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Autism spectrum disorder (ASD) is a developmental disability that can cause significant social, communication and behavioral challenges. It is a lifelong neurodevelopmental condition with its onset before the age of three years. It is characterized by abnormalities in communication, impaired social function, repetitive behaviors and restricted interests. Western medicine and research have seemingly stalled in respect to the management of autism however early intervention and behavioral therapies have shown improvement to some extent. The present study describes the case of childhood autism visited at Government Ayurvedic Medical College and Hospital, Akhnoor, Jammu. Child was diagnosed clinically and has been treated with Ayurvedic interventions, Panchakarma therapies and diet modification. Child has got relief symptomatically within one week of therapy started. Case study briefly explained Ayurvedic concepts regarding childhood autism and Ayurvedic treatment protocols in autistic disorder. Keywords: Autism Spectrum Disorder, hyperactivity, Ayurveda, Management, Prevention, Pinda Sweda, Abhyanga, Pichu, Nasya, Uttar Basti.

Malaria is a major public health issue that affects people all around the world. The diagnosis of red blood cells contaminated with insects under a microscope by a skilled specialist is a typical way of diagnosing malaria. This method does not function effectively, and the diagnosis is made based on the test taker's experience and expertise. Malaria blood tests have been using automated imaging technologies based on machine learning for early detection. However, thus far, practical performance is insufficient. The convolutional neural network CNN is used in this paper to present an innovative and resilient machine learning model. for automatically distinguishing single cells in tiny blood smears from normal microscope slides, such as infected or non-infected with this virus. Our new CNN model's average accuracy for 16 layers is 97.37 percent based on ten-fold confirmation using 27,578 single-image pictures. In the same photos, the learning transfer model only gets 91.99 percent. All performance indicators, such as sensitivity (96.99 percent vs 89.00 percent), clarity (97.75 percent vs 94.98 percent), accuracy (97.73 percent vs 95.12 percent), F1 school (97.36 percent vs 90.24) percent, and Matthew's correlation coefficient, demonstrate that the CNN model outperforms the transfer learning model (94.75 percent vs 85.25 percent). Index terms: Deep Learning; Convolutional Neural Networks; Malaria; Computer-Aided Diagnostics; Machine Learning

Sometimes, outcomes of random processes don’t seem to follow the theoretical probabilities due to the presence of bias and even when the probabilities are followed in a large number of trials, dynamic bias is still evident in many of these processes. This paper provides a short study on the bias using examples and defines what kind of processes could be biased. It also demonstrates the two types of bias which are dynamic and fixed. This study could be used to analyze the bias in various random processes and get a better understanding of the outcomes. Dynamic bias has further been explained with the help of 52 cards. The study helps in providing a better understanding of randomness and further helps in designing experiments.

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

Telescopic Handlers, also known as Telehandlers or teleporters or boom lifts are small human-operated machines extensively used by the agricultural, construction and landscaping industries. Different types of attachments are being used with these telehandlers which make them extremely versatile, safe, swift and costumer friendly. They prove out to be very efficient when used with various attachments designed as per the market requirements. Common attachments include forks, lifting jibs, man-basket or work platforms, buckets etc which are attached to the telescopic boom of the telehandlers as per use. However, these attachments should be chosen with utmost care keeping in mind that it does not pose any threat on the operation safety. While, these telehandlers have numerous advantages and applications, they also have certain limitations which slightly restricts their use in some cases. The present work is a review on telescopic handlers, their applications, limitations and future scope of research on their improvement to expand the domain of the usage.

This paper analyzes performance of Artificial Intelligence based optimization controller for the comparative study of maximum power point tracking (MPPT) in PV Systems. Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) control methods are the two such techniques used, and are simulated in MATLAB-Simulink using Trina Solar TSM-250PD05.08. The simulation results suitably depict the performance of these methods on the basis of some parameters like their rise time, settling time, time taken to reach maximum power point and their efficiency. It is found that maximum power point is tracked in PV systems with greater efficiency using PSO as compared to ANN.

The proposed work implements a hybrid model design in matlab software 2015a tool. The problem of stability in power distribution is resolved by using PI controller. The design is based on Solar and thermal power plant combination and concept of negative feedback control system. For the appraisal the item greenius was used, a program being reliably made at DLR since 1999. This program didn't have a limit work for PV diversions. Appropriately one of the purposes of this hypothesis was at first to dismember the precision of the PV show and thus make and execute a battery model to survey the budgetary sensibility of autonomous PV structures. Consequently, the greenius model was differentiated and PV-Watts and PVSyst photovoltaic models. Likewise, an electric amassing segment subject to a charge balance model was executed in the structure. This model showed to definitely depict the introduction of batteries without the prerequisite for certain parameters. The second purpose of this proposition was to update and evaluate the cash related credibility of crossbreed impact plants with a high breaking point factor (>90%).

Proper management of municipal solid waste is one of the prime matters of concern for metropolitan cities. To be able to successfully manage the solid waste generated, we need to plan in advance. A very essential pre-requirement for an efficient solid waste management is an accurate prediction of the garbage generation. Accurate forecasting of the quantity of MSW generation will enable us to design and operate an effective waste collection system. The main objective of this paper is to compare different models of artificial intelligence, viz. artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), discrete wavelet theory–artificial neural network (DWT-ANN), discrete wavelet theory–adaptive neuro-fuzzy inference system (DWT-ANFIS), genetic algorithm–artificial neural network (GA-ANN) and genetic algorithm–adaptive neuro-fuzzy inference system (GA-ANFIS) to examine and evaluate their capability in forecasting the amount of garbage being generated. A case example of the city of New Delhi, India, has been used for better understanding of different models. Root mean square error (RMSE), coefficient of determination (R2) and index of agreement (IA) values for every model were calculated, and the models were compared on the basis of it. The hybrid model of genetic algorithm and artificial neural network was found to have the lowest RMSE, the highest IA value and the highest R2 values, and hence is the most accurate of the above six models.

Every 6 minutes a child suffers a staircase-related injury as per a new study mentioned in an article by WebMD,. In this paper, we aim to show how various human activities can be identified and monitored with the help of a Signal Processing and Machine Learning techniques. The proposed algorithm demonstrates how different movements can be expressed in terms of signals and subsequently differentiated and identified. Several goal-specific algorithms are utilized, based on feature extraction and classification to aid in identification of the actions.