Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements.

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.

Machine learning-based approach to predict ice meltdown in glaciers due to climate change and solutions.

The fate of humankind and all other life forms on earth is threatened by a foe, known as climate change. All parts of the world are affected directly or indirectly by this phenomenon. The rivers are drying up in some places and in other places, it is flooding. The global temperature is rising every year and the heat waves are taking many souls. The cloud of "extinction" is upon the majority of flora and fauna; even humans are prone to various fatal and life-shortening diseases from pollution. This is all caused by us. The so-called "development" by deforestation, releasing toxic chemicals into air and water, burning of fossil fuels in the name of industrialisation, and many others have made an irreversible cut in the heart of the environment. However, it is not too late; all of this could be healed back with the help of technology and our efforts together. As per the international climate reports, the average global temperature has increased by a little more than 1 °C since 1880s. The research is primarily focused on the use of machine learning and its algorithm to train a model that predicts the ice meltdown of a glacier, given the features using the Multivariate Linear Regression. The research strongly encourages the use of features by manipulating them to determine the feature with a major impact on the cause. The burning of coal and fossil fuels is the main source of pollution as per the study. The research focuses on the challenges to gather data that would be faced by the researchers and the requirement of the system for the development of the model. The study is aimed to spread awareness in society about the destruction we have caused and urges everyone to come forward and save the planet.

Dark Web Data Classification Using Neural Network.

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)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction.

Internet of Medical Things (IoMT) and Reflective Belief Design-Based Big Data Analytics with Convolution Neural Network-Metaheuristic Optimization Procedure (CNN-MOP).

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.

Electromechanical dysfunction of the left atrium associated with interatrial block.

OBJECTIVES: Our purpose was to determine the effect of interatrial block (IAB, P-wave duration >/=120 ms) on left atrial (LA) dynamics. IAB is associated with LA enlargement (LAE). LA dysfunction is associated with decreased left ventricular filling, a propensity for LA appendage thrombus formation, and reduced atrial natriuretic peptide levels. We evaluated LA function in patients with and without IAB matched for LA size. METHODS: Echocardiograms with LA enlargement were analyzed. Twenty-four patients had IAB, and 16 patients without IAB formed the control group. LA volumes, A-wave acceleration times (At), LA stroke volume (LASV), ejection fraction (LAEF), and kinetic energy (LAKE) were calculated. RESULTS: The control group and patients with IAB had comparable maximal LA volume and diameter (P >.05). Patients with IAB had significantly longer At (115 +/- 39 ms vs 83 +/- 24 ms, P <.01) and smaller LASV (7 +/- 5 mL vs 17 +/- 6 mL, P <.01), LAEF (9% +/- 6% vs 25% +/- 8%, P <.01), and LAKE (20 +/- 14 vs 65 +/- 44 Kdyne/cm/s, P <.01). LAKE varied inversely with P-wave duration (r = -0.51, P <.01). P-wave duration and minimal LA volume were independent determinants of LAEF. CONCLUSIONS: Patients with IAB have a sluggish, poorly contractile LA, and the extent of dysfunction is related to the degree of electrical delay from IAB. IAB should be considered a marker of an electromechanically dysfunctional LA and hence a risk factor for atrial fibrillation and congestive heart failure.

Multiple coronary artery-left ventricular fistulas associated with hereditary hemorrhagic telangiectasia.

Coronary artery-left ventricular (LV) fistulas are extremely rare and can cause myocardial ischemia from coronary steal. We describe an elderly woman who presented with unstable angina from multiple and extensive coronary artery-LV fistulas. She also had clinical features suggestive of hereditary hemorrhagic telangiectasia (HHT). Association of coronary artery-LV fistulas with HHT has not been reported and can pose a management dilemma in view of the risks of extensive cardiopulmonary surgery and potential complications of myocardial ischemia, stroke, and brain abscess.

Intestinal strongyloidiasis manifesting as eosinophilic pleural effusion.

Strongyloidiasis caused by the helminth Strongyloides stercoralis is usually asymptomatic or causes mild dermatologic or gastrointestinal symptoms. In immunocompromised hosts, hyperinfection and dissemination can occur. In this case, I describe a nonimmunocompromised Southeast Asian man who had an eosinophilic pleural effusion, peripheral eosinophilia, and rhabditiform larvae of S stercoralis in the stools. There was complete resolution of the pleural effusion after thiabendazole therapy, thus suggesting strongyloidiasis as the cause of the effusion. Helminthic infections like strongyloidiasis should be considered in the differential diagnosis of an eosinophilic pleural effusion, especially in individuals from endemic areas.

Diabetic ketoacidosis and rhabdomyolysis following excessive intake of a weight reducing diet.

With the awareness of health problems related to obesity, weight reducing diets have become very popular. However, if these meal supplements are not taken as recommended, they can cause considerable harm in high risk individuals. We report a case of a young obese man who developed diabetic ketoacidosis (DKA) followed by rhabdomyolysis and acute renal failure (ARF) after excessive intake of a high carbohydrate containing weight-reducing meal. DKA associated with excessive intake of weight reducing diets has not been reported previously. In people with obesity and insulin resistance improper use of these supplements can cause severe metabolic complications.