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Objective: To determine the effect of Cochlear Implants on electrophysiological and behavioural response in children with hearing impairment at frequencies of 500, 1000, 2000, and 4000 Hz. Study Design: Cross sectional study. Place and Duration of Study: Department of ENT, Combined Military Hospital Rawalpindi Pakistan, from Jul 2021 to Jul 2022. Methodology: Seventy children with hearing impairment aged 2-12 years of either gender, who had undergone cochlear implantation were part of the study. Language and hearing conditions were examined during the audiological assessment of candidates for cochlear implantation. At ER-tone 5A and TDH-39 using ISO 389-2 and 389-1 calibrations, warble tones given at different frequencies were used to derive behavioral air conduction thresholds. The Auditory Steady State Response test began with a carrier frequency of 500 Hz and advanced to frequencies of 1000, 2000, and 4000 Hz. Thresholds were set using a 10 dB down and 5 dB up procedure until no responses could be captured. Behavioral and Auditory Steady State Response responses were noted in all patients. Results: Mean age of the patients was 6.66±2.74 years. Behavioral threshold>110 dB HL were obtained in 8(11.4%) subjects, 10(14.3%) had behavioral thresholds from 100 to 110 dB HL, and 52(74.3%) had 110 dB HL (15.7%), 31(44%) achieved 100 to 110 dB HL and 28(40%) achieved

Increasing interest in utilizing bio-lubricants has been sparked by pollution caused by the mineral oil-based lubricants and their depletion. This study aimed to assess the effectiveness of Jatropha oil based grease as a basis and sodium thickener as an additive. The grease's lubricating abilities were tested using the ASTM 2266 four ball test and molybdenum disulphide as an additive. According to the findings, adding molybdenum disulphide particles decreased wear and the coefficient of friction, with the latter falling by 69% when compared to virgin Jatropha grease. Given that Jatropha grease passed the four ball test, a simulation of real-world conditions, this indicates that it is a reliable lubricant. Wear examinations were performed on steel balls using an optical microscope and scanning electron microscopy, and the results were consistent. It was found that addition of 0.25% MoS2 to virgin Jatropha grease reduced coefficient of friction to 0.12 (69%) from 0.39. Similarly wear scar diameter reduced from 800.583 µ to 788.44 µ in presence of 0.25% MoS2 blended grease. The study concludes that the grease prepared could an alternative to grease made from mineral oils. The focus on adopting Jatropha grease as a more sustainable and bio-degradable option can preserve the environment.

Abstract Aquaporins (AQPs) are ubiquitous channel proteins that play a critical role in the homeostasis of the cellular environment by allowing the transit of water, chemicals, and ions. They can be found in many different types of cells and organs, including the lungs, eyes, brain, glands, and blood vessels. By controlling the osmotic water flux in processes like cell growth, energy metabolism, migration, adhesion, and proliferation, AQPs are capable of exerting their regulatory influence over a wide range of cellular processes. Tumour cells of varying sources express AQPs significantly, especially in malignant tumours with a high propensity for metastasis. New insights into the roles of AQPs in cell migration and proliferation reinforce the notion that AQPs are crucial players in tumour biology. AQPs have recently been shown to be a powerful tool in the fight against pathogenic antibodies and metastatic cell migration, despite the fact that the molecular processes of aquaporins in pathology are not entirely established. In this review, we shall discuss the several ways in which AQPs are expressed in the body, the unique roles they play in tumorigenesis, and the novel therapeutic approaches that could be adopted to treat carcinoma.

This paper introduces Matrix-Valued Neural Coordinated Federated Deep Extreme Machine Learning, a novel approach for enhancing health prediction and intrusion detection on the Internet of Healthcare Things (IoHT). By leveraging Machine Learning (ML), blockchain, and Intrusion Detection Systems (IDS), this technique ensures the security of medical data while enabling predictive health analytics. The IoHT, characterized by its vast network of interconnected devices, poses significant challenges in security and confidentiality. However, the integration of proposed technique empowers healthcare systems to proactively address these concerns while enhancing patient outcomes and reducing healthcare costs. Smart healthcare, enabled by ML and blockchain, is revolutionizing healthcare 5.0. Healthcare systems may employ IoHTs’ intelligent and interactive characteristics using proposed approach. Despite its benefits, medical data aggregation poses security, ownership, and regulatory compliance challenges. Federated Learning (FL) is a key technique for distributed learning that protects data. The proposed architecture has several unique benefits like 1) it provides a thorough examination of the incorporation of blockchain technology with FL for healthcare 5.0; 2) it takes the lead in creating a robust healthcare monitoring system that utilizes blockchain technology and IDS to identify and prevent harmful actions; 3) the development of crucial blockchain elements by means of mutual neuronal synchronization of Artificial Neural Networks (ANNs) showcases pioneering progress in safeguarding medical data; and 4) the framework underwent a thorough empirical assessment and outperformed existing methods in accurately predicting intrusion detection and disease prediction, achieving an impressive efficiency rate of 97.75% and 98% respectively. This development represents a major step forward in improving security and predictive abilities within the IoHT ecosystem, offering the potential for revolutionary advancements in healthcare delivery and patient care.

The adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on COOT bird optimization was developed for selecting the optimal parameters of ANFIS in the current study. This method combines COOT optimization with ANFIS to model the quality of service (QoS) characteristics of web services by using the adaptive neuro-fuzzy inference system COOT (ANFIS-COOT). In this instance, the quality of the web service (QWS) dataset was obtained from the GitHub database, which consists of 120 web services data, and then evaluated using the presented model on the dataset for estimating response time and throughput of web services. As significant evidence of ANFIS-COOT’s efficiency, the similar QWS data set is analyzed using four different prediction models: ANFIS, ANFIS-Beetle Antennae Search (ANFIS-BAS), ANFIS-Reptile Search Algorithm (ANFIS-RSA), and ANFIS-Snake Optimizer (ANFIS-SO). Moreover, the exploratory study used statistical benchmarks such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient ( $R^{2}$ ) to emphasize the accuracy of the proposed model. Based on analysis results, the presented model achieved optimal values of RMSE (59.7473), MAE (15.8531), MAPE (0.0705), and $R^{2}$ of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and $R^{2}$ of 97.12 % for modelling response time and throughput of web services, compared to other models. Eventually, this report demonstrates the viability of the ANFIS-COOT while tackling a complex problem and improving predictive performance.

