Java Platform Research Articles

Detecting and Localizing Wireless Spoofing Attacks on the Internet of Medical Things

This paper proposes a hybrid approach using design science research to identify rogue RF transmitters and locate their targets. We engineered a framework to identify masquerading attacks indicating the presence of multiple adversaries posing as a single node. We propose a methodology based on spatial correlation calculated from received signal strength (RSS). To detect and mitigate wireless spoofing attacks in IoMT environments effectively, the hybrid approach combines spatial correlation analysis, Deep CNN classification, Elliptic Curve Cryptography (ECC) encryption, and DSRM-powered attack detection enhanced (DADE) detection and localization (DAL) frameworks. A deep neural network (Deep CNN) was used to classify trusted transmitters based on Python Spyder3 V5 and ECC encrypted Hack RF Quadrature Signals (IQ). For localizing targets, this paper also presents DADE and DAL frameworks implemented on Eclipse Java platforms. The hybrid approach relies on spatial correlation based on signal strength. Using the training methods of Deep CNN1, Deep CNN2, and Long Short-Term Memory (LSTM), it was possible to achieve accuracies of 98.88%, 95.05%, and 96.60% respectively.

Bringing architecture-based adaption to the mainstream

Software architecture has been shown to provide an appropriate level of granularity for representation of a managed software system and reasoning about the impact of adaptation choices on its properties. Software architecture-based adaptability is the ability to adapt a software system in terms of its architectural elements, such as its components and their interfaces. Despite its promise, architecture-based adaptation has remained largely elusive, mainly because it involves heavy engineering effort of making non-trivial changes to the manner in which a software system is implemented. In this paper, we present Acadia—a framework that automatically enables architecture-based adaptation of practically any Java 9+ application without requiring any changes to the implementation of the application itself. Acadia builds on the Java Platform Module System (JPMS), which has brought extensive support for architecture-based development to Java 9 and subsequent versions. Acadia extends JPMS with the ability to provide and maintain a representation of an application’s architecture and make changes to it at runtime. The results of our experimental evaluation, conducted on three large open-source Java applications, indicate that Acadia is able to efficiently apply dynamic changes to the architecture of these applications without requiring any changes to their implementation.

Research on computationally generative design of woven braced structure based on fiber composite material in architectural design industry

ABSTRACT With the intervention of computer algorithm-assisted design in the realm of contemporary architectural design, architectural spatial forms and corresponding design methods have improved significantly. However, the limitations of material strength and plasticity impose certain constraints on the architectural design of the new century. Based on this occurrence, this paper studies the development of a computational generative design system for woven braced structure composed of fiber-reinforced materials. Through experimental data of material performance, the research firstly argues feasibility of employing fiber composite materials to braced structure. The conclusion is that the structure designed and generated could produce a support of 1 ton per ㎡. Moreover, based on the ant colony algorithm and the algorithm principle of cellular automata on the Java platform, the study designs a generative system with iterative computing of force as the major axis to achieve the self-organization and adaptability of the braced structure. Above all, this paper proposes a design strategy engaging with computational generative design approach, as well as a design attempt for the material and form of architecture.

A Novel Approach of Cloud Computing Network for Authentication and Security Enhancement of IoT Enabled Cancer Forecasting System

Recently, a variety of approaches have been employed to address a broad spectrum of real-world issues; these methodology cover a variety of areas, including healthcare systems. Previous researchers concentrated on health-care monitoring software. It had several shortcomings, such as poor health-care data storage, time, expense, and processing complexity. This paper proposes a unique IoT-enabled and secured clinical monitoring paradigm to address these issues. Initially, implant several sensors to gather information on vital indicators like body temperature fluctuation. Phone numbers, marital status, heart rate deviation, residence, name, age, and blood pressure are among the patient's health information. The IoT medical sensor dataset is used in this investigation. During pre-processing, extra unnecessary attributes are removed, resulting in data size reduction and normalization. A convolutional neural network supporting the classification of cancer sickness that is based on the improved teaching-learning optimization (CNN-ITLO) method. The CNN-ITLO model ascertains whether or not the patient is cancer-prone based on the sensor input. The management of the hospital receives the gathered data after which it is analyzed. Lastly, the homomorphic encryption approach is used to encrypt and store the patient's data on the cloud. The Java platform will be used to carry out the recommended work. According to the experiment results, the suggested method performed better than current cutting-edge practices.

