• Energy Research

En la industria de la salud remota, el análisis de datos denota la computarización de la recopilación, el procesamiento y la exploración de datos complicados para adquirir percepciones más finas y validar a los profesionales de la salud para que tomen decisiones familiares. Los conceptos básicos de la atención médica en la era moderna son desafíos vitales específicamente en los países en desarrollo debido a la escasez de hospitales y profesionales médicos difíciles. Como los sistemas difusos han reformado varias áreas de trabajo, la salud también lo ha aprovechado al máximo. En este documento, el propósito del estudio es introducir un control remoto novedoso e inteligente sistema de atención médica basado en tecnologías modernas como Internet de las cosas (IoT) y sistemas difusos neutrosóficos para garantizar un análisis de datos preciso con menos tiempo y consumo de energía. En este estudio, se diseña un método novedoso llamado análisis de datos difusos neutrosóficos Shapiro Wilk (BO-SWNF) basado en Blinder Oaxaca para atención médica remota. La recopilación de datos se realiza con el conjunto de datos WESAD. Los datos duplicados son eliminados por el modelo de preprocesamiento basado en regresión lineal Blinder Oaxaca. Con la aplicación de la función Blinder Oaxaca, se mejora la eficiencia energética. Finalmente, el algoritmo difuso neutrosófico Shapiro Wilk se aplica para garantizar un análisis de datos sólido. El experimental los resultados del BO-SWNF propuesto contemplan los datos para una mejor comprensión de la distribución de atributos. El resultado se lleva a cabo mediante el uso de la aplicación PYHTON para analizar la detección de estrés con el conjunto de datos WESAD. El método BO-SWNF propuesto logró un análisis de datos preciso general del 12% con un tiempo mínimo que garantiza una mejora del 56% y minimiza el consumo de energía en un 54%. Dans l'industrie de la santé à distance, l'analyse des données dénote l'informatisation de la collecte, du traitement et de l'exploration de données compliquées pour acquérir des perceptions plus fines et valider les professionnels de la santé pour prendre des décisions familières. Les bases des soins de santé à l'ère moderne sont des défis vitaux, en particulier dans les pays en développement, en raison du manque d'hôpitaux et de professionnels de la santé difficiles. Comme les systèmes flous ont réformé plusieurs domaines de travail, la santé en a également tiré le meilleur parti. Dans cet article, le but de l'étude est d'introduire une nouvelle télécommande intelligente système de soins de santé basé sur des technologies modernes comme l'Internet des objets (IoT) et les systèmes flous neutrosophiques pour assurer une analyse précise des données avec moins de temps et de consommation d'énergie.Dans cette étude, une nouvelle méthode appelée, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed.Data collection is performed with the WESAD dataaset.Duplicated data are eliminated by Blinder Oaxaca Linear Regressionbased Preprocessing model.Dans l'application de la fonction Blinder Oaxaca, l'efficacité énergétique est améliorée.Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis.The experimental les résultats du BO-SWNF proposé envisagent les données pour une meilleure compréhension de la distribution des attributs. Le résultat est réalisé en utilisant l'application PYHTON pour analyser la détection du stress avec l'ensemble de données WESAD. La méthode BO-SWNF proposée a permis d'obtenir une analyse de données globale précise de 12 % avec un temps minimum garantissant une amélioration de 56 % et minimisant la consommation d'énergie de 54 %. In the remote healthcare industry data analytics denotes the computerization of collection, processing, and exploring complicated data to acquire finer perceptions and validate healthcare practitioners to produce familiar decisions.Healthcare basics in the modern age are vital challenges specifically in developing countries owing to the shortfall of difficult hospitals and medical professionals.As fuzzy systems have reformed several areas of work, health has also made the most of it.In this paper, the purpose of the study is to introduce a novel and intelligent remote healthcare system based on modern technologies like the Internet of things (IoT) and Neutrosophic fuzzy systems to ensure precise data analysis with lesser time and energy consumption.In this study, a novel method called, Blinder Oaxaca-based Shapiro Wilk Neutrosophic Fuzzy (BO-SWNF) data analytics for remote healthcare is designed.Data collection is performed with the WESAD dataset.Duplicated data are eliminated by Blinder Oaxaca Linear Regressionbased Preprocessing model.With the application of the Blinder Oaxaca function, energy efficiency is enhanced.Finally, the Shapiro Wilk Neutrosophic Fuzzy algorithm is applied for ensuring robust data analysis.The experimental results of the proposed BO-SWNF envisage the data for finer comprehension of attribute distribution.The result is conducted by using PYHTON application to analyze stress detection with the WESAD dataset.The proposed BO-SWNF method achieved an overall accurate data analysis of 12% with minimum time ensuring 56%improvement and minimizing energy consumption by 54%. في مجال الرعاية الصحية عن بعد، تشير تحليلات البيانات إلى حوسبة جمع ومعالجة واستكشاف البيانات المعقدة لاكتساب تصورات أدق والتحقق من صحة ممارسي الرعاية الصحية لاتخاذ قرارات مألوفة. أساسيات الرعاية الصحية في العصر الحديث هي تحديات حيوية على وجه التحديد في البلدان النامية بسبب النقص في المستشفيات الصعبة والمهنيين الطبيين. نظرًا لأن الأنظمة الغامضة قد أصلحت العديد من مجالات العمل، فقد حققت الصحة أيضًا أقصى استفادة منها. في هذه الورقة، الغرض من الدراسة هو تقديم جهاز تحكم عن بعد جديد وذكي نظام الرعاية الصحية القائم على التقنيات الحديثة مثل إنترنت الأشياء (IoT) وأنظمة Neutrosophic الضبابية لضمان تحليل دقيق للبيانات مع وقت أقل واستهلاك أقل للطاقة. في هذه الدراسة، تم تصميم طريقة جديدة تسمى تحليلات بيانات Shapiro Wilk Neutrosophic Fuzzy (BO - SWNF) القائمة على Blinder Oaxaca للرعاية الصحية عن بعد. يتم جمع البيانات باستخدام مجموعة بيانات WESAD. يتم التخلص من البيانات المكررة بواسطة نموذج المعالجة المسبقة القائم على Blinder Oaxaca Linear Regression. مع تطبيق وظيفة Blinder Oaxaca، يتم تحسين كفاءة الطاقة. أخيرًا، يتم تطبيق خوارزمية Shapiro Wilk Neutrosophic Fuzzy لضمان تحليل بيانات قوي. تتصور نتائج BO - SWNF المقترحة البيانات من أجل فهم أدق لتوزيع السمات. يتم إجراء النتيجة باستخدام تطبيق PYHTON لتحليل الكشف عن الإجهاد باستخدام مجموعة بيانات WESAD. حققت طريقة BO - SWNF المقترحة تحليلًا دقيقًا شاملاً للبيانات بنسبة 12 ٪ مع الحد الأدنى من الوقت لضمان تحسين 56 ٪ وتقليل استهلاك الطاقة بنسبة 54 ٪.