The increasing prevalence of antibiotic resistance, driven by the production of extended-spectrum beta-lactamases (ESBLs), presents a critical challenge to current medical treatments, particularly in clinical settings. Understanding the distribution and frequency of ESBL-producing bacteria is essential for developing effective control strategies. This study investigated the antibiotic resistance and extended-spectrum beta-lactamase (ESBL) production in bacterial isolates in clinical and non-clinical (food) specimens in Tabuk, KSA. A total of 57 bacterial isolates were analysed, with E. coli and Pseudomonas sp. being the most prevalent. High resistance rates were observed, particularly against third-generation cephalosporins in clinical isolates. ESBL screening revealed a significant prevalence in clinical samples (58.3%), with E. coli showing the highest positivity. Conversely, only a low percentage of food isolates were ESBL positive. Molecular analysis confirmed the presence of various ESBL genes, with blaCTX-M being the most frequent, predominantly found in clinical isolates. This study highlights the concerning levels of antibiotic resistance and ESBL production in the region, emphasising the need for effective infection control measures and prudent antibiotic use.

The advent of nanotechnology has been instrumental in the development of new drugs with novel targets. Recently, metallic nanoparticles have emerged as potential candidates to combat the threat of drug-resistant infections. Diabetic foot ulcers (DFUs) are one of the dreadful complications of diabetes mellitus due to the colonization of numerous drug-resistant pathogenic microbes leading to biofilm formation. Biofilms are difficult to treat due to limited penetration and non-specificity of drugs. Therefore, in the current investigation, SnO2 nanoparticles were biosynthesized using Artemisia vulgaris (AvTO-NPs) as a stabilizing agent and were characterized using ultraviolet–visible (UV–vis) spectroscopy, Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDX). Furthermore, the efficacy of AvTO-NPs against biofilms and virulence factors of drug-resistant Candida albicans strains isolated from DFUs was assessed. AvTO-NPs displayed minimum inhibitory concentrations (MICs) ranging from 1 mg/mL to 2 mg/mL against four strains of C. albicans. AvTO-NPs significantly inhibited biofilm formation by 54.8%–87%, germ tube formation by 72%–90%, cell surface hydrophobicity by 68.2%–82.8%, and exopolysaccharide (EPS) production by 69%–86.3% in the test strains at respective 1/2xMIC. Biosynthesized NPs were effective in disrupting established mature biofilms of test strains significantly. Elevated levels of reactive oxygen species (ROS) generation in the AvTO-NPs-treated C. albicans could be the possible cause of cell death leading to biofilm inhibition. The useful insights of the present study could be exploited in the current line of treatment to mitigate the threat of biofilm-related persistent DFUs and expedite wound healing.

The proliferation of Internet of Things (IoT) solutions has led to a significant increase in cyber-attacks targeting IoT networks. Securing networks and especially wireless IoT networks against these attacks has become a crucial but challenging task for organizations. Therefore, ensuring the security of wireless IoT networks is of the utmost importance in today’s world. Among various solutions for detecting intruders, there is a growing demand for more effective techniques. This paper introduces a network intrusion detection system (NIDS) based on a deep neural network that utilizes network data features selected through the bagging and boosting methods. The presented NIDS implements both binary and multiclass attack detection models and was evaluated using the KDDCUP 99 and CICDDoS datasets. The experimental results demonstrated that the presented NIDS achieved an impressive accuracy rate of 99.4% while using a minimal number of features. This high level of accuracy makes the presented IDS a valuable tool.

IT services and resources on-demand through Internetwork are offered by Cloud Computing (CC), including the pay-for-you-go aspect. A lot is offered by the CC paradigm, such as Infrastructure related services, computing services, storage, and environments for deployment are also provided. The objective of this study is to survey one of the significant challenges in cloud computing, which is a multi-variant, multi-constraint issue termed Load unbalancing, resulting in the demising of the scalability, efficiency, and performance of the system. Equilibrium in the server workload distribution is still strived for by cloud service providers. The unbalancing issue is resolved by load balancing solutions in two ways: overloading and underloading. An extensive structural literature analysis of Load balancing and its constituent domains with the inclusion of various parameters, such as scalability, make-span, and throughput, are depicted in this research paper to enhance the QoS. A detailed and organized taxonomy of all the Load balancing algorithms based on nature system state, techniques, functionality, and types is also presented. The major focus of the survey is around the Static, dynamic, hybrid, and nature-inspired Load-Balancing algorithms.

Plasmacytoma is a monoclonal plasma B cell malignancy originating in either bone named as solitary plasmacytoma of the bone or in soft tissue labeled as extramedullary plasmacytoma (EMP). Solitary EMP is rare accounting for

5G is a key enabler of Industrial Internet of Things (IIoT) that provides seamless connectivity between machines, sensors and computing servers. Security and privacy are major concerns for 5G enabled IIoT. Physical Layer Security (PLS) is a promising technique that can enhance the security of 5G enabled IIoT. In this paper, we present an IRS-assisted PLS scheme for 5G enabled IIoT that improves the weighted secrecy sum-rate (WSSR) of the industrial wireless network, where eavesdroppers are present around the facility. The key idea is to jointly optimize active and passive beamforming vectors to increase the secrecy rate at the user. To maximize WSSR, we use the stable matching algorithm that optimally assigns IRSs for secure data sharing between industrial units. Simulation results show that the proposed scheme enhances the WSSR performance by 40% and minimum secrecy rate by 25% as compared to the random and maximum weight matching schemes, respectively.

Automatic segmentation of erythrocytes in microscopic blood smear phone images is a critical step to visualize and identify malaria using machine learning technologies. However, it still remains a challenging problem due to the scarcity of experts, low image qualities, slow manual and inefficient quality of diagnosis. To handle these issues to some extent, we proposed an effective multi-magnification deep residual neural network (MM-ResNet), where we fully automatically classify the microscopic blood smear images as either infected/ non-infected at multiple magnifications. We have experimentally evaluated our approach by using it to train more efficient variants of different compact deep convolutional neural networks (CNN), evaluated on phone datasets. The MM-ResNet end-to-end framework shows similar or superior accuracy than the baseline architectures, as measured by GPU timings on the publicly available microscopic blood smear phone images. This approach is the first application of a MM-ResNet for malaria-infected erythrocyte identification in microscopic blood smear images.