My Insurance Agent App

In today’s world life is full of risks so everyone wants to make himself secure, everyone wants to keep them on the safe side policy plays a vital role and provides them insurance about their money security, health security, accidental security, etc. This system has been proposed to maintain the record of the policyholder, Customer details, policy detail. My Insurance Agent App is Web Base Application implemented in java platform using MySql as backend application. The main aim of the project is any customer they can access the system through website at any time without going to the company. This project is to develop a web application for insurance company to atomize work procedure, using this system agents and policy holders can know details about present policies, schemes, policy specifications, terms and conditions on policy, policy registration by the customers. Agents commission is based upon customer policy registration and target agent achieves for every month or year. In present system there is no need of human interference in calculating any details. Total work is done using management system which will save time and less paper work and even human resource. The web based application is used within their organization under the distributed accessibility to check the status of the customers who have taken new policies and their proper track and reminding of policy premium payments. The major problem under the manual process is to keep track of the existing insurance policy holders, and remaining them at regular intervals about their policy premium payments. In order to render the ordinary services also it takes a great lot of time in just searching through the registers for the existing customer’s base. If the process of data storage can be automated and follow up of search can be increased, there is always a heavy chance of getting extra customers in turn, which can increase the profits ratio upon the organization

Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.

Artificial Intelligence (AI) plays a pivotal role in the diagnosis of health conditions ranging from general well-being to critical health issues. In the realm of health diagnostics, an often overlooked but critical aspect is the consideration of cost-sensitive learning, a facet that this study prioritizes over the non-invasive nature of the diagnostic process whereas the other standard metrics such as accuracy and sensitivity reflect weakness in error profile. This research aims to investigate the total cost of misclassification (Total Cost) by decision rule Machine Learning (ML) algorithms implemented in Java platforms such as DecisionTable, JRip, OneR, and PART. An augmented dataset with conjunctiva images along candidates' demographic and anthropometric features under supervised learning is considered with a specific emphasis on cost-sensitive classification. The opted decision rule classifiers use the text features, additionally the image feature 'a* value of CIELAB color space' extracted from the conjunctiva digital images as input attributes. The pre-processing consists of amalgamating text and image features on a uniform scale, normalizing. Then the 10-fold cross-validation enables the classification of samples into two categories: the presence or absence of the anemia. This study utilizes the Cost Ratio (ρ) extracted from the cost matrix to meticulously monitor the Total Cost in four different cost ratio methodologies namely Uniform (U), Uniform Inverted (UI), Non-Uniform (NU), and Non-Uniform Inverted (NUI). It has been established that the PART classifier stands out as the top performer in this binary classification task, yielding the lowest mean total cost of 629.9 compared to other selected classifiers. Moreover, it demonstrates a comparatively lower standard deviation 335.9, and lower total cost across all four different cost ratio methodologies. The ranking of algorithm performance goes as follows: PART, JRIP, DecisionTable, and OneR. The significance of adopting a cost-sensitive learning approach is emphasized showing the PART classifier's consistent performance within the proposed framework for learning the anemia dataset. This emphasis on cost-sensitive learning not only enhances the recommendations in diagnosis but also holds the potential for substantial cost savings and makes it a noteworthy focal point in the advancement of AI-driven health care.

