Physiological Conditions Monitoring System Based on IoT

Internet of Things (IoT) comprises smart devices, sensor nodes, and wearable elements for data sharing and services, through which the sensor networks are used for developing smart environments. IoT models are growing very fast because of the rapid growth of wireless devices and communications. In addition, the heterogeneous nature of the IoT paradigm heightens the risks to both individuals' data privacy and their data's security. As a direct consequence of this, comprehensive security models are required in order to guarantee secure communication between the various devices. The biggest obstacle in the way of effective and reliable device interaction in the Internet of Things is security

Image Processing based Plant Disease Detection and Classification

Generally, it has been observed that due to lack of proper knowledge of disease intensity, the farmer is not able to use the pesticide in proper quantity to treat the diseases. The use of pesticide mostly becomes more than necessary, due to which there is not only a loss of money, but also it causes soil and environmental pollution. If diseases severity-wise labelled data sets are available, it can be used to develop pesticide recommendation systems. Images with least infection severity can be used to train and validate a DL model to capture the plant diseases at very initial stage. Classification techniques may be viewed as variations of detection systems; however, instead of attempting to identify only one particular illness among several diseases, classification methods detect and name the diseases harming the plant. This presents various classification and plant disease detection methods based on image processing with results

Study And Modeling of Question Answer System Using Deep Learning Technique of AI

In this paper, the different QA system types, the theoretical foundation for deep learning models, the metaheuristic optimization techniques, and the performance assessment metrics are discussed. A suggested architecture for a question-and-answer system that takes a deep learning approach is shown here. The study also covers the constraints and factors to take into account regarding the aforementioned system

Denoising ECG Signal Using DWT with EAVO

Cardiovascular diseases are the leading cause of death across the world, and traditional methods for determining cardiac health are highly invasive and expensive. Detecting CVDs early is critical for effective treatment, yet traditional detection methods lack accessibility, accuracy, and cost-effectiveness – leaving patients with little hope of taking control of their own cardiac health. Noisy ECG signals make it difficult for health practitioners to accurately read and determine heart health. Unreliable readings can lead to misdiagnosis and needless expense. Despite the importance of ECG analysis, traditional methods of signal denoising are inefficient and can produce inaccurate results. This means that medical practitioners are struggling to obtain reliable readings, leaving them unable to accurately treat their patients and leading to a lack of confidence in the medical field. The Enhanced African Vulture Optimization (AVO) algorithm with Discrete Wavelet Transform (DWT) optimized by adaptive switching mean filtration (SMF) is proven to provide accurate denoising of the ECG signal. With this reliable method, medical professionals can quickly and accurately diagnose patients. Obtaining accurate ECG signals and interpreting them quickly is a challenge for healthcare professionals. Not only it takes a lot of time and skill but also requires specialized software to interpret the signals accurately. Healthcare professionals are facing a serious challenge when it comes to obtaining accurate ECG signals and interpreting them quickly. It requires them to spend extra time and effort, as well as specialize in the field with expensive software. Time is of the essence in healthcare and ECG readings can mean the difference between life and death. Specialized software can be expensive and time-consuming for those who don't have the resources or expertise. Our easy-to-use platform allows healthcare professionals to quickly interpret ECG signals, saving time, money, and lives! Get accurate readings. The EAVO algorithm and MIT-BIH dataset provide an effective solution to this problem. With the proposed filter built using EAVO, businesses can attain significant enhancements in reliable parameters and obtain accurate testing results in terms of SNR, MD, MSE and NRMSE

Study of Trust Aggregation Authentication Protocol

The main focus of this work is to sense and share the data that are required to be trusted and the solutions are to be provided to the data, as trust management models. Additionally, the elements in the IoT network model are required to communicate with the trusted links, hence the identity services and authorization model are to be defined to develop the trust between the different entities or elements to exchange data in a reliable manner. Moreover, data and the services are to be accessed from the trusted elements, where the access control measures are also to be clearly defined. While considering the whole trust management model, identification, authentication, authorization and access control are to be clearly defined

Optimal ECG Signal Denoising Using DWT with Enhanced African Vulture Optimization

