International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management最新文献

The three-dimensional convolutional neural network (3D-CNN) and long short-term memory (LSTM) have consistently outperformed many approaches in video-based facial expression recognition (VFER). The image is unrolled to a one-dimensional vector by the vanilla version of the fully-connected LSTM (FC-LSTM), which leads to the loss of crucial spatial information. Convolutional LSTM (ConvLSTM) overcomes this limitation by performing LSTM operations in convolutions without unrolling, thus retaining useful spatial information. Motivated by this, in this paper, we propose a neural network architecture that consists of a blend of 3D-CNN and ConvLSTM for VFER. The proposed hybrid architecture captures spatiotemporal information from the video sequences of emotions and attains competitive accuracy on three FER datasets open to the public, namely the SAVEE, CK + , and AFEW. The experimental results demonstrate excellent performance without external emotional data with the added advantage of having a simple model with fewer parameters. Moreover, unlike the state-of-the-art deep learning models, our designed FER pipeline improves execution speed by many factors while achieving competitive recognition accuracy. Hence, the proposed FER pipeline is an appropriate candidate for recognizing facial expressions on resource-limited embedded platforms for real-time applications.

In this current work, Weighted Bayesian Association rules using the Fuzzy set theory are proposed with the new concept of Fuzzy Weighted Bayesian Association Rules to design and develop a Clinical Decision Support System on the Bayesian Belief Network, which is an appropriate area to work in Clinical Domain as it has a higher degree of unpredictability and causality. Weighted Bayesian Association rules to construct a Bayesian network are already proposed. A "Sharp boundary" issue related to quantitative attribute domains may cause erroneous predictions in medicine and treatment in the medical environment. So to eradicate sharp boundary problems in the medical field, the fuzzy theory is applied in attributes to deal with real-life situations. A new algorithm is designed and implemented in this paper to set up a new Bayesian belief network using the concept of Fuzzy Weighted Association rule mining under the Predictive Modeling paradigm named Fuzzy weighted Bayesian belief network using numerous clinical datasets with outshone results.

The therapeutic nature of medicinal plants and their ability to heal many diseases raises the need for their automatic identification. Different parts of plants that help in their identification include root, fruit, bark, stem but leaf images have been widely used as they are an abundant source of information and are also easily available. This work explores the branch of Artificial Intelligence, called deep learning, and proposes an Ensemble learning approach to rapidly detect medicinal plants using the leaf image. The medicinal leaf dataset consists of 30 classes. Transfer learning approach was used to initialize the parameters and pre-train Neural networks namely MobileNetV2, InceptionV3, and ResNet50. These component models were used to extract features from the input images and the softmax layer connected to the Dense Layer was used as the classifier to train the models on the concerned dataset. The obtained accuracies were validated using threefold and fivefold cross-validation. The Ensemble Deep Learning- Automatic Medicinal Leaf Identification (EDL-AMLI) classifier based on the weighted average of the component model outputs was used as the final classifier. It was observed that the EDL-AMLI outperformed the state-of-the-art pre-trained models such as MobileNetV2, InceptionV3, and ResNet50 by achieving 99.66% accuracy on the test set and average accuracy of 99.9% using threefold and fivefold cross validation.

Nowadays security is main issue during transmission of data. Among many cryptographic methods, ECC is the public key asymmetric cryptosystem which provides faster computation over smaller size in comparison to other asymmetric key cryptosystems. In this paper, we have proposed a group security algorithm using the ECC cryptography algorithm. The group security is applied to ECC in terms of m-gram selection called ECC m-gram selection. Due to the group security implementation in terms of common grams, processing speed will be faster in comparison to individual item security. We have also made the comparison study between the traditional ECC algorithm with the proposed group security algorithm using generalized frequent-common gram selection for depicting lesser time requirements to achieve better security for the whole process.

