-based interface system that will allow D&M persons to communicate without understanding one another's languages. In this project, first gathered     and acquired data and created a dataset, after which extracting useful data from the images. Keywords After verification and trained data and model using the (LSTM) algorithm, TensorFlow, and Keras technology, classified the gestures according to alphabet. Using our own dataset, this system achieved an accuracy of around 86.75% in an experimental test. system uses the (LSTM) algorithm to process images and data.
Unleashing the Power of VGG16: Advancements in Facial Emotion Recognization
Authors: P V V S Srinivas Et al.
Published: December 2023

In facial emotion detection, researchers are actively exploring effective methods to identify and understand facial expressions. This study introduces a novel mechanism for emotion identification using diverse facial photos captured under varying lighting conditions. A meticulously pre-processed dataset ensures data consistency and quality. Leveraging deep learning architectures, the study utilizes feature extraction techniques to capture subtle emotive cues and build an emotion classification model using convolutional neural networks (CNNs). The proposed methodology achieves an impressive 97% accuracy on the validation set, outperforming previous methods in terms of accuracy and robustness. Challenges such as lighting variations, head posture, and occlusions are acknowledged, and multimodal approaches incorporating additional modalities like auditory or physiological data are suggested for further improvement. The outcomes of this research have wide-ranging implications for affective computing, human-computer interaction, and mental health diagnosis, advancing the field of facial emotion identification and paving the way for sophisticated technology capable of understanding and responding to human emotions across diverse domains.
On the Development of Machine Learning Based Real-Time Stress Monitoring : A Pilot Study
Authors: V. H. Ashwin Et al.
Published: December 2023

During specific environmental changes, the human body regulates itself through emotional, physical or mental responses. One such response is stress. The psychological and physical stability of an individual may be affected by recurrent occurrences of acute stress. This often leads to anxiety disorder, other psychological illnesses, hypertension, and other physiological disorders. The work performance of the individual is also negatively affected due to long-term stress. Across various age groups, the global population is primarily influenced by anxiety, depression and psychological stress. The long-term adverse effects of stress can be mitigated by effectively monitoring and managing stress through a cost-efficient and reliable stress detection system.  This paper mainly focuses on stress detection using a machine-learning approach. Wearable sensor data from electroencephalogram (EEG) and electrocardiogram (ECG) are considered during exposure to stress and the level of stress undergone by the participant is further analyzed. This approach helps in stress detection, analysis and mitigation, which in turn improves the quality life of people. Machining Learning technique k-means clustering algorithm is used after removal of artifacts to obtain case-specific clusters that segregate features pointing to non-stress and stress periods.  The results of the proposed K-means clustering algorithm are compared to state-of-the-art techniques such as Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF) and Support Vector Machine (SVM). From the results, it was concluded that the proposed algorithm outperformed the other with an accuracy of 96% in the overall analysis.
Emotional Storyteller for Vision Impaired and Hearing-Impaired Children
Authors: Abilash Kengatharan Et al.
Published: December 2023

Tellie is an innovative mobile app designed to offer an immersive and emotionally enriched storytelling experience for children who are visually and hearing impaired. It achieves this through four main objectives: Text extraction utilizes the CRAFT model and a combination of Convolutional Neural Networks (CNNs), Connectionist Temporal Classification (CTC), and Long Short-Term Memory (LSTM) networks to accurately extract and recognize text from images in storybooks. Recognition of Emotions in Sentences employs BERT to detect and distinguish emotions at the sentence level including happiness, anger, sadness, and surprise. Conversion of Text to Human Natural Audio with Emotion transforms text into emotionally expressive audio using Tacotron2 and Wave Glow, enhancing the synthesized speech with emotional styles to create engaging audio narratives. Conversion of Text to Sign Language: To cater to the Deaf and hard-of-hearing community, Tellie translates text into sign language using CNNs, ensuring alignment with real sign language expressions. These objectives combine to create Tellie, a groundbreaking app that empowers visually and hearing-impaired children with access to captivating storytelling experiences, promoting accessibility and inclusivity through the harmonious integration of language, creativity, and technology. This research demonstrates the potential of advanced technologies in fostering inclusive and emotionally engaging storytelling for all children.
A Novel Communication Pathway Metric Evaluation using Throughput and Energy Improvements over Wireless Sensor Networks
Authors: M Dilli Babu Et al.
Published: December 2023

