Epilepsy is a neurological disorder characterized by the existence of recurring seizures. Like many other neurological disorders, epilepsy can be appraisal by the electroencephalogram (EEG). The EEG signal is highly non-linear and non-stationary and consist of lot of data including significant data and artifact and hence, it is practically arduous to characterize and interpret it. However, it is a well-established clinical technique with low associated costs for detection of various neurological disorders. In this work, we propose a methodology for the automatic detection of normal, epilepsy and brain death from recorded EEG signals collected from clinic. Discrete wavelet transform is applied for feature extraction. Back Propagation neural network optimized by particle swarm optimization is used for classification of neurological disorders. Simple BPNN has several drawbacks which mainly include large time duration during EEG signal classification. This drawback is removed by PSO. In this paper, the proposed method used to detect the number of neurons in hidden layer of BPNN using optimization technique of PSO. Once the numbers of neurons in hidden layers are detected, optimum value for initial weights and biases for BPNN estimated which is further used for classification and sortilege of various neurological disorders. So that time duration decreases and accuracy increases. EEG signals are recorded for 30 minutes of three different patients that are epileptic, normal and brain death used to rehearse and test the proposed algorithm. A signal used to test is integrated signal by taking mean of 16 channels. By applying techniques to signals of epilepsy or normal or brain death patient which are known to us we find the more accurate results with less number of iteration and time .
Monitoring systems based on artificial intelligence (AI) and wireless sensors are in high demand and give exact data extraction and analysis. The main objective of this paper is to detect the most appropriate plant development parameters. This paper has the concept of reducing the hazards in agriculture and promoting intelligent farming. Advancement in agriculture is not new, but the AI-based wireless sensor will push intelligent agriculture to a new standard. The research goal of this work is to improve the prediction state using image processing-based machine learning techniques. The main objective of the paper, as described above, is to detect and control cotton leaf diseases. This paper comprises several aspects, including leaf disease detection, remote monitoring system depending on the server, moisture and temperature sensing, and soil sensing. Insects and pathogens are typically responsible for plant diseases that reduce productivity if not timely. This paper presents a method to monitor the soil quality and prevent cotton leaf diseases. The proposed system suggested uses a regression technique of artificial intelligence to identify and classify leaf diseases. The information would be delivered to farmers through the Android app after infection identification. The Android app also allows soil parameter values like moisture, humidity, and temperature to be displayed along with the chemical level in a container. The relay may be on/off to regulate the motor and chemical sprinkler system as required by using the Android app. In the proposed system, the SVM algorithm delivers the best accuracy in detecting various diseases and demonstrates its efficiency in the detection and control by the improvement of cultivation for the farmers.
This article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures.
The process of incorporating robotic technology and autonomous vehicles are increasing in all applications where for all real-time application developments time and energy can be saved for every single movement transfer as compared to human classifications. Thus, considering the advantage of autonomous process without any presence of an individual, the supply chain management can be designed using robotic technology. The robotic technology provides an informal route where all goods can be transported to different places within a short span of time, and any false identification in transfer of goods can also be easily identified. To drive the autonomous vehicle towards correct location, a precise protocol is chosen, which is termed common industrial protocol (CIP) where proper solutions can be achieved for all control applications using time synchronization model. Further, the data monitoring process is trailed using an online contrivance which is termed as internet router (IR) where short distance can be identified using corresponding addressing scheme.
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