AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach.

Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and naïve Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work.

VaccineChain: A checkpoint assisted scalable blockchain based secure vaccine supply chain with selective revocation.

In the present era of the pandemic, vaccination is necessary to prevent severe infectious diseases, i.e., COVID-19. Specifically, vaccine safety is strongly linked to global health and security. However, the main concerns regarding vaccine record forgery and counterfeiting of vaccines are still common in the traditional vaccine supply chains. The conventional vaccine supply chains do not have proper authentication among all supply chain entities. Blockchain technology is an excellent contender to resolve the issues mentioned above. Although, blockchain based vaccine supply chains can potentially satisfy the objectives and functions of the next-generation supply chain model. However, its integration with the supply chain model is still constrained by substantial scalability and security issues. So, the current blockchain technology with traditional Proof-of-Work (PoW) consensus is incompatible with the next-generation vaccine supply chain framework. This paper introduces a model named "VaccineChain" - a novel checkpoint-assisted scalable blockchain based secure vaccine supply chain. VaccineChain guarantees the complete integrity and immutability of vaccine supply records to combat counterfeited vaccines over the supply chain. The dynamic consensus algorithm with various validating difficulty levels supports the efficient scalability of VaccineChain. Moreover, VaccineChain includes anonymous authentication among entities to provide selective revocation. This work also consists of a use case example of a secure vaccine supply chain using checkpoint assisted scalable blockchain with customized transaction generation-rule and smart contracts to demonstrate the application of VaccineChain. The comprehensive security analysis with standard theoretical proofs ensures the computational infeasibility of VaccineChain. Further, the detailed performance analysis with test simulations shows the practicability of VaccineChain.

Enhanced link prediction using sentiment attribute and community detection.

In social network analysis, link prediction is an important area where the researchers can find the missing links and the future links possible among the users. Often, link prediction is made by analyzing the social linkage of the users in the given networks, i.e., the Topological structure of the networks. However, this approach leads to inconsistencies when researchers want to emphasize topics on which users have mainly engaged their selves in discussions. Mainly, this approach predicts future links based on available network structures without considering the topics on which the users are participating. This can be enhanced by incorporating the sentiment attributes and the community structure of the users in the network. In this paper, we propose an algorithm that incorporates the sentiment attribute of users and community structures along with the topological features. To evaluate the same, we have crawled the tweets of various countries concerning COVID-19 from Twitter. Experimental results show that users exhibiting the same emotion and belonging to the same community will influence other users to connect, thereby improving the performance of the link prediction.

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure.

This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

LSTM based prediction of malaria abundances using big data.

Malaria prevails in subtropical countries where health monitoring facilities are minimal. Time series prediction models are required to forecast malaria and minimize the effect of this disease on the population. This study proposes a novel scalable framework to predict the instances of malaria in selected geographical locations. Satellite data and clinical data, along with a long short-term memory (LSTM) classifier, were used to predict malaria abundances in the state of Telangana, India. The proposed model provided a 12 months seasonal pattern for selected regions in the state. Each region had different responses based on environmental factors. Analysis indicated that both environmental and clinical variables play an important role in malaria transmission. In conclusion, the Apache Spark-based LSTM presents an effective strategy to identify locations of endemic malaria.

An efficient EEG based deceit identification test using wavelet packet transform and linear discriminant analysis.

BACKGROUND: Brain-computer interface (BCI) is a combination of hardware and software that provides a non-muscular channel to send various messages and commands to the outside world and control external devices such as computers. BCI helps severely disabled patients having neuromuscular injuries, locked-in syndrome (LiS) to lead their life as a normal person to the best extent possible. There are various applications of BCI not only in the field of medicine but also in entertainment, lie detection, gaming, etc. METHODOLOGY: In this work, using BCI a Deceit Identification Test (DIT) is performed based on P300, which has a positive peak from 300 ms to 1000 ms of stimulus onset. The goal is to recognize and classify P300 signals with excellent results. The pre-processing has been performed using the band-pass filter to eliminate the artifacts. COMPARISON WITH EXISTING METHODS: Wavelet packet transform (WPT) is applied for feature extraction whereas linear discriminant analysis (LDA) is used as a classifier. Comparison with the other existing methods namely BCD, BAD, BPNN etc has been performed. RESULTS: A novel experiment is conducted using EEG acquisition device for the collection of data set on 20 subjects, where 10 subjects acted as guilty and 10 subjects acted as innocent. Training and testing data are in the ratio of 90:10 and the accuracy obtained is up to 91.67%. The proposed approach that uses WPT and LDA results in high accuracy, sensitivity, and specificity. CONCLUSION: The method provided better results in comparison with the other existing methods. It is an efficient approach for deceit identification for EEG based BCI.