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Purpose – Decentralised administrative arrangements and the active function of local government organisations are essential to tackle crisis effectively. Using Bangladesh as a case study, this paper examines the central and local government administrative arrangements during COVID-19 pandemic. Design/methodology/approach – This study applies qualitative content analysis and interviews to explore the local government’s role in Bangladesh’s COVID-19 management by interviews of 18 participants including government officials, experts, non-government organisations (NGOs) representatives, and the general public. This paper also analysed academic papers, policy documents and other publicly available documents, including newspaper reports. Findings – The Constitution of Bangladesh intensified the active participation of local government in each administrative unit through decentralised administrative management. This paper however reveals that the administrative arrangement during the COVID-19 pandemic in Bangladesh was primarily a centrally led system. The local government was not sufficiently involved, nor had it integrated into the planning and coordination process. This indicated the absence of active decentralised administration. Originality/value – This study fills the research gap of the administrative pattern and local relations in COVID-19 management by exploring the local government’s role during the catastrophic situation and highlights the importance of decentralised administrative actions in managing the crisis.

Abstract The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor (KNN), random forest (RF), decision tree (DT), and support vector machine (SVM) for arrhythmia detection. The proposed classifier leverages the Chi-square distance as a primary metric, providing a specialized and original approach for precise arrhythmia detection. To optimize feature selection and refine the classifier's performance, particle swarm optimization (PSO) is integrated with the Chi-square distance as a fitness function. This synergistic integration enhances the classifier’s capabilities, resulting in a substantial improvement in accuracy for arrhythmia detection. Experimental results demonstrate the efficacy of the proposed method, achieving a noteworthy accuracy rate of 98% with PSO, higher than 89% achieved without any previous optimization. The classifier outperforms machine learning (ML) and deep learning (DL) techniques, underscoring its reliability and superiority in the realm of arrhythmia classification. The promising results render it an effective method to support both academic and medical communities, offering an advanced and precise solution for arrhythmia detection in electrocardiogram (ECG) data.

Abstract The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.

To fully exploit enormous data generated by intelligent devices in edge computing, edge federated learning (EFL) is envisioned as a promising solution. The distributed collaborative training in EFL deals with delay and privacy issues compared to traditional centralized model training. However, the existence of straggling devices, responding slow to servers, degrades model performance. We consider data heterogeneity from two aspects: high dimensional data generated at edge devices where the number of features is greater than that of observations and the heterogeneity caused by partial device participation. With large number of features, computation overhead on the devices increases, causing edge devices to become stragglers. And incorporation of partial training results causes gradients to be diverged which further exaggerates when more training is performed to reach local optima. In this paper, we introduce elastic optimization methods for stragglers due to data heterogeneity in edge federated learning. Specifically, we define the problem of stragglers in EFL. Then, we formulate an optimization problem to be solved at edge devices. We customize a benchmark algorithm, FedAvg, to obtain a new elastic optimization algorithm (FedEN) which is applied in local training of edge devices. FedEN mitigates stragglers by having a balance between lasso and ridge penalization thereby generating sparse model updates and enforcing parameters as close as to local optima. We have evaluated the proposed model on MNIST and CIFAR-10 datasets. Simulated experiments demonstrate that our approach improves run time training performance by achieving average accuracy with less communication rounds. The results confirm the improved performance of our approach over benchmark algorithms.

This paper introduces a novel clustering approach based on Minkowski's mathematical similarity to improve EEG feature selection for classification and have efficient Particle Swarm Optimization (PSO) in the context of machine learning. Given the intricacy of high-dimensional medical datasets, feature selection plays a critical role in preventing disease and promoting public health. By employing Minkowski clustering, the objective is to group dataset records into two clusters exhibiting high feature coherence, thereby improving accuracy by applying optimization techniques like PSO to select the most optimal features. Furthermore, the proposed model can be extended to intelligent datasets, including EEG and others. As fewer features are needed for precise categorization, intelligent feature selection is an advanced step of machine learning. This paper investigates the key factors influencing feature selection in the EEG Bonn University dataset. The proposed system is compared against various optimization and feature selection methods, demonstrating superior performance in analyzing and classifying EEG signals based on accuracy measures. The experimental results have confirmed the effectiveness of the suggested model as a valuable tool for EEG data classification, achieving up to 100% accuracy. The outcomes of this research have the potential to benefit medical experts in related specialties by streamlining the process of identifying and diagnosing brain disorders. Technically, the machine learning algorithms RF, KNN, SVM, NB, and DT are employed to classify the selected features.