Source code is the heart of the software systems; it holds a wealth of knowledge that can be tapped for intelligent software systems and leverage the possibilities of reuse of the software. In this work, exploration revolves around making use of the pattern hidden in various software development processes and artifacts. This module is part of the smart requirements management system that is intended to be built. This system will have multiple modules to make the software requirements management phase more secure from vulnerabilities. Some of the critical challenges bothering the software development community are discussed. The background of Machine Learning approaches and their application in software development practices are explored. Some of the work done around modeling the source code and approaches used for vulnerabilities understanding in software systems are reviewed. Program representation is explored to understand some of the principles that would help in understanding the subject well. Further deeper dive into source code modeling possibilities are explored. Machine learning best practices are explored inline with the software source code modeling.

It has been proven that Internet of Things (IoT) platforms can improve the performance and efficiency of a wide range of processes. With the acceptance of IoT as a major part of the technology of Industry 4.0, the notion of leveraging the Internet in industries to enable automation and reconfigure existing industrial processes has greatly evolved. By introducing smart technology and intelligent processes, the Industrial Internet of Things (IIoT) is committed to bringing high operational efficiency, enhanced productivity, and effective management to industrial assets. Despite this, the reliance of IIoT on central architecture presents numerous challenges, including the security and maintenance of smart devices, privacy issues owing to third-party participation, and massive computations conducted by a central entity, all of which prevent its widespread adoption in businesses. Emerging blockchain technologies have the potential to transform IIoT platforms and applications. A distributed and decentralized approach followed by blockchain might offer interesting solutions to the challenges raised by IIoT. Furthermore, 5G networks are expected to deliver excellent solutions to meet the demands of decentralized systems, with a focus on application-specific vulnerabilities. Blockchain and IIoT, enabled by 5G, is a viable option to fully explore the potential of contemporary industry. In this context, this article analyzes and examines recent achievements to highlight the major obstacles in blockchain–IIoT convergence and presents a framework for potential solutions. A well-organized literature review by analyzing the existing work in three primary areas: blockchain consensus algorithms used in existing IoT and IIoT applications, blockchain for 5G-enabled IoT networks, and blockchain in industry have been performed, with major findings summarized in each area. Directions for the future are also provided and intend to assist researchers in understanding the full potential of these innovations.

In the twenty-first century, storing and managing digital documents has become commonplace for all corporate and public sectors around the world. Physical documents are scanned in batches and stored in a digital archive as a heterogeneous document stream, referred to as a digital package. To make Robotic Process Automation (RPA) easier, it’s necessary to automatically segment the document stream into a subset of independent, coherent multi-page documents by detecting the appropriate document boundary. It’s a common requirement of a TI company’s Automated Document Management Systems (ADMS), where business operations are automated using RPA and the goal is to extract information from digital documents with minimal user intervention. The current study proposes, evaluates, and compares a multi-modal binary classification network incorporating text and picture aspects of digital document pages to state-of-the-art baseline methodologies. Image and textual features are extracted simultaneously from the input document image by passing them through Visual Geometry Group 16 - Convolutional Neural Network (VGG16-CNN) and pre-trained Bidirectional Encoder Representations from Transformers (Legal-BERT ${}_{base}$ ) model through transfer learning respectively. Both features are finally fused and passed through a fully connected layer of Multi Layered Perceptron (MLP) to obtain the binary classification of the pages as the First Page (FP) and Other Page (OP). Real-time document image streams from production business process archive were obtained from a reputed Title Insurance (TI) company for the study. The obtained $F_{1}$ score of 97.37% and 97.15% are significantly higher than the accuracies of the considered two baseline models and well above the expected Straight Through Pass (STP) threshold defined by the process admin.

Tremor is an involuntary action and a common problem for elderly people and other age groups to perform daily activities. To assist those people in conducting daily activities, suppressing hand tremors is necessary. A flexure mechanism system can be placed as a tabletop device on which the elbow can be positioned to compensate for the tremor. Using this methodology, in this paper, flexure-based mechanisms for the hand tremor are proposed to help people perform eating activities easily. Flexure mechanisms with three different connection arrangements are designed, modeled (as pseudo-rigid bodies), analyzed, and developed. Finite element analysis is conducted to verify the results of the pseudo-rigid models for passive conditions. An experimental setup is accessed for both passive and active schemes, consisting of two link mechanisms, having a precise one-DOF tremor generated at the end-effector of the experimental setup arm. Gyroscope sensors are used to monitor the tremor present. A series of experiments are conducted for two cases: when flexure mechanisms are connected passively and when flexure mechanism is connected actively. It was found that the flexure mechanism can compensate for the tremor in passive and active mode by approximately 75% to 90%, respectively.

Kaposi’s sarcoma associated herpesvirus (KSHV) is causative agent of Kaposi’s sarcoma, Multicentric Castleman Disease and Pleural effusion lymphoma. KSHV-encoded ORF17 encodes a protease which cleaves -Ala-Ala-, -Ala-Ser- or -Ala-Thr-bonds. The protease plays an important role in assembly and maturation of new infective virions. In the present study, we investigated expression pattern of KSHV-encoded protease during physiologically allowed as well as chemically induced reactivation condition. The results showed a direct and proportionate relationship between ORF17 expression with reactivation time. We employed virtual screening on a large database of natural products to identify an inhibitor of ORF17 for its plausible targeting and restricting Kaposi’s sarcoma associated herpesvirus assembly/maturation. A library of 307,814 compounds of biological origin (A total 481,799 structures) has been used as a screen library. 1-oleoyl-2-hydroxy-sn-glycero-3-phospho-(1′-myo-inositol) was highly effective against ORF17 in in-vitro experiments. The screened compound was tested for the cytotoxic effect and potential for inhibiting Kaposi’s sarcoma associated herpesvirus production upon induced reactivation by hypoxia, TPA and butyric acid. Treatment of reactivated KSHV-positive cells with 1-oleoyl-2-hydroxy-sn-glycero-3-phospho-(1′-myo-inositol) resulted in significant reduction in the production of Kaposi’s sarcoma associated herpesvirus. The study identified a lysophosphatidic acid molecule for alternate strategy to inhibit KSHV-encoded protease and target Kaposi’s sarcoma associated herpesvirus associated malignancies.

