Abstract Machine learning approaches, such as artificial neural networks (ANN), effectively perform various tasks and provide new predictive models for complicated physiological systems. Examples of Robotics applications involving direct human engagement, such as controlling prosthetic arms, athletic training, and investigating muscle physiology. It is now time for automated systems to take over modelling and monitoring tasks. However, there is a problem with the massive amount of time series data collected to build accurate forecasting systems. There may be inconsistencies in forecasting muscle forces due to the enormous amount of data. As a result, anomaly detection techniques play a significant role in detecting anomalous data. Detecting anomalies can help reduce redundancy and free up large storage space for storing relevant time‐series data. This paper employs several anomaly detection techniques, including Isolation Forest (iforest), K‐Nearest Neighbour (KNN), Open Support Vector Machine (OSVM), Histogram, and Local Outlier Factor (LOF). These techniques have been used by Long Short‐Term Memory (LSTM), Auto‐Regressive Integrated Moving Average (ARIMA), and Prophet models. The dataset used in this study contained raw measurements of body movements (kinematics) and the forces generated during walking (kinetics) of 57 healthy people (29 Female, 28 Male) without walking abnormalities or recent leg injuries. To increase the data samples, we used TimeGAN that generates synthetic time series data with temporal dependencies, aiding in training robust predictive models for muscle force prediction. The results are then compared with different evaluation metrics for five different samples. It is found that anomaly detection techniques with LSTM, ARIMA, and Prophet models provided better performance in forecasting muscle forces. The iforest method achieved the best Pearson's Correlation Coefficient ( r ) of 0.95, which is a competitive score with existing systems that perform between 0.7 and 0.9. The methodology provides a foundation for precision medicine, enhancing prognostic capability over relying solely on population averages.

Summary The sensor nodes, which are available in the wireless sensor networks (WSN), are equipped with sensing abilities, and communication. Several domains require the sensor nodes to be arranged in aggressive surroundings, in which observing malicious activities within the sensor network. Therefore, the present research proposes the border‐hunting optimization‐based deep CNN (BHO‐DCNN) for the mobile agent (MA)‐based intrusion detection in WSN. The importance of the research relies on the BHO‐DCNN model for identifying the intrusion available in the network is established by integrating the optimization with its features through a deep classifier for detection in a precise manner. The algorithm follows the communal hierarchy, surrounding, group hunting, and prey attacking, which provides an enhanced rate of convergence in the method of detection. The analysis is achieved through the database IDS 2018 Intrusion CSVs depending on the performance like delay, alive nodes, normalized energy, as well as throughput. The obtained number of alive nodes through the developed BHO‐DCNN algorithm is 45, end‐to‐end delay is 0.2572 ms, normalized energy is 0.1622 J, as well as throughput, is 0.3125% for nodes 50 at the populate rate of 100, respectively.

Abstract The in-silico toxicity prediction techniques are useful to reduce rodents testing (in-vivo). Authors have proposed a computational method (in silico) for the toxicity prediction of small drug molecules using their various physicochemical properties (molecular descriptors), which can bind to the antioxidant response elements (AREs). The software PaDEL-Descriptor is used for extracting the different features of drug molecules. The ARE data set has total 7439 drug molecules, of which 1147 are active and 6292 are inactive, and each drug molecule contains 1444 features. We have proposed a novel ensemble-based model that can efficiently classify active (binding) and inactive (non-binding) compounds of the data set. Initially, we performed feature selection using random forest importance algorithm in R, and subsequently, we have resolved the class imbalance issue by ensemble learning method itself, where we divided the data set into five data frames, which have an almost equal number of active and inactive drug molecules. An ensemble model based upon the votes of four base classifiers is proposed, which gives an accuracy of 97.14%. The K-fold cross-validation is conducted to measure the consistency of the proposed ensemble model. Finally, the proposed ensemble model is validated on some new drug molecules and compared with some existing models.

Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrovirus known as Human T-cell Lymphotropic virus (HTLV). Out of the four different types of HTLVs, HTLV-1 is most prominent in scourging over 20 million people around the world and still not much effort has been made in understanding the epidemiology and controlling the prevalence of this virus. This condition further worsens when most of the infected cases remain asymptomatic throughout their lifetime due to the limited diagnostic methods; that are most of the times unavailable for timely detection of infected individuals. Moreover, at present, there is no licensed vaccination for HTLV-1 infection. Therefore, there is a need to develop the faster and efficient diagnostic method for the detection of HTLV-1. Influenced from the outcomes of the machine learning techniques in the field of bio-informatics, this is the first study in which 64 hybrid machine learning techniques have been proposed for the prediction of different type of HTLVs (HTLV-1, HTLV-2, and HTLV-3). The hybrid techniques are built by permutation and combination of four classification methods, four feature weighting, and four feature selection techniques. The proposed hybrid models when evaluated on the basis of various model evaluation parameters are found to be capable of efficiently predicting the type of HTLVs. The best hybrid model has been identified by having accuracy, an AUROC value, and F1 score of 99.85 percent, 0.99, and 0.99, respectively. This kind of the system can assist the current diagnostic system for the detection of HTLV-1 as after the molecular diagnostics of HTLV by various screening tests like enzyme-linked immunoassay or particle agglutination assays there is always a need of confirmatory tests like western blotting, immuno-fluorescence assay, or radio-immuno-precipitation assay for distinguishing HTLV-1 from HTLV-2. These confirmatory tests are indeed very complex analytical techniques involving various steps. The proposed hybrid techniques can be used to support and verify the results of confirmatory test from the protein mixture. Furthermore, better insights about the virus can be obtained by exploring the physicochemical properties of the protein sequences of HTLVs.

Demand side load prediction is one of the most challenging tasks in smart grid environment due to uncertainties between demand and supply. Hence, in order to overcome this issue, this paper presents a scheme based on machine learning and deep learning for energy load forecasting by considering the weather condition of the area. We propose a two-tier Ensemble model, which ensembles the results of machine learning model (Support Vector Machine) and deep learning models (Convolu- tional Neural Network One Dimensional and Long Term Short memory) with a simple neural network to predict the load and demand gap. Then, we train and test the model with the UMass Smart* Dataset - 2017 release by taking the readings of appliances and weather conditions. The experimental results demonstrate that the proposed scheme has a significant improvement over the existing load forecasting methods having short-term and long- term load prediction models with an overall accuracy of 95.6%.

In Small Scale Wireless Sensor Networks (SSWSNs), reliability is defined as the capability of a network to perform its intended task under certain conditions for a stated time span. There are many tools for modeling and analyzing the reliability of a network. As the intricacy of various networks is increasing, there is a need for many sophisticated methods for reliability analysis. The term reliability is used as an umbrella term to capture various attributes such as safety, availability, security, and ease of use. The existing methods have many shortcomings which include inadequacy of a novel framework and inefficacy to handle scalable networks. This paper presents a novel framework which predicts the overall reliability of the SSWSNs in terms of performance metrics such as, sent packets, received packets, packets forfeit, packet delivery ratio and throughput. This framework includes various phases starting with scenario generation, construction of a dataset, applying ensemble based machine learning techniques to predict the parameters which cannot be calculated. The ensemble model predicts with an optimum accuracy of 99.9% for data flow, 99.9% for the protocol used and 97.6% for the number of nodes. Finally, to check the robustness of the ensemble model 10-fold cross-validation is used. The dataset used in this work is available as a supplement at http://bit.ly/SSWSN-Reliability .