Lifetime connectivity and low power consumption are two requirements for WSNs. Additionally, increasing WSN commercialization, application monitoring for higher QoS. Maintaining the improvement of the wireless sensor networks effectiveness requires the establishment of the energy-efficient and consistent connection. In order to increase the efficiency of routing algorithms in latest technology assisted WSNs, a novel method is developed. The gadgets with the lowest power consumption are carefully selected for the appropriate use and carefully managed. The goal of this research is to develop low-power routing algorithms for use in WSNs. Numerous sectors, including the military, the medical profession, surveillance of observing, public transportation sectors etc., can benefit from wireless sensor networks deployed with a large number of mobile nodes in a communications system. To examine data from nodes that are freely moving about the zone, we developed and simulated a wireless network system based on the Distributed Internet of Things. The proposed method of Intelligent Route Metric Analyzer (IRMA) improves upon the efficiency of the standard algorithm with regards to energy consumption. In addition, the ground wireless sensing design's ideal trajectory is identified using a realistic tempering approach. In the end, this concept is compared to similar ones like AODV and the findings reveal that it performs better with regard to of energy consumption, delay and other important metrics. According to the paper we analyzed, the primary objective was to devise a routing method that would sustain network operation for as long as feasible by decreasing the energy needed for different operations at each individual sensor node while keeping the total energy consumption of the nodes constant over their lifetimes. Finally, the study makes a contribution to the ongoing discussion of the difficulties in developing a routing protocol for WSN, taking into account the interdependency of different network factors. Median throughput, median network latency, and standardized route load are only few of the efficiency metrics addressed in this article.
A Hybrid Resampling Approach for Multiclass Skewed Datasets and Experimental Analysis with Diverse Classifier Models
Authors: Rose Mary Mathew Et al.
Published: December 2023

In real-life scenarios, imbalanced datasets pose a prevalent challenge for classification tasks, where certain classes are heavily underrepresented compared to others. To combat this issue, this article introduces DOSAKU, a novel hybrid resampling technique that combines the strengths of DOSMOTE and AKCUS algorithms. By integrating both oversampling and undersampling methods, DOSAKU significantly reduces the imbalance ratio of datasets, enhancing the performance of classifiers. The proposed approach is evaluated on multiple models employing different classifiers, and the results demonstrate its superiority over existing resampling measures, making it an effective solution for handling class imbalance challenges. DOSAKU's promising performance is a substantial contribution to the field of imbalanced data classification, as it offers a robust and innovative solution for improving predictive model accuracy and fairness in real-world applications where imbalanced datasets are common.
Quadrupole Electromagnetic Linear Positioning System (QELPS):
Authors: Kintali Manohar Et al.
Published: December 2023

In linear motion systems, including linear motors and actuators, precise and controlled linear motion is provided for various applications. However, they have several drawbacks: high costs, complexity, limited stroke length, high energy consumption, speed limitations, heat generation, noise and vibration, limited load capacity, environmental considerations, and integration challenges. High costs are especially significant for applications requiring high precision. The components' complexity and additional control electronics can increase maintenance and trouble-shooting requirements. Ensuring accurate and efficient operation necessitates regular maintenance. The limitation in stroke length, determined by the drive's size and guide length, can pose challenges for applications requiring long strokes. High energy consumption can be a concern, and speed limitations may be challenging. Managing heat generation is crucial to prevent component damage. Noise and vibration can be problematic, particularly in quiet applications. Integration challenges can arise when dealing with complex systems or automation processes. To overcome some of these drawbacks, an innovative coil configuration design for linear positioning system applications is proposed. The proposed design focuses on the Quadrupole Electromagnetic linear Positioning System (QELPS), comprising four coils generating a uniform electromagnetic field to produce a Lorentz force on the slider. The QELPS design is meticulously crafted using 3D modeling in ANSYS software, and the magnetic characteristics indicate the potential for scaling this model to different levels. The power circuit of the QELPS is simulated using ANSYS Simplorer and incorporates silicon-controlled rectifiers (SCR) and a pulse width modulation (PWM) pulse generator. The design achieves a force of 27.6 newtons with the paper presenting current and force plots in comprehensive detail. Furthermore, an interactive design algorithm is introduced, facilitating the customization of this model for various linear track dimensions. This research aims to advance linear drive technology and enhance linear motion applications by developing this new coil configuration design and harnessing the Quadrupole Electromagnetic System.
Employee Attrition Prediction based on Grey Wolf Optimization and Deep Neural Networks
Authors: G Pratibha Et al.
Published: December 2023