Background: Red marine algae have shown the potential to reduce inflammation, influence microbiota, and provide neuroprotection. Objective: To examine the prebiotic properties of Palmaria palmata aqueous extract (Palmaria p.) and its potential as a neuroprotective agent in multiple sclerosis (MS). Methods: eighty-eight adult Swiss mice were divided into four male and four female groups, including a control group (distilled water), Palmaria p.-treated group (600 mg/kg b.w.), cuprizone (CPZ)-treated group (mixed chow 0.2%), and a group treated with both CPZ and Palmaria p. The experiment continued for seven weeks. CPZ treatment terminated at the end of the 5th week, with half of the mice sacrificed to assess the demyelination stage. To examine the spontaneous recovery, the rest of the mice continued until the end of week seven. Behavioral (grip strength (GS) and open field tests (OFT)), microbiome, and histological assessments for general morphology of corpus callous (CC) were all conducted at the end of week five and week 7. Results: Palmaria p. can potentially protect against CPZ-induced MS with variable degrees in male and female Swiss mice. This protection was demonstrated through three key findings: (1) increased F/B ratio and expansion of the beneficial Lactobacillus, Proteobacteria, and Bactriodia communities. (2) Protection against the decline in GS induced by CPZ and prevented CPZ-induced anxiety in OFT. (3) Preservation of structural integrity. Conclusions: Because of its propensity to promote microbiota alterations, its antioxidant activity, and its content of −3 fatty acids, Palmaria p. could be a promising option for MS patients and could be beneficial as a potential probiotic for the at-risk groups as a preventive measure against MS.

Biofilms are complex communities of microorganisms that grow on surfaces and are embedded in a matrix of extracellular polymeric substances. These are prevalent in various natural and man-made environments, ranging from industrial settings to medical devices, where they can have both positive and negative impacts. This review explores the diverse applications of microbial biofilms, their clinical consequences, and alternative therapies targeting these resilient structures. We have discussed beneficial applications of microbial biofilms, including their role in wastewater treatment, bioremediation, food industries, agriculture, and biotechnology. Additionally, we have highlighted the mechanisms of biofilm formation and clinical consequences of biofilms in the context of human health. We have also focused on the association of biofilms with antibiotic resistance, chronic infections, and medical device-related infections. To overcome these challenges, alternative therapeutic strategies are explored. The review examines the potential of various antimicrobial agents, such as antimicrobial peptides, quorum-sensing inhibitors, phytoextracts, and nanoparticles, in targeting biofilms. Furthermore, we highlight the future directions for research in this area and the potential of phytotherapy for the prevention and treatment of biofilm-related infections in clinical settings.

Abstract Background Obstetrical outcomes in assisted reproduction techniques (ART) were compared with naturally conceived pregnancies and among each other in multiple reports. However, many important changes in the practice of in vitro fertilization (IVF) over the years, including single embryo transfers (sET) and the introduction of modified natural IVF (mnIVF), and the advances in the frozen embryo transfer (FET) might have impacted the outcomes. Our study is the first to our knowledge to assess four different groups, including spontaneous pregnancies, mnIVF, stimulated IVF (sIVF), and FET altogether in a head-to-head comparison. This is a retrospective study on perinatal outcomes of singleton babies conceived naturally or using three different ART protocols between 2011 and 2014. The primary objective was the comparison of gestational age and birth weight between spontaneously conceived pregnancies (NAT, n= 15,770), mnIVF (n=235), sIVF (n=389), and FET (n=222). Results Our results show a significant difference in favor of naturally conceived pregnancies over ART in term of gestational age. In fact, the gestational age of babies in the NAT group was statistically higher compared to each one of the ART groups alone. Regarding the birth weight, the mean was significantly higher in the FET group compared to the other categories. Conclusion Differences in perinatal outcomes are still found among babies born after different modes of conception. However, there is still need for well-designed high-quality trials assessing perinatal outcomes between naturally conceived pregnancies and different ART protocols based on different maternal and treatment characteristics.

In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.

The advantages of IoT are incontrovertible; however, significant risks and uncertainties over the protection of best practices and their related costs prevent many companies from adopting the technology. Also, users are aware of the consequences of breaches of IoT protection. Recent research indicates that 90% of consumers lack confidence in the safety of IoT devices. IoT defense has been described as one of the weakest areas. In this respect, significant efforts have been made, often through traditional cryptographic methods, to overcome security and privacy issues in IoT systems. The list of security issues (confidentiality, integrity, authentication, and availability) must be addressed to secure IoT devices from attack. IoT nodes’ specific features do not adequately protect the whole security spectrum of IoT networks through existing solutions. This paper aims to provide security to the IoT devices using nature-inspired Gravitational Search-guided artificial neural key. In this paper, artificial neural synchronization is used to create a neural key exchange protocol between two IoT devices over a public channel for cryptographic purposes. This proposed technique has many benefits, such as 1) it provides an optimized design of the neural network that facilitates the development and establishment of a neural key between the two approved IoT devices. 2) It offers the sharing of the private neural key over the public channel via artificial neural synchronization 3)Three hidden layers of the neural network contribute to the deep internal design. So, it’s going to be hard for the intruder to infer the internal layout. 4) The increase in the weight spectrum of the neural network raises the complexity of an effective attack exponentially, but the effort to create a neural key decreases over polynomial time. Various parametric experiments have been conducted out on the proposed methodology. The simulations of the procedure demonstrate efficacy in terms of the findings cited in the paper.