Since January 2020, the emergence of Covid-19 has sparked a worldwide search for information about Covid-19. People frequently use the internet to search the information on the virus. However, the pandemic have triggered the information-seeking trends among public. As a result, large amount of information could lead to infodemic. It will create public concerned such as panic and paranoid because this information spread rapidly. The aim of this study is to analyze information about Covid-19 that has been searched in Malaysia. We investigated online search behavior related to the Covid-19 outbreak among public by using Google Trends to understand public searching behavior on Covid-19. The findings from this study can be used as a tool to monitor public searching activities on Covid-19, which could predict future action regarding the outbreak.

Electronic healthcare systems (EHS) are the most emerging field of today's digital world which is used for remote health monitoring, evidence-based treatment, disease prediction, modeling, etc. Many internet of things (IoT) devices and body sensors are involved in such systems for data collection. Every time a cloud-based solution is adopted to collect and preserve collected personal health information. Secure data transmission is a big challenge in such an environment as the devices are memory and power-constrained. This research focuses on a lightweight ciphering mechanism that can be used to secure an electronic healthcare system. Traditional cryptographic solutions are not suitable due to the operational complexity. Some popular lightweight block ciphers which includes SIMON, HEIGHT, LEA, etc. are used in IoT device to increase the speed. Hence, in this paper, we have proposed a lightweight security framework with a flexible key structure to protect the data in the electronic healthcare system. The proposed scheme increases the speed by minimum 4 $\%$ with compared to existing literature. Our experimental analysis shows that the proposed technique also have a low computational and communicational load. The brief security analysis using automated validation of internet security protocols and applications (AVISPA) tool shows that the proposed scheme can withstand all network attacks.

The world was ambushed in 2019 by the COVID-19 virus which affected the health, economy, and lifestyle of individuals worldwide. One way of combating such a public health concern is by using appropriate, rapid, and unbiased diagnostic tools for quick detection of infected people. However, a current dearth of bioinformatics tools necessitates modeling studies to help diagnose COVID-19 cases. Molecular-based methods such as the real-time reverse transcription polymerase chain reaction (rRT-PCR) for detecting COVID-19 is time consuming and prone to contamination. Modern bioinformatics tools have made it possible to create large databases of protein sequences of various diseases, apply data mining techniques, and accurately diagnose diseases. However, the current sequence alignment tools that use these databases are not able to detect novel COVID-19 viral sequences due to high sequence dissimilarity. The objective of this study, therefore, was to develop models that can accurately classify COVID-19 viral sequences rapidly using protein vectors generated by neural word embedding technique. Five machine learning models; K nearest neighbor regression (KNN), support vector machine (SVM), random forest (RF), Linear discriminant analysis (LDA), and Logistic regression were developed using datasets from the National Center for Biotechnology. Our results suggest, the RF model performed better than all other models on the training dataset with 99% accuracy score and 99.5% accuracy on the testing dataset. The implication of this study is that, rapid detection of the COVID-19 virus in suspected cases could potentially save lives as less time will be needed to ascertain the status of a patient.

The success of deep learning, a subfield of Artificial Intelligence technologies in the field of image analysis and computer can be leveraged for building better decision support systems for clinical radiological settings. Detecting and segmenting tumorous tissues in brain region using deep learning and artificial intelligence is one such scenario, where radiologists can benefit from the computer based second opinion or decision support, for detecting the severity of disease, and survival of the subject with an accurate and timely clinical diagnosis. Gliomas are the aggressive form of brain tumors having irregular shape and ambiguous boundaries, making them one of the hardest tumors to detect, and often require a combined analysis of different types of radiological scans to make an accurate detection. In this paper, we present a fully automatic deep learning method for brain tumor segmentation in multimodal multi-contrast magnetic resonance image scans. The proposed approach is based on light weight UNET architecture, consisting of a multimodal CNN encoder-decoder based computational model. Using the publicly available Brain Tumor Segmentation (BraTS) Challenge 2018 dataset, available from the Medical Image Computing and Computer Assisted Intervention (MICCAI) society, our novel approach based on proposed light-weight UNet model, with no data augmentation requirements and without use of heavy computational resources, has resulted in an improved performance, as compared to the previous models in the challenge task that used heavy computational architectures and resources and with different data augmentation approaches. This makes the model proposed in this work more suitable for remote, extreme and low resource health care settings.