Location decision of low-altitude service station for transfer flight based on modified immune algorithm

The location of Low-Altitude Flight Service Station (LAFSS) is a comprehensive decision work, and it is also a multi-objective optimization problem (MOOP) with constraints. As a swarm intelligence search algorithm for solving constrained MOOP, the Immune Algorithm (IA) retains the excellent characteristics of genetic algorithm. Using some characteristic information or knowledge of the problem selectively and purposefully, the degradation phenomenon in the optimization process can be suppressed and the global optimum can be achieved. However, due to the large range involved in the low-altitude transition flight, the geographical characteristics, economic level and service requirements among the candidate stations in the corridor are quite different, and the operational safety and service efficiency are interrelated and conflict with each other. And all objectives cannot be optimal. Therefore, this article proposes a Modified Immune Algorithm (MIA) with two-layer response to solve the constrained multi-objective location mathematical model of LAFSS. The first layer uses the demand track as the cell membrane positioning pattern recognition service response distance to trigger the innate immunity to achieve the basic requirements of security service coverage. In the second layer, the expansion and upgrading of adjacent candidate sites are compared to the pathogen’s effector, and the adaptive immunity is directly or indirectly triggered again through the cloning, mutation and reproduction between candidate sites to realize the multi-objective equilibrium of the scheme. Taking 486,000 km2 of Sichuan Province as an example, MIA for LAFSS is simulated by the MATLAB platform. Based on the Spring open source application framework of Java platform, the cesiumjs map data is called through easyui, and the visualization of site selection scheme is presented with the terrain data of Map World as the background. The experimental results show that, compared with dynamic programming and ordinary immunization, the immune trigger mode of double response and the improved algorithm of operation parameter combination designed by the Taguchi experiment, the total economic cost of location selection is reduced by 26.4%, the service response time is reduced by 25%, the repeat coverage rate is reduced by 29.5% and the effective service area is increased by 17.5%. The security risk, service efficiency and location cost are balanced. The present work is to provide an effective location method for the layout number and location of local transfer flight service stations. For complex scenes with larger scale of low-altitude flight supply and demand and larger terrain changes in the region, the above research methods can be used to effectively split and reduce the dimension.

Strengthening IoT Intrusion Detection through the HOPNET Model

The rapid growth of Internet of Things (IoT) applications has raised concerns about the security of IoT communication systems, particularly due to a surge in malicious attacks leading to network disruptions and system failures. This study introduces a novel solution, the Hyper-Parameter Optimized Progressive Neural Network (HOPNET) model, designed to effectively detect intrusions in IoT communication networks. Validation using the Nsl-Kdd dataset involves meticulous data preprocessing for error rectification and feature extraction across diverse attack categories. Implemented on the Java platform, the HOPNET model undergoes comprehensive evaluation through comparative analysis with established intrusion detection methods. Results demonstrate the superiority of the HOPNET model, with improved attack prediction scores and significantly reduced processing times, highlighting the importance of advanced intrusion detection methods for enhancing IoT communication security. The HOPNET model contributes by establishing robust defense against evolving cyber threats, ensuring a safer IoT ecosystem, and paving the way for proactive security measures as the IoT landscape continues to evolve.

Time Window Oriented IoT Vehicle Pathway Study for the Dynamically Changing Needs of E-Commerce Customers

The main dynamic truck routing problem also presents a significant difficulty in the logistics sector, which is an unavoidable development trend of the contemporary technological changing society. A dynamic vehicle routing problem with time window model is suggested by the study in order to establish an effective and low-energy dynamic response method. The fundamental concept is to disrupt the conventional strategy of static dynamic consumers responding in time slots by dividing the dynamic time window into a static time window with several time slice intervals. The study makes use of cutting-edge ideas including dynamic attitude, before-and-after time slicing, and continuous optimisation while proposing a new method for model solution to optimise dynamic vehicle route issues effectively and affordably. The study employs the Solomon optimisation dataset and runs simulation studies on the Java platform to confirm its efficacy. The experimental findings demonstrated that the optimisation technique employed in the study reduced the cost of travelling by 83.8 miles while also considerably increasing the average vehicle utilisation by 3.6%. Because driving distance cost and vehicle number cost are typically positively connected with dynamic attitude, the study employs solutions that can increase dynamic response efficiency and save money. As a result, their robustness is higher.

