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Abstract 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.

Abstract Biosensor nodes of a wireless body area network (WBAN) transmit physiological parameter data to a central hub node, spending a substantial portion of their energy. Therefore, it is crucial to determine an optimal location for hub placement to minimize node energy consumption in data transmission. Existing methods determine the optimal hub location by sequentially placing the hub at multiple random locations within the WBAN. Performance measures like link reliability or overall node energy consumption in data transmission are estimated for each hub location. The best-performing location is finally selected for hub placement. Such methods are time-consuming. Moreover, the involvement of other nodes in the process of hub placement results in an undesirable loss of network energy. This paper shows the whale optimization algorithm (WOA)-based hub placement scheme. This scheme directly gives the best location for the hub in the least amount of time and with the least amount of help from other nodes. The presented scheme incorporates a population of candidate solutions called "whale search agents". These agents carry out the iterative steps of encircling the prey (identifying the best candidate solution), bubble-net feeding (exploitation phase), and random prey search (exploration phase). The WOA-based model eventually converges into an optimized solution that determines the optimal location for hub placement. The resultant hub location minimizes the overall amount of energy consumed by the WBAN nodes for data transmission, which ultimately results in an elongated lifespan of WBAN operation. The results show that the proposed WOA-based hub placement scheme outperforms various state-of-the-art related WBAN protocols by achieving a network lifetime of 8937 data transmission rounds with 93.8% network throughput and 9.74 ms network latency.

Abstract Background and Aims Diabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature‐fusion strategy employing supervised learning classifiers. Methods Preprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics. Results Based on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes. Conclusion The recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.

Abstract This novel approach in animal biology could revolutionize identifying endangered species, addressing the issue of misclassifying potentially harmful animals based solely on visual characteristics. Particularly impactful for farmers in agricultural fields, it aims to reduce the heightened risk of venomous animal attacks, ultimately improving safety. Due to a lack of accessible education, illiterate farmers are more susceptible to adopting superstitious beliefs, which tragically leads to fatal snakebites even when medical treatment is readily available. Furthermore, environmental factors can unexpectedly hold typically non‐threatening animals responsible for a large number of human deaths each year. However, the complexity of human recognition of these hazards has prompted the development of a novel design approach aimed at simplifying the process. Integration of the ResNet learning algorithm in conjunction with You Only Look Once (YOLOv5) within the framework is recommended to facilitate real‐time processing and improve accuracy. This combined approach not only speeds up animal recognition but also takes advantage of ResNet's deep learning capabilities. The first phase entails deploying YOLOv5 to detect the presence of snakes in the proposed study, achieving a remarkable 87% precision in snake detection thanks to the synergistic fusion of ResNet and YOLOv5.

Measurement of e-commerce usability based on static quantities variable is state-of-the-art because of the adoption of sequential tracing of the next phase in the categorical data. The global COVID-19 outbreak has completely disrupted society and drastically altered daily life. The concept refers to an electronic commerce network that appears with thorough, understandable conviction, demand, and rapid confirmation as a replacement for the economical market’s “brick-and-mortar” model, which replaces how we do everything, including business strategy, and provides a better understanding with the interpretation of e-commerce features. This study was supervised to analyze usability assessments using statistical methods and security assessments using online e-commerce security scanner tools to investigate e-business standards that consider the caliber of e-services in e-commerce websites across Asian nations. The method was developed to optimize complex systems based on multiple criteria. The initial (supplied) weights are used to determine the compromise ranking list and compromise solution. This paper examines the usability of e-commerce in rural areas using a new data set from the Jharkhand region. On the e-commerce websites of Jharkhand, India, usability is commonly considered in conjunction with learnability, memorability, effectiveness, engagement, efficiency, and completeness. Using a user-oriented questionnaire testing method, this survey attempts to close the gaps mentioned above. Then, across each column, divide each value by the column-wise sum that is created using their corresponding value, whichever produces a new matrix B. Finally, determine the row-wise sum of matrix B representing the ( $3\times1$ ) matrix. Using model trees and bagging, this study addresses classification-related issues. This regression technique is useful for problems involving classification. The model is trained using secondary data from the MBTI 16 personality factors affecting personality category.