Cardiovascular diseases (CVDs) are the world's leading cause of death; therefore cardiac health of the human heart has been a fascinating topic for decades. The electrocardiogram (ECG) signal is a comprehensive non-invasive method for determining cardiac health. Various health practitioners use the ECG signal to ascertain critical information about the human heart. In this paper, the noisy ECG signal is denoised based on Discrete Wavelet Transform (DWT) optimized with the Enhanced African Vulture Optimization (AVO) algorithm and adaptive switching mean filter (ASMF) is proposed. Initially, the input ECG signals are obtained from the MIT-BIH ARR dataset and white Gaussian noise is added to the obtained ECG signals. Then the corrupted ECG signals are denoised using Discrete Wavelet Transform (DWT) in which the threshold is optimized with an Enhanced African Vulture Optimization (AVO) algorithm to obtain the optimum threshold. The AVO algorithm is enhanced by Whale Optimization Algorithm (WOA). Additionally, ASMF is tuned by the Enhanced AVO algorithm. The experiments are conducted on the MIT-BIH dataset and the proposed filter built using the EAVO algorithm, attains a significant enhancement in reliable parameters, according to the testing results in terms of SNR, mean difference (MD), mean square error (MSE), normalized root mean squared error (NRMSE), peak reconstruction error (PRE), maximum error (ME), and normalized root mean error (NRME) with existing algorithms namely, PSO, AOA, MVO, etc

Classification Models for Plant Diseases Diagnosis: A Review

Plants are important source of our life. Crop production in a good figure and good quality is important to us. The diagnosis of a disease in a plant can be manual or automatic. But manual detection of disease in a plant is not always correct as sometimes it can be not be seen by naked eyes so an automatic method of detection of plant diseases should be there. It can make use of various artificial intelligence based or machine learning based methods. It is a tedious task as it needs to be identified in earlier stage so that it will not affect the entire crop. Disease affects all species of plant, both cultivated and wild. Plant disease occurrence and infection severity vary seasonally, regarding the environmental circumstances, the kinds of crops cultivated, and the existence of the pathogen. This review attempts to provide an exhaustive review of various plant diseases and its types, various methods to diagnose plant diseases and various classification models used so as to help researchers to identify the areas of scope where plant pathology can be improved

FML: Face Model Learning from Videos

Monocular image-based 3D reconstruction of faces is a long-standing problem in computer vision. Since image data is a 2D projection of a 3D face, the resulting depth ambiguity makes the problem ill-posed. Most existing methods rely on data-driven priors that are built from limited 3D face scans. In contrast, we propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces. Our face model is learned using only corpora of in-the-wild video clips collected from the Internet. This virtually endless source of training data enables learning of a highly general 3D face model. In order to achieve this, we propose a novel multi-frame consistency loss that ensures consistent shape and appearance across multiple frames of a subject's face, thus minimizing depth ambiguity. At test time we can use an arbitrary number of frames, so that we can perform both monocular as well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ, Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19

Photoluminescence Properties of Two Closely Related Isostructural Series Based on Anderson-Evans Cluster Coordinated With Lanthanides [Ln(H2O)7{X(OH)6Mo6O18}]•yH2O, X = Al, Cr

The paper describes synthesis and structural characterization of the whole series of two closely related lanthanide coordinated chromium or aluminum hexamolybdates (Anderson-Evans cluster) including twelve new members hitherto unreported: [Ln(H2O)7{X(OH)6Mo6O18}]·4H2O and [Ln(H2O)7{X(OH)6Mo6O18}Ln(H2O)7]{X(OH)6Mo6O18}·16H2O where X = Al or Cr and Ln = La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, and Y. Crystal structures of all the solids were established by powder and single crystal X-ray diffraction techniques. The two series are dictated by a different aggregation of the same set of molecular species: Lighter lanthanides favor coordination interaction between lanthanide ions and molybdate cluster forming 1D chains (Series I) while the heavier lanthanides result in the stacking of a cation, a pair of lanthanide hydrates coordinating to the cluster, and an anion, the discrete cluster is further stabilized through a large number of water molecules (Series II). Crystallization with Er3+ and Tm3+ ions results in a concomitant mixture of Series I and II. Photoluminescence of single crystals of all the chromium molybdates was dominated by a ruby-like emission including those which contain optically active ions Pr, Sm, Eu, Tb, Dy, and Tm. In contrast, aluminum analogs showed photoluminescence corresponding to characteristic lanthanide emissions. Our results strongly suggest a possible energy transfer from f levels of lanthanide ions to d levels of chromium (III) causing the quenching of lanthanide emission when coordinated with chromium molybdates. Intensity measurements showed that the emission from chromium molybdates are almost two orders of magnitude lower than naturally occurring ruby with broader line widths at room temperature