Despite the constructive application of promising technologies such as Neural Networks, their potential for predicting human resource management outcomes still needs to be explored. Therefore, the primary aim of this paper is to utilize neural networks and meta-heuristic technologies to predict employee attrition, thereby enhancing prediction model performance. The conventional Grey Wolf optimization optimization (GWO) has gained substantial attention notice because of its attributes of robust convergence, minimal parameters, and simple implementaton. However, it encounter problems with slow convergence rates and susceptibility to local optima in practical optimization scenarios. To address these problems, this paper introduces an enhanced Grey Wolf Optimization algorithm incorporating the utilization of Cauchy-Gaussian mutation, which contributes to enhancing diversity within the leader wolf population and enhances the algorithm's global search capabilities. Additionally, this work preserves exceptional grey wolf individuals through a greedy selection of 2 mechanisms to ensure accelerated convergence. Moreover, an enhanced exploration strategy is suggested to expand the optimization possibilities of the algorithm and improve its convergence speed. The results shows that the proposed model achieved the accuarcy of 97.85%, precision of  98.45%, recall of 98.14%, and f1-score of 97.11%. Nevertheless, this paper extends its scope beyond merely predicting employee attrition probability and activities to enhance the precision of such predictions by constructing an improved model employing a Deep Neural Network (DNN)..
Dermatological Detection and Classification using Machine Learning Techniques
Authors: Sarangam Kodati Et al.
Published: December 2023

Dermatology is the medical field that focuses on the study and treatment of skin conditions. It is a specialized branch of medicine that encompasses both diagnostic and surgical procedures related to the skin. It is a widespread disease among them. The researchers have shows a lot of attention to the early detection of lesions. Because of their proliferation ability to other parts of the body, death rates are quite high. A system that can distinguish between benign and malignant lesions is essential because melanoma can be cured with an early and accurate diagnosis. Dermoscopic skin lesion images are first segmented using data mining techniques, to identify the area of interest of the lesion part. When compared to individual classifier algorithms, dermatology datasets benefit from the various data mining techniques and feature selection methods. The SVM provides more accurate and effective skin disease prediction in terms of accuracy, precision, and Specificity.
An Innovative Deep Learning Method to Diagnose Mosquito-Borne Illnesses in Blood Image Analysis
Authors: Shaimaa Saadoon Mahmood ALrfae Et al.
Published: December 2023

Introduction: Malaria, an infectious illness carried by the bite of infected mosquitoes, is a significant public health concern, especially in Africa. The management of mosquito-human contact is crucial to mitigate its transmission. Artificial intelligence, including machine learning and deep learning techniques, is being utilized to enhance the diagnosis and identification of mosquito species. This advancement aims to facilitate the development of more efficient control measures.

Aims and Objective: To analyze the efficiency of three deep learning models in identifying blood-borne diseases by evaluating the macro and micro picture of blood samples.

Method: In this retrospective investigation, three deep learning algorithms, namely Convolutional Neural Networks (CNN), MobileNetV2, and ResNet50, were used to identify mosquito-borne illnesses, focusing on malaria. The research used a dataset of 120 blood samples gathered over one year from the hospital's pathology department. The CNN model streamlines preprocessing with multilayer perceptrons, simplifying malaria component extraction. MobileNetV2, a lightweight network, outperforms others with fewer parameters. Its compact blocks in Dense-MobileNet models minimize constraints and computation expenses. ResNet50 resolves degradation issues with a residual structure, preventing overfitting as hidden layers increase.

Results: The study evaluated three deep learning models (CNN, MobileNetV2, and ResNet50) for medical classification. The study also demonstrated improved True Positive Rates as False Positive Rates increased, indicating better accurate identification while controlling false positives. ResNet50 consistently outperformed the other models, showcasing its superior performance. The study revealed high precision scores for all models, classifying "Uninfected" and "Infected" cases. ResNet50 exhibited slightly higher precision, indicating its precision-based superiority. Overall, all models demonstrated vital accuracy, and ResNet50 showed exceptional performance. The study found that ResNet50 performs better in True Positive and False Positive Rates.

