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The world is advancing to a new era where a new concept is emerging that deals with "wirelessness". As we know, renewable energy is the future, and this research studied the integration of both fields that results in a futuristic, powerful, and advanced model of wireless body area networks. Every new emerging technology does have some cons; in this case the issue would be the usage of excess energy by the sensors of the model. Our research is focused on solving this excessive usage of energy to promote the optimization of energy. This research work is aimed to design a power-saving protocol (PSP) for wireless body area networks (WBANs) in electronic health monitoring (EHM). Our proposed power-saving protocol (PSP) supports the early detection of suspicious signs or sporadic elder movements. The protocol focuses on solving the excessive energy consumption by the body attached to IoT devices to maximize the power efficiency (EE) of WBAN. In a WSNs network, the number of sensor nodes (SNs) interact with an aggregator and are equipped with energy harvesting capabilities. The energy optimization for the wireless sensor networks is a vital step and the methodology is completely based on renewable energy resources. Our proposed power-saving protocol is based on AI and DNN architectures with a hidden Markov model to obtain the top and bottom limits of the SN sources and a less computationally challenging suboptimal elucidation. The research also addressed many critical technical problems, such as sensor node hardware configuration and energy conservation. The study performed the simulation using the OMNET++ environment and represent through results the source rate to power critical SNs improves WBAN's scheme performance in terms of power efficiency of Sporadic Elder Movements (SEM) during various daily operations., Competing Interests: The authors declare that they have no conflict of interest.

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S 3 VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S 3 VM can improve the prediction., Competing Interests: The authors declare that there are no conflicts of interest regarding the publication of this article., (Copyright © 2022 Anand Singh Rajawat et al.)

In recent times, the Internet of Medical Things (IoMT) is a new loomed technology, which has been deliberated as a promising technology designed for various and broadly connected networks. In an intelligent healthcare system, the framework of IoMT observes the health circumstances of the patients dynamically and responds to backings their needs, which helps detect the symptoms of critical rare body conditions based on the data collected. Metaheuristic algorithms have proven effective, robust, and efficient in deciphering real-world optimization, clustering, forecasting, classification, and other engineering problems. The emergence of extraordinary, very large-scale data being generated from various sources such as the web, sensors, and social media has led the world to the era of big data. Big data poses a new contest to metaheuristic algorithms. So, this research work presents the metaheuristic optimization algorithm for big data analysis in the IoMT using gravitational search optimization algorithm (GSOA) and reflective belief network with convolutional neural networks (DBN-CNNs). Here the data optimization has been carried out using GSOA for the collected input data. The input data were collected for the diabetes prediction with cardiac risk prediction based on the damage in blood vessels and cardiac nerves. Collected data have been classified to predict abnormal and normal diabetes range, and based on this range, the risk for a cardiac attack has been predicted using SVM. The performance analysis is made to reveal that GSOA-DBN_CNN performs well in predicting diseases. The simulation results illustrate that the GSOA-DBN_CNN model used for prediction improves accuracy, precision, recall, F1-score, and PSNR., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 A. Sampathkumar et al.)

Context: Teaching methods in pathology for undergraduate medical students are not effective., Aims: To document measures that can be adopted by individual teachers that can excite the interest, participation of the students and help them learn pathology in a clinical reasoning context., Settings and Design: Medical students in a large international medical school with class sizes of 700-900 were taught the pathology course in a period of sixteen weeks for two cohorts of students each year over a period of twenty years., Subjects and Methods: Specific learning objectives were devised to achieve higher levels of cognitive domain including interpretation, analysis and problem solving of clinical data of patients related to the objectives. The teaching sessions were modified to provide for maximum active participation by students with effective feedback at multiple points. Additional learning tools like concept maps, clickers, modified essay questions, flipped classrooms, clinicopathological conferences, directed self-learning activities were included. Learning objectives and assessment tools for professional behavior and communication skills were included., Results: The students actively participated in all the learning activities with enthusiasm and achieved the objectives as reflected in the performance in the in-house examinations and the USMLE step one examination which tests clinical vignette-based problem-solving principles of which around 70% are related to pathology., Conclusions: The teaching sessions in pathology were useful and effective with adaptation to interactive, clinical reasoning platforms for teaching and assessment.

Objective: To determine the incidence of accidentally diagnosed genital malignancies in women undergoing gynecological surgeries for pathologies presumed to be benign., Methods: Data of all women who underwent hysterectomy for benign indications were retrieved from hospital records. A total of 628 cases of hysterectomy and 35 cases of myomectomy were included. The final histopathology was accessed through the computer records, and the total number of genital tract malignancies was included., Results: The incidence of occult uterine, ovarian, and cervical malignancies in women undergoing hysterectomy was 0.47%, 0.31%, and 0.15%, respectively. The incidence of uterine corpus malignancies in women undergoing myomectomy was 2.8%, and 3 cases of smooth muscle tumor of uncertain malignant potential (STUMP) were also identified. Endometrial stromal sarcomas were the most common pathology among uterine malignancies, followed by leiomyosarcoma and endometrial adenocarcinoma., Conclusion: Postoperative diagnosis of malignancies in women undergoing surgery for benign indications poses great challenges to the treating surgeon. Occult malignancies must be ruled out by thorough preoperative assessment, especially in some subsets of women. Morcellation is an inevitable technique and complement to laparoscopic surgeries, although little chances of malignancy spread is related to its use. Nevertheless, thorough preoperative investigations and always ensuring contained morcellation can minimize these chances.