Cloud computing has revolutionized the management and analysis of data for organizations, offering scalability, flexibility, and cost-effectiveness. Effective task scheduling in cloud systems is crucial to optimize resource utilization and ensure timely job completion. This research presents a novel method for job scheduling in cloud computing, employing the Johnson Sequencing algorithm across three servers. Originally developed for scheduling tasks in a manufacturing context, the Johnson Sequencing method has proven successful in resolving task scheduling challenges. Here, we adapt this method to address job scheduling among three servers within a cloud computing environment. The primary objective of the algorithm is to minimize the makespan, representing the total time required to complete all tasks. This study considers a scenario where a diverse set of jobs, each with varying processing durations, needs to be distributed across three servers using the Johnson Sequencing method. The algorithm strategically determines the optimal order for task execution on each server while accounting for job interdependencies and processing times on the individual servers. To put the Johnson Sequencing algorithm into practice for cloud computing job scheduling, we propose a three-step approach. First, we construct a precedence graph by analyzing the relationships among jobs. Subsequently, the precedence graph is transformed into a two-machine Johnson Sequencing problem by allocating jobs to servers. Finally, we employ the Dynamic Heuristic Johnson Sequencing method to determine the best order of jobs on each server, effectively minimizing the makespan. Through comprehensive simulations and testing, we compare the performance of our suggested Dynamic Heuristic Johnson Sequencing technique with existing scheduling algorithms. The results demonstrate significant improvements in terms of makespan reduction and resource utilization when employing our proposed method with three servers. Furthermore, our approach exhibits remarkable scalability and effectiveness in resolving complex job scheduling challenges within cloud computing settings. The outcomes of this research contribute to the optimization of resource allocation and task management in cloud systems, offering potential benefits to a wide range of industries and applications.

Scheduling of robots is one of the imperative assignment in a multi robot system. Scheduling is prerequisite when there is a multiple task need to be assigned to multi robot in an arranged manner. There is a growing need for robots to perform complex tasks autonomously. Multi-robot environment becomes complex as there are multiple factors need to be addressed simultaneously which require fast computation and more space. Using cloud computing platform could be one of the optimal solution for this problem. This paper presents the use of cloud computing platform for implementing the proposed Periodic Min-Max Algorithm (PMW) for multi robot task scheduling. Amazon web service (AWS) platform is utilized for deploying the algorithm for multi robot task scheduling. The task performed by the robots is considered as a single service in context with cloud platform and it withdraw an advantage when the number of services increases with time. Time requirement to complete the task and the load balancing parameter are analysed using the proposed approach and is compared with other relevant work. The results presented in the paper clearly shows the performance improvement in both the parameters. There is an improvement of about 3-7% in both the parameters and are reported in the paper. The paper also emphasize on the deployment of cloud computing platform for the service robots. Time completion factor is analysed and reported in the paper to proof the advantage of using cloud platform for the service robots. The novel way of using the algorithm with cloud server seeks many advantage are also observed, analysed and presented in the paper.

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network’s real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

Abstract Globally, the reserves of heavy crude oil are seven times more abundant than that of light crude, and yet, they are underutilized because of their high viscosity and density, which is largely due to the presence of large amounts of asphaltenes. Biotransformation of heavy oil asphaltenes into smaller metabolites can be used for reducing their viscosity. Several microorganisms capable of asphaltene biodegradation have been reported but only few have been characterized for its biotransformation. In the present study, a 9-membered microbial consortium was isolated from an oil contaminated soil. About 72% and 75% asphaltene biotransformation was achieved by growing cells at shake flask level and in a 1.5 l bioreactor, respectively. A representative structure of asphaltene was constructed based on LC–MS, 1H-NMR, 13C-NMR, FT-IR, ICPMS and elemental analysis (CHNS) of n-heptane purified asphaltene from Maya crude oil. Biotransformation of asphaltene, as analyzed by performing 1H-NMR, FT-IR and elemental analysis, resulted in 80% decrease in S and N when compared to the control along with incorporation of oxygen in the structure of asphaltene. About 91% decrease in the viscosity of the Maya crude oil was observed after two weeks when oil: aqueous phase ratio was 1:9. The results suggest that the isolated microbial consortium can be used for biological upgradation of heavy crude oil. To our knowledge, this is the first report where a microbial consortium resulted in such high asphaltene biotransformation.