Blind-spots, where wireless signals do not reach within the coverage range, often emerge in a dynamic environment due to obstacles, geographical location or mobility of cellular users (CUs). Thus greatly reducing the overall system performance in terms of coverage and throughput. Relay-aided cognitive Device to Device (cD2D) communication system underlying the 5G cellular network can help mitigate blind-spots. Cognitive capability helps D2D users to acquire the spectrum opportunistically for proximity communication and establish a semi-independent network underlying the 5G network, which not only offloads 5G-New Radio (NR) base station but also enhances the overall system performance. In this work, we have developed a relay-aided cognitive D2D network that helps CUs falling into the blind-spots to retain access to the 5G network and increase wireless coverage. Relay selection requires mutual consent between the relay and the device in the blind-spot. The in-coverage devices are tempted to act as relays through incentive-based mechanism. For enhanced system performance a suitable match among the devices in blind-spots and the relays is required. cD2D enabled relay selection algorithm (cDERSA) is proposed in this work, in which a cognitive D2D user (cDU), which is a CU falling in the blind-spot, establishes a relayed cD2D link to access 5G-NR gNodeB. All cDUs as well as the tempted relays, i.e. cognitive D2D relays (cDRs), first scan their surroundings for devices capable of D2D communication and based on multi-criteria objective functions, build a priority table. A stable marriage problem is formulated and solved using a unique, stable, distributed, and efficient matching algorithm based on the Gale-Shapley algorithm. A new incentive mechanism is also developed to keep relays motivated to share their resources. Simulation is performed and their results show improvement in throughput and average user satisfaction, which validates our proposed cDERSA.

The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications, all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure, software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services.

This paper presents an in-depth analysis and investigation on the performance of static photovoltaic (PV) array configurations subjected to various partial shading conditions (PSCs). Under PSCs, the electrical characteristics of the PV modules are critically monitored and reasons for their behavioral changes are highlighted. By doing so, this study aims to improve the efficiency of PV systems by minimizing mismatch losses and determining the optimum array configuration which is characterized by the highest maximum power and lowest relative losses under PSCs. Besides, this study complements and carries forward the previous studies through the detailed analysis of each configuration subjected to various practically probable PSCs. Three different PV array sizes ( $5\times4$ , $5\times5$ , and $3\times10$ ) are used to analyze the results and performance under considered shading scenarios. MATLAB/Simulink platform is used to model and simulate the PV array using the single diode (5-parameters) model. In-depth analysis of current flow across cross-ties and bypass diodes activation shows that the diagonal shading pattern leads to lower power loss (PL). Besides, the Total Cross-Tied (TCT) configuration demonstrates superior performance under most of the PSCs compared to other configurations. These results provide valuable information about the performance of PV array which may lead to better estimation and prediction of global maximum power (GMP) generation of a PV system.

Increasing prevalence and simplicity of using Artificial Intelligence (AI) techniques, Steganography is shifting from conventional model building to AI model building. AI enables computers to learn from their mistakes, adapt to emerging inputs, and carry out human-like activities. Traditional Linguistic Steganographic approaches lack automation, analysis of Cover text and hidden text volume and accuracy. A formal methodology is used in only a few Steganographic approaches. In the vast majority of situations, traditional approaches fail to survive third-party vulnerability. This study looks at evaluation of an AI-based statistical language model for text Steganography. Since the advent of Natural Language Processing (NLP) into the research field, linguistic Steganography has superseded other types of Steganography. This paper proposes the positive aspects of NLP-based Markov chain model for an auto-generative cover text. The embedding rate, volume, and other attributes of Recurrent Neural Networks (RNN) Steganographic schemes are contrasted in this article between RNN-Stega and RNN-generated Lyrics, two RNN methods. Here the RNN model follows Long Short Term Memory (LSTM) neural network. The paper also includes a case study on Artificial Intelligence and Information Security, which discusses history, applications, AI challenges, and how AI can help with security threats and vulnerabilities. The final portion is dedicated to the study’s shortcomings, which may be the subject of future research.

Cellular V2X (C-V2X) is an emerging standard that will enable the employment of the 6G Internet of Vehicles (IoV). It is envisaged to provide seamless connectivity, long-range communications, higher data rate transmissions, and centralized and ad hoc modes of communications for vehicular applications. In particular, service applications that require data transmission between vehicles and infrastructure stations known as Road Side Units (RSUs) can greatly benefit from C-V2X. In this paper, we propose a Network-Assisted Service Message Transmission (NA-SMT) protocol that uses Uu and PC5 links of C-V2X to efficiently disseminate service messages from RSUs to the vehicles. In the first phase of the NA-SMT protocol, RSUs select downlink relay vehicles for each service message with the highest Channel Quality Indicator ( $CQI$ ) within a defined reliable range of the destination vehicle. The second phase of the NA-SMT protocol schedules V2V transmission by cooperative sub-channel allocation by the RSUs to reliably disseminate service messages to the destination. Simulation results show that NA-SMT protocol improves packet reception ratio and end-to-end delay of service messages at different channel conditions and service message generation rates.

Steganography or data hiding is used to protect the privacy of information in the transit; it has been observed that the information that flows through Online Social Networks (OSN) is very much unsafe. Therefore, people hesitate to communicate their sensitive data on social media.. Most of the information on the online social network is not useful to users and appears to disregard such details. People’s actions provided a possibility for digital Steganography through the Internet.. TCPIP covert channels were used for steganography until the last decade. People began to utilize social media as a covert conduit to communicate hidden messages to targeted users as social media grew in popularity. There are numerous Online Social Networks accessible nowadays, ranging from Facebook to the more contemporary Twitter and Instagram. All of them may be utilized as covert channels without the general public noticing. The primary characteristic of steganography is the protection of information privacy; nonetheless, it has been utilized more for illicit message transmission, which is a source of concern. To make matters worse, adversaries are using steganalysis techniques to mess with the concealed data. In this article, we examine the different social media steganography techniques, such as those used on Facebook, WhatsApp, and Twitter, as well as the difficulties that these approaches raise. The positive and negative consequences of social media, as well as its current state, are discussed in this study. This paper discusses how the performances of Steganography methods may be assessed using the Entropy value of the Stego object. A look of the three features of steganography. It has been given with undetectability, robustness, and payload capacity. Finally, the paper’s concept’s future scope is explored.