A novel perspective for M-polynomials to compute molecular descriptors of borophene nanosheet

Nanomaterials feature exceptional, one-of-a-kind qualities that might be used in electronics, medicine, and other industries. Two-dimensional nanomaterials called borophene have a variety of intriguing characteristics, which helped them to leave an indelible impression in the fields of chemistry, material science, nanotechnology, and condensed matter physics. The concept of modelling the structure of a molecule or chemical network to a chemical graph and then quantitatively analysing them with the aid of topological descriptors was a major advance in the fields of mathematics and chemistry, with a wide range of applications. M-polynomial approach is a very versatile and quick method for computing the degree-based descriptors of chemical graphs or networks. The degree-based descriptors of the beta _{12}-Borophene nanosheet are established in this study utilising the M-polynomial technique. A program code that enables to generate the M-polynomial of any chemical structure was developed in Java platform and the same is displayed. At the conclusion, the numerical and graphical comparison based on the identified analytic expressions is also provided. Additionally, the QSPR analysis was also carried out and the outcoms are presented therein.

BacSeq: A User-Friendly Automated Pipeline for Whole-Genome Sequence Analysis of Bacterial Genomes.

Whole-genome sequencing (WGS) of bacterial pathogens is widely conducted in microbiological, medical, and clinical research to explore genetic insights that could impact clinical treatment and molecular epidemiology. However, analyzing WGS data of bacteria can pose challenges for microbiologists, clinicians, and researchers, as it requires the application of several bioinformatics pipelines to extract genetic information from raw data. In this paper, we present BacSeq, an automated bioinformatic pipeline for the analysis of next-generation sequencing data of bacterial genomes. BacSeq enables the assembly, annotation, and identification of crucial genes responsible for multidrug resistance, virulence factors, and plasmids. Additionally, the pipeline integrates comparative analysis among isolates, offering phylogenetic tree analysis and identification of single-nucleotide polymorphisms (SNPs). To facilitate easy analysis in a single step and support the processing of multiple isolates, BacSeq provides a graphical user interface (GUI) based on the JAVA platform. It is designed to cater to users without extensive bioinformatics skills.

DRUG DISTRIBUTION INFORMATION SYSTEM AT PT. ANUGERAH PHARMINDO LESTARI JAKARTA BASED ON JAVA NETBEANS

Drug Distribution Information System (DDIS) implemented at PT. Pharmindo Lestari Jakarta has changed the way companies manage and distribute pharmaceutical products. Developed using NetBeans' Java platform, the system has won numerous awards for its seamless integration and ease of use. DDIS provides real-time information on inventory levels, product expiration dates and sales data, enabling companies to make informed decisions about procurement and distribution activities. It also offers various features such as order tracking, billing and invoicing, which have greatly increased the efficiency of the company's operations. One of the main benefits of DDIS is its ability to integrate with other systems such as accounting software, which has eliminated the need for manual data entry and reduced the risk of errors. The system also provides comprehensive reporting capabilities, enabling companies to analyze sales trends, identify potential bottlenecks, and make informed decisions about resource allocation.

Bibliometric and visualized analysis of drug resistance in ovarian cancer from 2013 to 2022.

