Search

Malaria is predominant in many subtropical nations with little health-monitoring infrastructure. To forecast malaria and condense the disease's impact on the population, time series prediction models are necessary. The conventional technique of detecting malaria disease is for certified technicians to examine blood smears visually for parasite-infected RBC (red blood cells) underneath a microscope. This procedure is ineffective, and the diagnosis depends on the individual performing the test and his/her experience. Automatic image identification systems based on machine learning have previously been used to diagnose malaria blood smears. However, so far, the practical performance has been insufficient. In this paper, we have made a performance analysis of deep learning algorithms in the diagnosis of malaria disease. We have used Neural Network models like CNN, MobileNetV2, and ResNet50 to perform this analysis. The dataset was extracted from the National Institutes of Health (NIH) website and consisted of 27,558 photos, including 13,780 parasitized cell images and 13,778 uninfected cell images. In conclusion, the MobileNetV2 model outperformed by achieving an accuracy rate of 97.06% for better disease detection. Also, other metrics like training and testing loss, precision, recall, fi-score, and ROC curve were calculated to validate the considered models. [ABSTRACT FROM AUTHOR]

Artificial intelligence is an emerging technology that revolutionizes human lives. Despite the fact that this technology is used in higher education, many professors are unaware of it. In this current scenario, there is a huge need to arise, implement information bridge technology, and enhance communication in the classroom. Through this paper, the authors try to predict the future of higher education with the help of artificial intelligence. This research article throws light on the current education system the problems faced by the subject faculties, students, changing government rules, and regulations in the educational sector. Various arguments and challenges on the implementation of artificial intelligence are prevailing in the educational sector. In this concern, we have built a use case model by using a student assessment data of our students and then built a synthesized using generative adversarial network (GAN). The dataset analyzed, visualized, and fed to different machine learning algorithms such as logistic Regression (LR), linear discriminant analysis (LDA), K-nearest neighbors (KNN), classification and regression trees (CART), naive Bayes (NB), support vector machines (SVM), and finally random forest (RF) algorithm and achieved a maximum accuracy of 58%. This article aims to bridge the gap between human lecturers and the machine. We are also concerned about the psychological emotions of the faculty and the students when artificial intelligence takes control. [ABSTRACT FROM AUTHOR]

In this work, the known and unknown structural as well as biological properties of 3-(2-aminoethyl) indole (tryptamine) were interpreted using molecular spectroscopy (FT-IR, FT-Raman, NMR and UV–Visible) and cheminformatic tools. The supportive drug-related information was gained by analysing the obtained data which will be useful for the drug chemist for the pharmaceutical research. The important biological properties of the present chemical species satisfied the Lipinski five rules and it was opt to fabricate complex antibiotic compounds. The acquired charge potential load for creating antibiotic strain on compositional parts was keenly observed from the obtained data and it was evaluated by the vibrational analysis and Mulliken charge profile. From the NMR data, the chemical nodal points were noted and their movement around the molecule was carefully monitored. The degenerate and non-degenerate energy profile of orbital interaction system was studied and the link of chemical reactivity path was identified. The significance of excited electronic transitions among non-bonding molecular orbital system was justified and their transitional energy coefficient was determined. The toxicity level was checked from the chirality characteristics and enantiomer structure obtained from vibrational circular dichroism profile. [ABSTRACT FROM AUTHOR]

Digital watermarking is a process in which a piece of information is embedded into a digital data. In this article we review the digital watermarking scheme of image which uses DFT to embed the watermark. Digital watermarking using DFT has attracted researchers due to the fact that watermark using DFT are RST invariant compared to other scheme using other transforms such as DCT or DWT. In the presented article, we tried to cover all the aspect of digital watermarking particularly on DFT based scheme. We also gave some terminology used in the digital image watermarking and briefly discuss the attacks involved in digital image watermarking. [ABSTRACT FROM AUTHOR]

Face Biometric systems are vulnerable to spoofing attacks. The activeness of face recognition has raised concerns about face spoof attacks, where a printed photo or video of an authorized persons face could be used to gain access to facilities or service. Hence, we present a novel approach based on analyzing facial image quality and face image texture for detecting whether there is a live person or a fake person. The proposed approach analyzes the spoof face detection based on video and print out image. Two different feature are extracted (chromatic moment and Local Binary Pattern (LBP)) from the face image. Extracted feature are fed into Classifier, SVM classifier trained for different face spoof attack (i.e. printed photo and played video) is used to distinguish between real and fake face.Face Biometric systems are vulnerable to spoofing attacks. The activeness of face recognition has raised concerns about face spoof attacks, where a printed photo or video of an authorized persons face could be used to gain access to facilities or service. Hence, we present a novel approach based on analyzing facial image quality and face image texture for detecting whether there is a live person or a fake person. The proposed approach analyzes the spoof face detection based on video and print out image. Two different feature are extracted (chromatic moment and Local Binary Pattern (LBP)) from the face image. Extracted feature are fed into Classifier, SVM classifier trained for different face spoof attack (i.e. printed photo and played video) is used to distinguish between real and fake face.Face Biometric systems are vulnerable to spoofing attacks. The activeness of face recognition has raised concerns about face spoof attacks, where a printed photo or video of an authorized persons face could be used to gain access to facilities or service. Hence, we present a novel approach based on analyzing facial image quality and face image texture for detecting whether there is a live person or a fake person. The proposed approach analyzes the spoof face detection based on video and print out image. Two different feature are extracted (chromatic moment and Local Binary Pattern (LBP)) from the face image. Extracted feature are fed into Classifier, SVM classifier trained for different face spoof attack (i.e. printed photo and played video) is used to distinguish between real and fake face. [ABSTRACT FROM AUTHOR]

The reliance on sensor nodes for sending data to the master node raises serious concerns about both data confidentiality & authenticity in hostile environments. In particular an unauthenticated sensor node may exist in the network which leads to various attacks. We target a multi-tier sensor network with resource rich master node at the upper tier and authentication nodes at the middle tier. Master nodes collect data from the sensor nodes and response the queries from the network holder. The proposed scheme offers data confidentiality by providing authentication to sensor nodes which exists in the network (particularly in the cell). The keys which are used for authentication is remains valid only over a session time which makes the sensor network protected. The high efficiency of our approaches is defined by detailed performance evaluations. [ABSTRACT FROM AUTHOR]

In this study, an attempt has been made to study an image retrieval technique based on the combination of Haar wavelet transformation using lifting scheme and the colour histogram (CH) called lifting wavelet-based colour histogram. The colour feature is described by the CH, which is translation and rotation invariant. The Haar wavelet transformation is used to extract the texture features and the local characteristics of an image, to increase the accuracy of the retrieval system. The lifting scheme reduces the processing time to retrieve images. The experimental results indicate that the proposed technique outperforms the other schemes, in terms of the average precision, the average recall and the total average precision/recall. [ABSTRACT FROM AUTHOR]

Blood cells carry important information that can be used to represent a person's current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet's architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.