As Blockchain innovation picks up popularity in many areas, it is frequently hailed as a sound innovation. Because of the decentralization and encryption, many imagine that data put away in a Blockchain is and will consistently be protected. Among various abstraction layers of Blockchain architecture, the consensus layer is the core component behind the performance and security measures of the Blockchain network. Consensus mechanisms are a critical component of a Blockchain system’s long-term stability. Consensus forms the core of blockchain technology. Therefore, a range of consensus protocols has been introduced to maximize Blockchain systems’ efficiency and meet application domains’ individual needs. This research paper describes the layered architecture of Blockchain. A comprehensive review of mainstream consensus protocols mainly Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Activity (PoA) is presented in the paper. These mainstream consensus protocols have been explained and detailed performance analysis of these consensus protocols has been done. We have proposed a performance matrix of these consensus protocols based on different parameters like Degree of decentralization, Latency, Fault Tolerance Rate, Scalability, etc. Consensus protocols being the core of a strong fault-tolerant secured blockchain system, the proposed work intends to help inappropriate protocol selection and further research on strengthening trust and ownership in the technology. Depending upon different parameters like decentralization which is low in POA compared to other protocols, whereas POW is non-scalable, so depending on the priority of a particular performance parameter, the paper will help in the selection of a specific protocol.

Purpose:Metallo β-lactamases (MβL) production is a worldwide problem, particularly in gram-negative bacteria. As scanty data is available on the prevalence of MBL, the present study is being undertaken to determine the prevalence, antibacterial sensitivity patterns, and molecular characterization of MβL associated resistant genes in gram-negative bacteria isolated from ocular infections.Material and MethodsAt a tertiary eye care center in south India, 359 gram-negative pathogens, 200 isolates from eye infections, and 159 isolates from normal flora of the eye were studied. A gold standard microbiology method was used to identify the isolates. An antibiotic double disc synergy test and a combination disc test were used to detect MβL production. Multiplex PCR was used to investigate the molecular characteristics of the MβL encoding genes blaVIM, blaIMP, and blaNDM.ResultsOf the 359 gram-negative bacterial pathogens, Pseudomonas aeruginosa 108 (30.1%) and Enterobacter agglomerans 46 (12.8%) were commonly isolated. High prevalence of P. aeruginosa 81% (17 strains) was detected as an MβL producer and it shows 100% resistance to 2nd and 3rd generation cephalosporins and meropenem. Multiplex PCR detected only the blaVIM gene in 56 (28%) of various eye infections and 27 (17%) of normal flora of the gram-negative bacteria (GNB). The blaVIM gene is detected predominantly in 51.8% of keratitis and 21.4% of postoperative endophthalmitis. High prevalence of the gene was detected in P. aeruginosa 42.9% (24 of 56) and Alcaligens denitrificans 10.7% (6 of 56) from eye infections. Whereas, in the control group, P. aeruginosa and E. coli each had 14.8% (4 of 27) that were shown positive.ConclusionThe emerging MβLs mediated resistance among P. aeruginosa is a challenging task for ophthalmologists, especially in patients with endophthalmitis and bacterial keratitis. This local knowledge will aid in advising appropriate antibiotic use and avoiding unnecessary antibiotic prescriptions, which are highly warranted.

Retropharyngeal abscess (RPA) is a rare infective pathology of deep neck spaces affecting young children and adults. In view of scarce literature availability and atypical presentation in infants, often lacking fever or lymphadenopathy, it poses a definitive diagnostic dilemma to the clinicians. It should be kept in mind for all infants presenting with respiratory distress. History should be carefully taken for any upper respiratory tract infections or nasal discharge preceding the illness. Prompt airway control is of paramount importance, since there is a higher prevalence of airway compromise in infants and early surgical intervention is therefore indicated. Usually, intraoral approach is sufficient for incision and drainage. Here, we present a rare case of RPA in a 3-month-old infant in whom early radiological investigations, especially contrast-enhanced computerized tomography/noncontrast head computerized tomography helped not only in timely diagnosis but also in planning of surgery. A multidisciplinary, integrated strategy of pediatrician, neurosurgeon, microbiologist, radiologist, and otorhinolaryngologist is ideal for patient care.

Intelligent Transport Systems (ITS) require accurate information to be shared among vehicles and infrastructure nodes for applications including accident information or pre-crash warnings, to name a few. Due to its sensitive nature, ITS applications are vulnerable against data integrity attacks where nodes transmit false information that results in wrong decision making by the applications. A characteristic of such attacks is that the false transmitted information is significantly different than the actual information. In this paper, we propose an Outlier Detection, Prioritization and Verification (ODPV) protocol that efficiently isolates false data and improves traffic management decisions. ODPV uses the isolation forest algorithm to detect outliers, fuzzy logic to prioritize outliers and C-V2X communications to verify the outliers. Extensive simulation results verify the effectiveness of the proposed protocol to isolate the outliers.

Motivated by the synergistic integration of soft computing paradigms this paper introduces a fully adaptive multiple-input-multiple-output NeuroFuzzy control for multi-type Flexible AC Transmission Systems (FACTS) to damp low frequency oscillations. The novel control strategy integrates the complementary features of locally controllable fuzzy Bspline membership functions and robust wavelet neural networks in NeuroFuzzy structure. The gradient decent based back-propagation mechanism used for parameters update has been optimized using online Adaptive Learning Rates (ALRs). The stability of the proposed algorithm has been ensured by deriving an upper bound on ALRs using Lyapunov stability criteria. The application of this controller to provide damping signals to various FACTS controllers like Static Synchronous Series Compensator (SSSC) and Static Synchronous Compensator (STATCOM) can effectively enhance the dynamic stability of the system. A benchmark multi-machine power system has been used for performance validation of the controller by applying various faults under different loading scenarios. Conventional Lead-Lag and NeuroFuzzy controls have been considered for comparative evaluation using nonlinear time and frequency domain techniques to reveal that the proposed control performs better in different operating regions. Furthermore, the graphical results obtained from time and frequency domain simulations have been quantified numerically using different performance indices and Energy Spectral Density (ESD), respectively. The temporal, spectral and numerical analysis confirms the superior performance of the proposed control scheme.