Conclusion: The study has concluded that ResNet50 has shown the best performance in detecting blood-borne diseases.
Colon Cancer Detection by using Transfer Learning
Authors: V T Ram Pavan Kumar Et al.
Published: December 2023

Cancer detection at early stage will provide the chance of existence of living. Using image processing techniques medical image analysis becoming easier and exact stage of cancer is also detected. In this work transfer learning technique is used to identify the Colon Cancer.
Exploring the Dynamics of Football Tourism in Chengdu city, China: A Structural Equation Modeling Approach to Understanding Tourists’ Intention
Authors: Pei Diao Et al.
Published: December 2023

The burgeoning field of football tourism represents a significant intersection of sports enthusiasm and travel, yet its dynamics, particularly in emerging markets like Chengdu, China, remain underexplored. The research aimed to elucidate how various factors, including motivation for football tourism, online word of mouth, subjective norms, destination image, and attitudes, influence the intention to engage in football tourism in Chengdu. With an emphasis on a quantitative survey to gather empirical data. The survey reached fans of various football clubs who had experience with football tourism in Chengdu, resulting in 609 valid responses. The data were analyzed using Structural Equation Modeling (SEM), employing SPSS 27.0 and AMOS 27.0 for descriptive statistical analysis, reliability, validity tests, and model construction. Key findings revealed that motivation, online word of mouth, and subjective norms significantly influence both the image of football tourism destinations and attitudes towards football tourism. These, in turn, have a notable impact on the intention to visit football tourism destinations. The study underscores the importance of destination image and social influences in shaping tourist attitudes and intentions, offering insights into the decision-making processes of potential football tourists. In conclusion, this research provides a deeper understanding of the determinants of football tourism in Chengdu. It offers valuable insights for stakeholders in optimizing strategies and offerings to capitalize on the growing market of football tourism, emphasizing the role of digital platforms and social factors in shaping tourist preferences and decisions.
Modern Approaches to Uncertain Database Exploration from Categorizing Data to Advanced Mining Solutions
Authors: Sridevi Malipatil Et al.
Published: December 2023

In today's digitized era, the ubiquity of data from diverse sources has introduced unique challenges in database management, notably the issue of data uncertainty. Uncertainty in databases can arise from various factors – sensor inaccuracies, human input errors, or inherent vagueness in data interpretation. Addressing these challenges, this research delves into modern approaches to uncertain database exploration. The paper begins by exploring methods for categorizing data based on certainty levels, emphasizing the importance and mechanisms to distinguish between certain and uncertain data. The discussion then transitions to highlight pioneering mining solutions that enhance the utility of uncertain databases. By integrating state-of-the-art techniques with traditional database management principles, this study aims to bolster the reliability, efficiency, and versatility of data mining in uncertain contexts. The implications of these methods, both theoretically and in real-world applications, hold the potential to redefine how uncertain data is perceived and utilized in diverse sectors, from healthcare to finance.
Dimensionality Reduction using Feature Selection Techniques on EDM for Student Academic Performance Prediction
Authors: Kajal Mahawar Et al.
Published: December 2023

The aim of this study is to perform dimensionality reduction on student datasets using different feature selection strategies in order to better forecast students' academic progress and minimize high-dimensionality concerns. The effectiveness of models built utilizing classification algorithms and pertinent features chosen using different feature selection strategies has been experimentally evaluated in this study. Nine feature selection methods, including Chi-Square, Pearson Correlation, Variance Threshold, Feature Importance, Recursive Feature Elimination (RFE), Lasso, Ridge, Random Forest, and XGBoost, as well as one classification method, logistic regression, were applied to the student dataset to determine the results of the analysis.  In this experiment, dimensionality reduction techniques were used to reduce the number of features from 70 to the optimal 21 feature subsets for analysis of student academic performance prediction. Five measures were used to assess the effectiveness of the techniques: r2, precision, recall, f-score, and accuracy. With the logistic regression classifier, the feature subset chosen by the chi-square had the highest accuracy of 72.4%, precision of 71.4%, recall of 72.4%, f-score of 70%, and r-square of -0.221%.
Qualitative Evaluation of Modified Polyvinyl Alcohol Gel Used as A Preservative Medium Compared to Traditional Modified Kaiserling Iii Solution in Pathology Museum
Authors: Shantiram Goswami S.
Published: December 2023