The overall effectiveness of a website as an e-commerce platform is influenced by how usable it is. This study aimed to find out if advanced web metrics, derived from Google Analytics software, could be used to evaluate the overall usability of e-commerce sites and identify potential usability issues. It is simple to gather web indicators, but processing and interpretation take time. This data is produced through several digital channels, including mobile. Big data has proven to be very helpful in a variety of online platforms, including social networking and e-commerce websites, etc. The sheer amount of data that needs to be processed and assessed to be useful is one of the main issues with e-commerce today as a result of the digital revolution. Additionally, on social media a crucial growth strategy for e-commerce is the usage of BDA capabilities as a guideline to boost sales and draw clients for suppliers. In this paper, we have used the KMP algorithm-based multivariate pruning method for web-based web index searching and different web analytics algorithm with machine learning classifiers to achieve patterns from transactional data gathered from e-commerce websites. Moreover, through the use of log-based transactional data, the research presented in this paper suggests a new machine learning-based evaluation method for evaluating the usability of e-commerce websites. To identify the underlying relationship between the overall usability of the eLearning system and its predictor factors, three machine learning techniques and multiple linear regressions are used to create prediction models. This strategy will lead the e-commerce industry to an economically profitable stage. This capability can assist a vendor in keeping track of customers and items they have viewed, as well as categorizing how customers use their e-commerce emporium so the vendor can cater to their specific needs. It has been proposed that machine learning models, by offering trustworthy prognoses, can aid in excellent usability. Such models might be incorporated into an online prognostic calculator or tool to help with treatment selection and possibly increase visibility. However, none of these models have been recommended for use in reusability because of concerns about the deployment of machine learning in e-commerce and technical issues. One problem with machine learning science that needs to be solved is explainability. For instance, let us say B is 10 and all the people in our population are even. The hash function’s behavior is not random since only buckets 0, 2, 4, 6, and 8 can be the value of h(x). However, if B = 11, we would find that 1/11th of the even integers is transmitted to each of the 11 buckets. The hash function would work well in this situation.

Business-based decision support systems have been proposed for a few decades in the e-commerce and textile industries. However, these Decision Support Systems (DSS) have not been so productive in terms of business decision delivery. In our proposed model, we introduce a content-based image retrieval model based on a DSS and recommendations system for the textile industry, either offline or online. We used the Fashion MNIST dataset developed by Zalando to train our deep learning model. Our proposed hybrid model can demonstrate how a DSS can be integrated with a system that can separate customers based on their personal characteristics in order to tailor recommendations of products using behavioral analytics, which is trained based on MBTI personality data and Deap EEG data containing numerous pre-trained EEG brain waves. With this hybrid, a DSS can also show product usage analytics. Our proposed model has achieved the maximum accuracy compared to other proposed state-of-the-art models due to its qualitative analysis. In the first section of our analysis, we used a deep learning algorithm to train our CBIR model based on different classifiers such as VGG-net, Inception-Net, and U-net which have achieved an accuracy of 98.2% with a 2% of minimized error rate. The result was validated using different performance metrics such as F-score, F-weight, Precision, and Recall. The second part of our model has been tested on different machine learning algorithms with an accuracy rate of 89.9%. Thus, the entire model has been trained, validated, and tested separately to gain maximum efficiency. Our proposal for a DSS system, which integrates several subsystems with distinct functional sets and several model subsystems, is what makes this study special. Customer preference is one of the major problems facing merchants in the textile industry. Additionally, it can be extremely difficult for retailers to predict customer interests and preferences to create products that fulfill those needs. The three innovations presented in this work are a conceptual model for personality characterization, utilizing an amalgamation of an ECG classification model, a suggestion for a textile image retrieval model using Denoising Auto-Encoder, and a language model based on the MBTI for customer rating. Additionally, we have proposed a section showing how blockchain integration in data pre-processing can enhance its security and AI-based software quality assurance in a multi-model system.

The issue of pollution in urban cities is a major problem these days especiallyin cities like the New Delhi is detected with more number of toxic gases in air, whic h has deduced the air quality of New Delhi. Thus, predictive analytics play a significant role in predicting the future instances of air quality based on the historical data. Forecasting the air quality of these cities is mandatory to overcome its consequences. Several machines learning algorithm is widely used these days to predict the future instances. Such as random forest, support vector machine, regression, classification, and so on. Main pollutants which present in the air are PM2.5, PM10, CO, NO2, SO2and O3 . In this paper we have focused mainly on data set of New Delhi for predicting ambient air pollution and quality using several machines learning algorithm.

This protocol has been designed to measure the in-vitro DNA polymerization activity in crude cell extracts of the Antarctic bacterium Pseudomonas syrinagae Lz4W. This bacterium can grow at 4 °C with optimum growth rate at 22 °C. The slow growth rate of the bacterium observed at low temperature (4 °C) compared to higher temperature (22 °C) can be attributed to the reduced rate of DNA replication at low temperature. Here we describe a protocol which we have used to quantify the in vitro DNA polymerization of cell extracts at two different temperatures.