Recent advances in deep learning, coupled with the onslaught of unlabelled medical data have drawn ever-increasing research interests by discovering multiple levels of distributed representations and solving complex medical related problems. Malaria disease detection in early stage requires an accurate and precise diagnosis in order to achieve successful patient remission. This paper proposes a comprehensive computer-aided diagnosis (CAD) scheme for identifying the presence of malaria parasites in thick blood smear images. The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder. The optimum size of the CAD scheme used in this research is 12500-2500-100-50-2, where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes. Moreover, the 10- fold cross validation reflects that the classification is reliable and is applicable to new patient blood smear images. The proposed CAD scheme has been evaluated using malaria blood smear image data set, achieving a detection accuracy of 89.10%, a sensitivity of 93.90% and specificity of 83.10%. The extensive comparative experiment suggests that the proposed CAD scheme provides richer effectiveness and efficiency for malaria data set compared to other deep learning techniques for better diagnosis decision and management. This work implements a novel approach to fast processing and will be a beneficial tool in disease identification.

Mahalanobis taguchi system (MTS) is a multi-variate statistical method extensively used for feature selection and binary classification problems. The calculation of orthogonal array and signal-to-noise ratio in MTS makes the algorithm complicated when more number of factors are involved in the classification problem. Also the decision is based on the accuracy of normal and abnormal observations of the dataset. In this paper, a multiclass model using Improved Mahalanobis Taguchi System (IMTS) is proposed based on normal observations and Mahalanobis distance for agriculture development. Twenty-six input factors relevant to crop cultivation have been identified and clustered into six main factors for the development of the model. The multiclass model is developed with the consideration of the relative importance of the factors. An objective function is defined for the classification of three crops, namely paddy, sugarcane and groundnut. The classification results are verified against the results obtained from the agriculture experts working in the field. The proposed classifier provides 100% accuracy, recall, precision and 0% error rate when compared with other traditional classifier models.

Duodenal ulcer disease is uncommon in paediatric age group. Its perforation is even rarer. However, it should be kept in mind when examining children with acute abdomen especially if there are signs of shock or possibility of upper gastrointestinal bleed. We report a case of a 6 years old female child, a known case of thalassemia major and taking oral Deferasirox since two years of age. She had atypical presentation as there was no previous history of peptic ulcer disease and she only suffered epigastric pain and vomiting for a week but due to lack of proper diagnosis at a local clinic developed duodenal ulcer perforation, which was ultimately diagnosed at a tertiary care hospital and managed with Graham Patch Closure. Keywords: Paediatric age group, duodenal ulcer, duodenal perforation, epigastric pain, Desferasirox.

The updated affiliation information can be seen in the affiliation part of this correction [...]

Image memorability is a very hard problem in image processing due to its subjective nature. But due to the introduction of Deep Learning and the large availability of data and GPUs, great strides have been made in predicting the memorability of an image. In this paper, we propose a novel deep learning architecture called ResMem-Net that is a hybrid of LSTM and CNN that uses information from the hidden layers of the CNN to compute the memorability score of an image. The intermediate layers are important for predicting the output because they contain information about the intrinsic properties of the image. The proposed architecture automatically learns visual emotions and saliency, shown by the heatmaps generated using the GradRAM technique. We have also used the heatmaps and results to analyze and answer one of the most important questions in image memorability: “What makes an image memorable?”. The model is trained and evaluated using the publicly available Large-scale Image Memorability dataset (LaMem) from MIT. The results show that the model achieves a rank correlation of 0.679 and a mean squared error of 0.011, which is better than the current state-of-the-art models and is close to human consistency (p = 0.68). The proposed architecture also has a significantly low number of parameters compared to the state-of-the-art architecture, making it memory efficient and suitable for production.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.

DataStream mining is a challenging task for researchers because of the change in data distribution during classification, known as concept drift. Drift detection algorithms emphasize detecting the drift. The drift detection algorithm needs to be very sensitive to change in data distribution for detecting the maximum number of drifts in the data stream. But highly sensitive drift detectors lead to higher false-positive drift detections. This paper proposed a Drift Detection-based Adaptive Ensemble classifier for sentiment analysis and opinion mining, which uses these false-positive drift detections to benefit and minimize the negative impact of false-positive drift detection signals. The proposed method creates and adds a new classifier to the ensemble whenever a drift happens. A weighting mechanism is implemented, which provides weights to each classifier in the ensemble. The weight of the classifier decides the contribution of each classifier in the final classification results. The experiments are performed using different classification algorithms, and results are evaluated on the accuracy, precision, recall, and F1-measures. The proposed method is also compared with these state-of-the-art methods, OzaBaggingADWINClassifier, Accuracy Weighted Ensemble, Additive Expert Ensemble, Streaming Random Patches, and Adaptive Random Forest Classifier. The results show that the proposed method handles both true positive and false positive drifts efficiently.

Lactobacillus rhamnosus (LGG) is well documented strain with wide clinical applications. We investigated herein the possible protection role of LGG in against early preneoplastic colon carcinogenesis Aberrant crypt foci (ACF) in rat model. Sprague Dawley rats were pre-treated with or without LGG via oral gavage, followed by ACF induction with Dimethylhydrazine (DMH). LGG treated groups showed significant decrease in total number of ACF’s, and increased Bax, P53, and Caspase-3 proteins expression with significant attenuation of NFkB, Cox2 and β catenin expression compared to DMH group. Serum cytokines array revealed significant elevation of IL-2, IL-6, and IFN-γ, and attenuated level of IL-10 in LGG+DMH treated animals. Finally, the 16S rRNA fecal microbiome genomic analysis showed that LGG alters the gut microbiome structure, composition and functions at phylum, family and genus level. Collectively, LGG reduces colon ACFs incidence and multiplicity via inhibition of pro-carcinogenesis biomarkers and modulate the gut microbiome.