Pathology and anatomy museums preserve body tissues and organs traditionally in modified Kaiserling III solution and display in transparent glass jars, which serve as valuable teaching materials for students. Other techniques tried in preserving specimens are embedding in molten wax, embedding in epoxy resin, and plastination techniques. Modified Carbopol-940 gel has been tried recently as a preservative mounting medium in pathology museum. However, the use of modified Polyvinyl alcohol (PVA) gel as a preservative medium is not mentioned in the literature. Gel preservative medium can prevent leakage, toxic vapors, and irritation of eyes and skin.  Aims: Qualitative evaluation of modified Polyvinyl alcohol gel as a preservative medium for specimens in pathology museums compared to modified Kaiserling III solution over one year. Objectives:  1) To qualitatively evaluate physical integrity of mounted tissue specimens stored in modified Kaiserling III solution and modified Polyvinyl alcohol gel for one year.  2)  To qualitatively evaluate physical, chemical and microbiological parameters of modified Kaiserling III solution and modified Polyvinyl alcohol gel for one year. Materials and methods: In this qualitative cross-sectional study, six different organs, two sets each, were preserved in jars containing modified Kaiserling III solution and modified Polyvinyl alcohol gel.  The media were evaluated for stability by physical tests, chemical test, and microbiological test, before and after the study. The specimens were assessed for physical disintegration by observing for tissue shredding, and evaluated for histopathological appearances, before and after the study. Results showed mild variability in stability of the preservative media but   good tissue integrity of the specimens over a period of one year.
Performance of Broiler Chicken on Diet Supplemented with Black Pepper (Piper Nigrum) Powder on Haematological and Biochemical Parameters
Authors: Dozo Vineituono , Savino N. , Vidyarthi V. K. , Zuyie R. , Nizamuddin Nizamuddin , Rutsa M. Catherine , Singh Rajan
Published: December 2023

The present research work was conducted with the aim to find the different levels of black pepper powder supplementation on the performance in terms of growth, feed intake, carcass characteristics, blood parameters, overall performance and cost of rearing of broiler chicken. For this, a total of 120- day old, broiler chicks of hybrid Cobb-400 strain, were equally distributed into four treatments (T1, T2, T3 and T4) with five replications per treatment following Randomized Block Design. The birds were reared for 42 days. The birds in control group (T1) were provided with standard diets of broiler starter and broiler finisher. However, the diets of other groups were supplemented with black pepper powder @7.5, 10.0 and 12.5 g/kg feed for T2, T3 and T4 groups, respectively. Feed intake was recorded daily and weekly body weight was recorded replication wise. Gain in weight and feed conversion efficiency was calculated accordingly. The inputs of all items were precisely recorded for calculation of economics of rearing. On the 42nd day, from each treatment three birds were selected for collection of blood analysis viz. Hb, WBC, RBC, HDL, LDL and Cholesterol. No mortality was observed for all the treatments. The results showed no significant difference in body weight, body weight gain, feed intake, feed conversion efficiency, overall performance index and net profit per bird. In terms of blood constituents viz, RBC, WBC, Cholesterol were better in T1 group.
Applications of Datamining Techniques for Predicting the Post - Covid 19 Symptoms in Saudi Arabia, Jazan
Authors: Rasitha Banu  , N. Sasikala  , Amal Ramadan  , Thani Babikar  , Maha Yousif Rizgalla  , Ashraf Abdelmageid Ibrahim khattab
Published: December 2023

Background The entire world was combating COVID-19; however, a significant proportion of patients demonstrate the persistence of some COVID-19 symptoms, new symptom development, or exaggeration of pre-existing disease after a negative viral load. They are referred to as a post-COVID-19 syndrome. According to various researches, COVID-19 has a wide range of long-term effects on virtually all systems, including the respiratory, cardiovascular, gastrointestinal, neurological, mental, and dermatological systems. Finding the various symptoms of post-acute and chronic is critical since they might have a significant impact on the patients' everyday functioning. As a result, we aimed to distinguish the symptoms immediately after the initial phase in which the symptoms affected them for more than three weeks using data mining techniques. Methodology: Post-COVID conditions do not affect everyone the same way. They can cause various types and combinations of symptoms in different people. The purpose of this research is to analyse the complications of post covid-19 syndrome. The purpose of Data mining is for discovering the knowledge from vast amount of database. To classify the symptoms of post covid-19, data mining techniques is used. In this study, ranking method was used in preprocessing to select subset of attributes for strengthening the rate of accuracy of classifiers. The data were collected through Google form of 384 household of students from Public Health College in Jazan University. The WEKA open-source software is used for this research work under Windows7 environment. An experimental study is carried out using data mining technique such as J48 and Random Forest tree. The data records are classified as six categories such as General symptoms, Nervous symptoms, Respiratory symptoms, Heart symptoms, Digestive symptoms and normal. Result: The performances of classifiers are evaluated through the confusion matrix in terms of accuracy, time taken to build the Model and error rate. It has been concluded that Random Forest Tree gives better accuracy, minimum time taken to build the model and less error rate than the J48 classifier.
Testing and Design of Discriminants for Local Seismic Events Recorded during the Redmond Salt Mine Monitoring Experiment
Authors: Tibi Rigobert , Downey Nathan , Brogan Ronald
Published: December 2023