As one of the cancers that seriously threatens women's health, ovarian cancer has a high morbidity and mortality rate. Surgery and chemotherapy are the basic treatment strategies for ovarian cancer, and chemotherapy resistance is a significant factor in affecting the prognosis, survival cycle, and recurrence of ovarian cancer. This article aims to analyze articles about ovarian cancer and drug resistance via bibliometric software, offering new ideas and directions for researchers in this field. Both Citespace and Vosviewer are bibliometric software on the Java platform. Articles were collected on ovarian cancer and drug resistance in the Web of Science Core Collection database from 2013 to 2022. The countries, institutions, journals, authors, keywords, and references were analyzed, and the development status of this field was indicated from multiple perspectives. Studies on ovarian cancer and drug resistance generally showed an increasing trend from 2013 to 2022. The People's Republic of China and Chinese institutions contributed more to this field. Gynecologic Oncology published the most articles, and the journal with the most citations was Cancer Research. Li Li was the author with the most publications, and Siegel RL was the author with the most citations. Through burst detection, it can be found that the research hotspots in this field mainly focused on the in-depth exploration of the drug resistance mechanism of ovarian cancer and the progress of PARP inhibitors and bevacizumab in the treatment of ovarian cancer. Many studies on the mechanism of drug resistance in ovarian cancer have been discovered; however, the deeper mechanism remains to be explored. Compared with traditional chemotherapy drugs, PARP inhibitors and bevacizumab have shown better efficacy, but PARP inhibitors have initially shown drug resistance. The future direction of this field should be to overcome the resistance of existing drugs and actively develop new ones.

Constructing exception handling chains for testing Java virtual machine implementations

AbstractThe Java virtual machine (JVM) is the cornerstone of the Java platforms. A JVM's exception handling implementation interrupts, when the objective application encounters an exception (or an error), the normal execution of the application and performs specific handling tasks. However, little research has been done in systematically validating JVMs' exception handling implementations—test programs or even applications need to be carefully designed for throwing/catching exceptions at runtime; a JVM's exception handling implementation is also complicated, making it challenging to design tests for testing all of its functionalities. Inspired by the recent success of fuzz testing of compilers and JVM implementations, we introduce EHCBuilder, the first technique for fuzzing JVMs' exception handling implementations. The key idea is to construct exception handling chains, each of which abstracts a program's execution into a sequence of exception throwings, catchings, and/or handlings. A classfile seed can then be mutated into test programs with diverse exception handling chains, enabling (1) exceptions to be continuously thrown and caught at runtime, and (2) JVMs' exception handling implementations to be much more thoroughly tested. We have implemented EHCBuilder and evaluated EHCBuilder on popular JVM implementations including OpenJDK's HotSpot, Eclipse's OpenJ9, Azul's Zulu, and Oracle's GraalVM. Our results show that EHCBuilder can generate programs with very intricate exception handling chains and reveal differences among JVMs' exception handling implementations: Up to thousands of lines of source code in HotSpot's exception handling implementation are covered more than the original benchmarks; during 39 K iterations, EHCBuilder generates exception handling chains of different lengths, revealing 258 runtime differences. We classify the differences into four categories, and reveal a fast throw issue confirmed by HotSpot developers and another initCause issue confirmed by the OpenJ9 community.

A hybrid Square Shaped Adaptive Neuro-Fuzzy Interference with M-PeSOA model for optimal path selection

In Vehicular cloud computing (VCC), at the time of congestion, to deal with traffic, the vehicles’ underutilized resources are shared; subsequently, these resources aren’t constrained to computing power, storage, along with internet connectivity. Nevertheless, owing to the vehicular network characteristics, attaining the QoS requirements search together with the allocation of resources in the Vehicular Cloud (VC) has turned into a complicated task. An intelligent Square Shaped (SS)-Adaptive Neuro-Fuzzy Interference (SS-ANFIS) methodology for Resource Scheduling (RS) in addition to Mean-centered Penguins Search Optimization Algorithm (M-PeSOA) for Optimal Path Selection (OPS) in the VC is proposed here for efficient resource allocation. (a) Feature extraction, (b) Vehicles clustering, (c) OPS, (d) Resource information extraction, and (e) RS included in the proposed methodology. First, the vehicular network is initialized following that the vehicle features are extracted. Next, Cluster Heads (CHs) are generated regarding which vehicles are clustered; subsequently, the multi-paths are generated. After that, by employing the M-PeSOA, the OPS procedure is conducted; thus, the VC’s resource information is extracted aimed at scheduling the resources efficiently. Lastly, by employing the SS-ANFIS, vehicles are scheduled in the optimal paths. The proposed resource allocation system’s performance is assessed, and the experiential outcomes are analogized using the sumo tool and java platform.