Microtubule affinity regulating kinase 4 (MARK4) regulates the mechanism of microtubules by its ability to phosphorylate the microtubule-associated proteins (MAP’s). MARK4 is known for its major role in tau phosphorylation via phosphorylating Ser262 residue in the KXGS motif, which results in the detachment of tau from microtubule. In lieu of this vital role in tau pathology, a hallmark of Alzheimer’s disease (AD), MARK4 is a druggable target to treat AD and other neurodegenerative disorders (NDs). There is growing evidence that NDs and diabetes are connected with many pieces of literature demonstrating a high risk of developing AD in diabetic patients. Metformin (Mtf) has been a drug in use against type 2 diabetes mellitus (T2DM) for a long time; however, recent studies have established its therapeutic effect in neurodegenerative diseases (NDs), namely AD, Parkinson’s disease (PD) and amnestic mild cognitive impairment. In this study, we have explored the MARK4 inhibitory potential of Mtf, employing in silico and in vitro approaches. Molecular docking demonstrated that Mtf binds to MARK4 with a significant affinity of −6.9 kcal/mol forming interactions with binding pocket’s critical residues. Additionally, molecular dynamics (MD) simulation provided an atomistic insight into the binding of Mtf with MARK4. ATPase assay of MARK4 in the presence of Mtf shows that it inhibits MARK4 with an IC50 = 7.05 µM. The results of the fluorescence binding assay demonstrated significant binding of MARK4 with a binding constant of 0.6 × 106 M−1. The present study provides an additional axis towards the utilization of Mtf as MARK4 inhibitor targeting diabetes with NDs.

Background Consumer electronics or daily use home appliances are the basic necessity of every household. With the adoption of IoT in consumer electronics, this industry is set to rise exponentially. In recent times, the demand for consumer electronics rises amidst the pandemic due to a paradigm shift from in-office culture to work from home. Despite intelligent IoT devices, smart home configuration, and appliances at our disposal, the rudimentary client-server architecture fails to provide facilities like full access control of data and devices, transparency, secured communication, and synchronization between multiple devices, etc. to the users. Methods To overcome these limitations, Blockchain technology has been adopted in recent years, however, it has its own set of limitations in its widespread implementation. Hence, we propose a methodology using the IOTA platform, a distributed ledger technology (DLT) for secured communication between consumer electronics devices and appliances. Results The implementation provides access control, interoperability, data storage, and management with an exploratory insight towards a decentralized micro-payment use-case between Electric cars and charging stations.

Atypical antipsychotic drugs are commonly associated with undesirable side effects including body weight gain (BWG) and metabolic deficits. Many pharmacological interventions have been tested in an attempt to minimize or prevent these side effects. Preliminary evidence suggests that antidiabetic drugs may be effective in attenuating antipsychotic-induced BWG. In the current study, we examined the effect of an antidiabetic drug empagliflozin (EMPA) on BWG induced by anatypical antipsychotic drug olanzapine (Ola) in female and male Wistar rats. Rats were divided into six groups based on the dose they received: group 1 (female control), group 2 (female EMPA, 20 mg/kg; IG), group 3 (female Ola, 4 mg/kg; IP), group 4 (female Ola, 4 mg/kg; IP + EMPA, 20 mg/kg; IG), group 5 (male control), and group 6 (male Ola, 4 mg/kg; IP). Ola induced sustained increase in BWG. The subsequent treatment of Group 3 and 4 with EMPA attenuated the Ola-induced BWG in female Wistar rats. In terms of the gender difference between female and male Wistar rats, the male control group 5 gained more weight throughout the study as compared to the female control group 1. Similarly, the male Ola group 6 gained more weight throughout the study as compared to the female Ola group 3. However, Ola did not cause any weight difference between male rats treated with Ola in comparison with male control group, thus showing a significant gender difference regarding body weight between male and female Wistar rats regardless of Ola administration. In addition, the present findings showed that EMPA effectively attenuates the Ola induced BWG in female Wistar rats. These novel findings should help to better understand the underlying molecular and behavioral mechanisms contributing to the observed increase in body weight after treatment with Ola and other atypical antipsychotic drugs across male and female rats.

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.

Vitamin D deficiency distresses nearly 50% of the population globally and multiple studies have highlighted the association of Vitamin D with a number of clinical manifestations, including musculoskeletal, cardiovascular, cerebrovascular, and neurological disorders. In the current study, vitamin D oil-in-water (O/W) nanoemulsions were developed and incorporated in edible gummies to enhance bioavailability, stability, and patient compliance. The spontaneous emulsification method was employed to produce a nano-emulsion using corn oil with tween 20 and lecithin as emulsifiers. Optimization was carried out using pseudo-ternary phase diagrams and the average particle size and polydispersity index (PDI) of the optimized nanoemulsion were found to be 118.6 ± 4.3 nm and 0.11 ± 0.30, respectively. HPLC stability analysis demonstrated that the nano-emulsion prevented the degradation and it retained more than 97% of active vitamin D over 15 days compared to 94.5% in oil solution. Similar results were obtained over further storage analysis. Vitamin D gummies based on emulsion-based gelled matrices were then developed using gelatin as hydrocolloid and varying quantities of corn oil. Texture analysis revealed that gummies formulated with 10% corn oil had the optimum hardness of 3095.6 ± 201.7 g on the first day which remained consistent on day 45 with similar values of 3594.4 ± 210.6 g. Sensory evaluation by 19 judges using the nine-point hedonic scale highlighted that the taste and overall acceptance of formulated gummies did not change significantly (p > 0.05) over 45 days storage. This study suggested that nanoemulsions consistently prevent the environmental degradation of vitamin D, already known to offer protection in GI by providing sustained intestinal release and enhancing overall bioavailability. Soft chewable matrices were easy to chew and swallow, and they provided greater patient compliance.

Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing. As a result, missing values may occur not just in training data but also in testing data that might contain a single undiagnosed episode or a participant. This study evaluates different imputation and regression procedures identified based on regressor performance and computational expense to fix the issues of missing values in both training and testing datasets. In the context of healthcare, several procedures are introduced for dealing with missing values. However, there is still a discussion concerning which imputation strategies are better in specific cases. This research proposes an ensemble imputation model that is educated to use a combination of simple mean imputation, k-nearest neighbour imputation, and iterative imputation methods, and then leverages them in a manner where the ideal imputation strategy is opted among them based on attribute correlations on missing value features. We introduce a unique Ensemble Strategy for Missing Value to analyse healthcare data with considerable missing values to identify unbiased and accurate prediction statistical modelling. The performance metrics have been generated using the eXtreme gradient boosting regressor, random forest regressor, and support vector regressor. The current study uses real-world healthcare data to conduct experiments and simulations of data with varying feature-wise missing frequencies indicating that the proposed technique surpasses standard missing value imputation approaches as well as the approach of dropping records holding missing values in terms of accuracy.