The Redmond Salt Mine (RSM) Monitoring Experiment in Utah was designed to record seismoacoustic data at distances less than 50 km for algorithm testing and development. During the experiment from October 2017 to July 2019, six broadband seismic stations were operating at a time, with three of them having fixed locations for the duration, whereas the three other stations were moved to different locations every one-and-half to two-and-half months. RSM operations consist of nighttime underground blasting several times per week. The RSM is located in proximity to a belt of active seismicity, allowing direct comparison of natural and anthropogenic sources. Using the recorded data set, we built 1373 events with local magnitude (ML) of −2.4 and lower to 3.3. For 75 blasts (RMEs) from the Redmond Salt Mine and 206 tectonic earthquakes (EQs), both ML and the coda duration magnitude (MC) are well constrained. We used these events to test and design discriminants that separate the RMEs from the EQs and are effective at local distances. The discriminants consist of ML−MC, low-frequency Sg to high-frequency Sg, Pg/Sg phase-amplitude ratios, and Rg/Sg spectral amplitude ratios, as well as different combinations of two or more of these classifiers. The areas under the receiver operating characteristic curves (AUCs) of 0.92–1.0 for ML−MC, low-frequency Sg to high-frequency Sg, and Rg/Sg indicate that these discriminants are very effective. Conversely, the AUC of only 0.57 for Pg/Sg suggests that this discriminant is only slightly better than a random classifier. Among the effective classifiers, Rg/Sg, shows the lowest likelihood of misclassification (4.3%) for the populations. Results of joint discriminant analyses suggest that even the arguably ineffective single classifier, like Pg/Sg in this case, can provide some value when used in combination with others.
Severe Acute Respiratory Syndrome Coronavirus 2 and the Digestive System
Authors: Paolo Sossai  , Silvia Uguccioni  , Francesco Amenta
Published: December 2023

The coronaviruses belong to a family of related RNA viruses that notoriously spread through airborne transmission and were first identified in 1965 by Tyrrel and Bynoe[1]. The viruses of this family had contributed to two large epidemics, namely, the Severe Acute Respiratory Syndrome (SARS) in 2003 with a lethality rate of approximately 10% and Middle East Respiratory Syndrome in 2013 with a lethality rate of approximately 35%.
Spinal anesthesia efficiency in thoracolumbar stabilizations
Authors: Akgul Mehmet Huseyin , Akgun Mehmet Yigit
Published: December 2023

Background and purpose – Spinal surgery has an important place in neurosurgery practice. Surgical procedures on the lumbar spine include stabilization, discectomy, foraminotomy and decompression. Lumbar and lower thoracic spinal surgery can be safely performed under spinal anesthesia (SA). However, there are not many studies on the safety and efficacy of spinal anesthesia in patients who have undergone long segment stabilization surgery. Methods – Patients who underwent lumbar and lower thoracic spinal instrumentation operations with general anesthesia (GA) or spinal anesthesia were included in the study. Demographic characteristics and American Society of Anesthesiologists (ASA) physical status of the patients were all recorded. Visual analog scale and quality of life scores were obtained before and after the operation. Results – 572 patients with SA and 598 patients with GA were included in the study, 352 / 347 had only-lumbar region and 220 / 251 had thoracolumbar region operations, respectively. All patients underwent short/long segment stabilization. Mean operating time was 106.1 / 156.7 minutes. Average blood loss was 375 / 390 mL. All patients were mobilized 16-24 / 24-36 hours after surgery. In our patient group, there were both high-risk and normal-risk subgroups in terms of ASA physical status. During the clinical follow-up, a statistically significant improvement was found for VAS and quality of life scores for both groups (p<0.05). Conclusions – Spinal anesthesia appears to be a very effective method in lumbar and thoracolumbar surgery. Along with careful patient selection, using this highly effective method provides a comfortable space for the surgeon.
Axiological strategies for reforming foreign language professional education
Authors: Крупченко А.К. , Медведева Т.Ю. , Папуткова Г.А.
Published: December 2023