Java in Robotics: Bridging Software Development and Hardware Control

This research examines how Java bridges robotics software development with hardware control. The main goals are to assess Java's performance in robotic system integration, identify its drawbacks, and suggest ways to improve it. JRobotics, LeJOS, and ROSJava, are reviewed using secondary data to determine their effects on hardware interface, real-time performance, and data processing. According to major studies, Java's platform freedom and modularity enable software and hardware integration. Real-time performance, hardware interface, and memory management remain issues. The Real-Time Specification for Java (RTSJ) and specialized libraries provide partial solutions but need additional development. Policy implications include investing in Java library improvements and improving Java developer-robotics researcher cooperation. Research and optimization will improve Java's position in robotics, making robots more efficient and versatile.

CLASSIFICATION FOR BIG DATA DRIVEN MARINE WEATHER FORECASTING USING MACHINE LEARNING TECHNIQUES

Marine weather forecasting has raised sizeable awareness in numerous ocean related domains. With immeasurable portion of data to handle with, big data problem solving unfolds gateways for abundance of predictions. Machine Learning (ML) is an essential algorithm for big data prediction. However with the increasing size and nature of data i.e., Big Data, Marine Weather Forecasting with Big Data with minimum time, error and maximum accuracy is of major concern to be addressed. In this work, a method called, Perceptred-based Feature and Kriging Gradient Boost Classification (PF-KGBC) is introduced with big data with the objective of improving the prediction performance marine weather with high accuracy and less time consumption. The PF-KGBC method is split into two parts. They are feature selection using perceptron classifier model and classification using Kriging Ensemble extreme Gradient Boost for marine weather forecasting. With the assistance of supervised learning algorithm based on perceptron classifier that involves a functional input represented by vector of numbers belongs to particular class. Based on linear predictor function set of weights are integrated with feature vector to make essential marine weather feature vector. After feature selection process, Kriging Ensemble extreme Gradient Boost Classification is performed with the purpose of forecasting marine weather data. Here, the weak learner is combined to form strong classifier. Kriging regression estimates the dependent data variation when any of factors or values in independent data gets changed. With this accurate marine weather prediction with high accuracy and lesser time consumption is ensured. Meanwhile, we analyzed the results of the proposed PF- KGBC method compared with other conventional methods, and the running performance on the Java platform. The results show that the proposed method achieved satisfactory prediction results and improvements were observed in terms of accuracy, time and error rate considerably. Keywords: Machine Learning, Perceptron, Feature Selection, Kriging, Gradient Boost, Classification, Ensemble

Automatic Detection of Java Cryptographic API Misuses: Are We There Yet?

The Java platform provides various cryptographic APIs to facilitate secure coding. However, correctly using these APIs is challenging for developers who lack cybersecurity training. Prior work shows that many developers misused APIs and consequently introduced vulnerabilities into their software. To eliminate such vulnerabilities, people created tools to detect and/or fix cryptographic API misuses. However, it is still unknown (1) how current tools are designed to detect cryptographic API misuses, (2) how effectively the tools work to locate API misuses, and (3) how developers perceive the usefulness of tools’ outputs. For this paper, we conducted an empirical study to investigate the research questions mentioned above. Specifically, we first conducted a literature survey on existing tools and compared their approach design from different angles. Then we applied six of the tools to three popularly used benchmarks to measure tools’ effectiveness of API-misuse detection. Next, we applied the tools to 200 Apache projects and sent 57 vulnerability reports to developers for their feedback. Our study revealed interesting phenomena. For instance, none of the six tools was found universally better than the others; however, CogniCrypt, CogniGuard, and Xanitizer outperformed SonarQube. More developers rejected tools’ reports than those who accepted reports (30 versus 9) due to their concerns on tools’ capabilities, the correctness of suggested fixes, and the exploitability of reported issues. This study reveals a significant gap between the state-of-the-art tools and developers’ expectations; it sheds light on future research in vulnerability detection.