A tumor is an abnormal development of cells in the human body. A tumor develops when cells divide without any control. Tumors change their size from a small to large lump. Tumors appear anywhere in the body. The early stage of diagnosis is an essential one in disease treatment. Many researchers carried out their research on different tumor detection methods. However, the tumor detection accuracy level was not improved and tumor detection time consumption not minimized. In order to address these problems, an Iterative Reflect Perceptual Sammon Bagging Classification (IRPS-BAC) Method is introduced. The aim is to accurately detect brain tumors as early as possible and make the method suitable for real-time applications. The IRPS-BAC Method comprises two processes, namely, feature selection and classification using the iterative reflect perceptual sammon feature selection process and bagging classification process. In the IRPS-BAC Method, an input of medical data are gathered from the Epileptic Seizure Recognition Data Set and Cervical Cancer Risk Classification database. After that, iterative reflect perceptual sammon feature selection process is carried out to select the relevant features. Iterative reflect perceptual divergence computes the variation between two features. After that, sammon mapping projects the similar and dissimilar features into feature space. By this manner, the relevant features get selected using the IRPS-BAC Method. With the help of selected relevant features, bagging classification process is carried out. In bagging classification process, internal node processes the selected features and leaf node to make the tumor decision as normal or cancerous one based on information gain. This, in turn, helps to reduce the time complexity and error rate. The performance of the proposed IRPS-BAC Method is determined by two benchmark datasets through comparing the parameter such as tumor detection time, tumor detection accuracy and error rate with the existing approaches. In the Epileptic Seizure Recognition Data Set, the proposed IRPS-BAC Method improves tumor detection accuracy by 16%, with minimum time period and the error rate of 41 ms and 58% for tumor detection as compared to existing methods. By using Cervical Cancer Risk Classification, the proposed IRPS-BAC Method exhibited higher classification performance measures, including accuracy (14%), time (46 ms), and error rate (61%), than the current conventional approaches.

Heart disease (HD) has surpassed all other causes of death in recent years. Estimating one’s risk of developing heart disease is difficult, since it takes both specialized knowledge and practical experience. The collection of sensor information for the diagnosis and prognosis of cardiac disease is a recent application of Internet of Things (IoT) technology in healthcare organizations. Despite the efforts of many scientists, the diagnostic results for HD remain unreliable. To solve this problem, we offer an IoT platform that uses a Modified Self-Adaptive Bayesian algorithm (MSABA) to provide more precise assessments of HD. When the patient wears the smartwatch and pulse sensor device, it records vital signs, including electrocardiogram (ECG) and blood pressure, and sends the data to a computer. The MSABA is used to determine whether the sensor data that has been obtained is normal or abnormal. To retrieve the features, the kernel discriminant analysis (KDA) is used. By contrasting the suggested MSABA with existing models, we can summarize the system’s efficacy. Findings like accuracy, precision, recall, and F1 measures show that the suggested MSABA-based prediction system outperforms competing approaches. The suggested method demonstrates that the MSABA achieves the highest rate of accuracy compared to the existing classifiers for the largest possible amount of data.