Рассматриваются перспективы становления и развития научно-образовательного суверенитета России на примере иноязычного профессионального образования в условиях выхода из Болонской системы. Выделены положения современной языковой образовательной политики, обозначившие изменения в ценностно-целевых установках иноязычной подготовки будущих специалистов. Определены в качестве аксиологических стратегий реформирования системы иноязычного профессионального образования такие научно-обоснованные направления как аксиология иноязычного образования и профессиональная лингводидактика.

The prospects for the formation and development of the scientific and educational sovereignty of Russia are considered on the example of foreign language vocational education in situation of exit from the Bologna system. The provisions of the modern language educational policy are highlighted, which marked changes in the value-target settings of the foreign language training of future specialists. Such scientifically based areas as the axiology of foreign language education and professional linguodidactics are defined as axiological strategies for reforming the system of foreign language professional education.
Modeling Real-life Data Sets with a Novel G Family of Continuous Probability Distributions: Statistical Properties, and Copulas
Authors: Abdullah Murtadha Mansour , Masmoudi Afif
Published: December 2023

This work presents a novel two-parameter G family of continuous probability distributions with compounded parameters. To determine and examine the pertinent mathematical properties, calculations are performed. In one of the special sections, the standard inverse-Rayleigh baseline model is mathematically and statistically emphasized. We generated a number of bivariate and multivariate distributions using the copula method. These new distributions will aid in the modelling of bivariate and multivariate data. The applicability and flexibility of the new compounded two-parameters-G family are demonstrated through three applications to real-life data sets. These examples demonstrate the applicability of the family.
Schwarz’s Bayesian Information Criteria: A Model Selection Between Bayesian-SEM and Partial Least Squares-SEM
Authors: Marliana Reny Rian , Suhayati Maya , Ningsih Sri Bekti Handayani
Published: December 2023

In this academic work a comparison between a Bayesian-Structural Equation Modelling (B-SEM) and a Partial Least Squares-Structural Equation Modelling (PLS-SEM) on a relationship amongst self-directed learning readiness (SDLR), E-learning readiness, and learning motivation of undergraduate students throughout the outbreak of Covid-19 is studied. The B-SEM is built using prior distribution i.e., inverse-Gamma, inverse-Wishart, and normal distribution on specific parameters of the model with 19000 iterations on Markov Chain Monte Carlo (MCMC) algorithm. Whereas the PLS-SEM is established using Ordinary Least Squares (OLS) method, PLS algorithm with 300 iterations, and 5000 subsamples on bootstrapping. The objective of this study is to get the most compatible model which represent the relationship between three latent variables in this study. Schwarz’s Bayesian Information Criteria (BIC) is used on model selection between these two models. Data were obtained from 214 undergraduate students with three majors of study at the Faculty of Information Technology, Sebelas April university in Indonesia. Both models produce the same output which depict that self-directed learning readiness significantly affects the learning motivation of the students, while there is not a significant effect of e-learning readiness on learning motivation. With the lower BIC value, which is a negative value, PLS-SEM is more fitted for portray the influence of self-directed learning readiness, and e-learning readiness to learning motivation of students than B-SEM model.
Knowledge, attitude and practice regarding enteric fever among doctors of Bangabandhu Sheikh Mujib Medical University
Authors: Alam Kaoser , Shams Mohammad Jahan , Tabassum Chowdhury Rehnuma , Uzzal Mahfuzur Rahman , Hasan M. Zakir , Chowdhury M. A. Jalil , Rahaman Mohammad Ferdous Ur
Published: December 2023