Blockchain based Securing Medical Records in Big Data Analytics

The patient privacy is danger while medical records and data are transmitted or share beyond secure big data. This is because violations push them to the margins and they begin to avoid fully revealing their stages. This kind of stages contains negative impact in scientific investigate. To overcome this issue, Secure Block Chain System for Managing and Sharing Electronic Medical Records in Big Data Field is proposed. In this manuscript, a Cryptographic Hash Generator (CHG) technique based Secured and Trusted Data storage and transmission using Block Chain (BC) in Hadoop Distributed File System (HDFS). Initially, the Big data collected from the health care center is partitioned into sensitive and insensitive data. Block chain system utilizes an asymmetric cryptography for validating transactions authentication. Here, the user key is created through secured bitwise cryptographic hash generator (CHG) while there is required to fetch the newly record for usage. In block chain system, when a user seeking data from a healthcare application have forward a request to CHG. The message is send back to the user with a secret key for confirmation. The key can be decrypted or even denied access if only a valid user allows the user to link to this cluster. Only sensitive data were selected to the process of encryption for the process of encryption, this CHG technique employs the Discrete Shearlet Transform (DST) for encrypting the data, and the data’s are warehoused in the block chain to upgrade the level of security. And the insensitive data are put directly on the Hadoop Distributed File System. During the verification process, CHG is utilized for creating the request forward through the user. The operator creates the purpose of remote key to create the block (request) and signing the request using transaction private key, then forward to request queuing. To validate a request, the request from the queue is supplied first and an Improved Grey wolf Optimization algorithm (IGWO) is utilized to determine the optimal request that is fetch through the consensus node for initiating the process of validation. After accepting the user’s request, access is given to the user associated with input or requested data, then the verified request is set to broadcast. The proposed method is executed in JAVA and Hadoop platform. Experimental results show that the proposed BC-CHG-DST-IGWOA shows better performances of higher Efficiency 20.14%, 31.25%, 24.33%, 14.69%, lower time 16.12%, 15.09%, 21.36%, 46.26% compared with the existing methods, such as medical records managing and securing blockchain based system supported by genetic algorithm with discrete wavelet transform (BC-SMR-BD-GA-DWT), DQN-based optimization framework to secure shared blockchain systems (BC-SMR-BD-DQNSB), Hyper ledger blockchain enabled secure medical record management along deep learning-based diagnosis model (BC-SMR-BD-HBESDM-DLD), Secure attribute-based signature scheme with multiple authorities for blockchain in electronic health records system (BC-SMR-BD-MA-ABS) respectively.

Manta ray optimized deep contextualized bi-directional long short-term memory based adaptive galactic swarm optimization for complex question answering

Complex question answering (CQA) is used for human knowledge answering and community questions answering. CQA system is essential to overcome the complexities present in the question answering system. The existing techniques ignores the queries structure and resulting a significant number of noisy queries. The complex queries, distributed knowledge, composite approaches, templates, and ambiguity are the common challenges faced by the CQA. To solve these issues, this paper presents a new manta ray foraging optimized deep contextualized bidirectional long-short term memory based adaptive galactic swarm optimization (MDCBiLSTMAGSO) for CQA. At first, the given input question is preprocessed and the similarity assessment is performed to eliminate the misclassification. Afterwards, the attained keywords are mapped into applicant results to improve the answer selection. Next, a new similarity approach named InfoSelectivity is introduced for semantic similarity evaluation based on the closeness among elements. Then, the relevant answers are classified through the MDCBiLSTM and optimized by a new manta ray foraging optimization (MRFO). Finally, adaptive galactic swarm optimization (AGSO) resultant is the best output. The proposed scheme is implemented on the JAVA platform and the outputs of designed approach achieved the better results when compared with the existing approaches in average accuracy (98.2%).