Background: Enteric fever remains a significant public health challenge, particularly in developing countries. Understanding the knowledge, attitudes, and practices of general practitioners regarding enteric fever is crucial for effective disease management and control. This study aims to assess these aspects among general practitioners, providing insights into current practices and identifying areas for improvement.
Methods: This cross-sectional survey was conducted with 200 general practitioners. The questionnaire covered various aspects, including baseline characteristics, knowledge about enteric fever causative agents and symptoms, diagnostic practices, antibiotic preferences, and general attitudes towards management and prevention. Data were analyzed to determine the prevalence of specific knowledge and practices.
Results: Majority of practitioners (74%) were residents, predominantly working indoors (61.5%). All respondents correctly identified Salmonella typhi and para typhi as causative agents and fever as a primary symptom. However, there was variability in recognizing other symptoms and diagnostic methods. Ceftriaxon was the most preferred antibiotic (67%), and blood culture correctly identified as gold standard for diagnosis by 71% of respondents. Attitudes and practices varied, with significant number not adhering to recommended diagnostic and treatment protocols.
Conclusions: The study reveals a solid foundation of basic knowledge about enteric fever among general practitioners but also highlights significant gaps in the understanding of clinical symptoms, diagnostic practices, and treatment protocols. These findings underscore the need for enhanced educational initiatives and standardized guidelines to improve the management of enteric fever.
RELIGIÕES POPULARES TRADICIONAIS E A BUSCA DE SENTIDO AO COTIDIANO DA VIDA
Authors: Lemos Carolina Teles , Azevedo Gilson Xavier de
Published: December 2023

As religiões populares tradicionais existem e se manifestam de diversas formas e em cada uma delas assumem significados muito específicos e numinosos. Tais manifestações, refletem a contínua relação que o ser humano estabelece com o mundo ao seu redor, com a vida e consigo mesmo. O homo religius é um ser em existência que dá sentido e significado ao seu cotidiano, e o faz também por meio desse religare, que é o sentimento intrínseco de pertencimento ao cotidiano da vida. O que se percebe sobre esse conjunto de costumes e vivências religiosas, sejam eles de origem católica, indígena, africana, protestante, espírita, oriental, holística, pagã ou outros, é a força que a crença humana manifesta nas entidades, nos deuses, nas rezas, nos rituais que criam círculos de proteção em relação às mais diversas situações, cuja força humana não consegue por si só compreender e propor soluções.
Current trends and future perspectives of natural polymer loaded nanoparticle based drug delivery system for the management of inflammatory bowel disease
Authors: Basak Ankita , Ghosh Soumyadip , Ganguly Debgopal , Garain Soukat , Ghosh Riya , Choudhury Ananta , Deka Himangshu , Sarmah Jahnabi
Published: December 2023

Targeting the drug delivery system is very tough nowadays due to premature drug release at the upper GIT tract and altered pH conditions. Colon-specific drug delivery systems can overcome that problem using different polymer combinations. A nanoparticulate drug delivery system is the prominent dosage form that impacts the bioavailability and requires a low dose to excrete the therapeutic efficacy. All nanoscience and nanotechnology are applications of Nanometrology, the science of measurements at the nanoscale. NPDDSs were primarily developed to combine the colloidal stability of solid particle suspensions in biological fluids and the solubilizing properties of liquids. An ideal drug-delivery system possesses two elements: the ability to target and control the drug release. Colloidal drug carriers offer a number of potential advantages as delivery systems, such as better bioavailability for poorly soluble drugs. Researchers have created various sophisticated and multifunctional nanocarrier systems that can transport pharmaceuticals in a targeted, sustained, and regulated manner to provide therapeutic medications that are safer and more effective, particularly to ulcerative colitis. These innovative technologies are improving the pharmacokinetic profile of pharmaceuticals, increasing their systemic circulation, and decreasing the frequency of pharmacological side effects. The review focuses on the current trend and future perspectives of natural polymer-based-loaded nanoparticle-based drug delivery systems for the management of inflammatory bowel disease.
Research Insights: Can Financial Inclusion Reduce Extreme Weather Impacts on Rural Children?
Authors: Bernal Carolina , Vlaicu Razvan
Published: December 2023

Extreme rainfall induces rural households to choose immediate benefits over long-run investments in education by increasing the incidence of child labor and household chores at the expense of school attendance. Over-indebtedness through pre-existing formal loans reinforces the likelihood that a child works due to rainfall shocks. Asset insurance, foreign remittances, and natural disaster aid reduce or eliminate the shock-induced shift toward domestic activities and away from schooling.