Diagnostic systems of cardiac arrhythmias face early and accurate detection challenges due to the overlap of electrocardiogram (ECG) patterns. Additionally, these systems must manage a huge number of features. This paper proposes a new classifier Kullback-Leibler classifier (KLC) that combines feature optimization and probabilistic Kullback-Leibler (KL) divergence. Particle swarm optimization (PSO) is used for optimizing the features of ECG data, while KL divergence counts the variance between training and testing probability distributions. The proposed framework led the new classifier to distinguish normal and abnormal rhythms accurately. MIT-BIH Standard Arrhythmia Dataset (MIT-BIH) is used to test the validity of the proposed model. The experimental results show the proposed classifier achieves results in precision (86.67%), recall (86.67%), and F1_Score (86.5%).

Based on Hilbert Random Secure Distribution, a novel data-hiding method for embedding secret information about the patient in a cover image MRI sample has been proposed. Least significant bit (LSB) and most significant bit (MSB) techniques are applied for the physical hiding. Medical images confidentiality suffers from potential attacks and tracing by an unauthorized access. Technically, distributing the secret text in a random way on the cover image is the core security function of the proposed model. In order to evaluate the performance of the proposed solution, three quality metrics: Peak signal to noise ratio (PSNR), Mean Square Error (MSE), percentage residual difference (PRD) and Structural Similarity Index measure (SSIM) were computed and compared on ten MRI images. Experimental results showed significant results in comparison with other models and reached average PSNR up to 61 db. Furthermore, the security analysis in case of $512\times 512$ image samples show complex probability of distribution based on the Hilbert space model.

Abstract Background Infection control in cystic fibrosis (CF) patients plays a crucial role in improving the survival of patients with CF. Antimicrobial sensitivity patterns in these patient groups in our country are currently lacking. Therefore, the purpose of the study was to evaluate the microbiological cultures and antimicrobial susceptibility pattern of pediatric CF patients. Method A total of 50 respiratory samples were prospectively collected from the period between February 2021 and October 2021. Sputum and oropharyngeal swabs were processed for culture and microbiological testing. Sample collection and evaluation were performed according to the Good Laboratory Practice guidelines (GLP). Informed written consent was ensured before participation. Statistical analysis was performed with SPSS v 26. Result The median age of the children was 30 months (6–120) months, with a male predominance (66% vs 34%). Single and two organisms were isolated in 72% (n = 36) and 12% (n = 6) of cases, respectively. During the study period, 36% of the patients harbored Pseudomonas aeruginosa, 18% harbored Klebsiella pneumoniae, and both Staphylococcus aureus and Escherichia coli were detected in 16% of cases. Levofloxacin was found to be the most active antibiotic agent with 100% susceptibility. In contrast, nearly all isolates were resistant to amoxicillin, erythromycin and rifampicin. Conclusion Levofloxacin is the most effective agent to treat CF patients. Active surveillance of the resistance pattern should always continue to be promoted.

Mental illness is increasingly recognized as a substantial public health challenge worldwide. With the advent of social media, these platforms have become pivotal for individuals to express their emotions, thoughts, and experiences, thereby serving as a rich resource for mental health research. This paper is devoted to the creation of a comprehensive dataset and an innovative data annotation methodology to explore the underlying causes of these mental health issues. Our approach included the extraction of over one million Reddit posts from five different subreddits, spanning the pre-pandemic, during-pandemic, and post-pandemic periods. These posts were methodically annotated using a set of specific criteria, aimed at identifying various root causes. This rigorous process produced a richly categorized dataset, invaluable for detailed analysis. The complete unlabelled dataset, along with a subset that has been expertly annotated, is prepared for public release, as outlined in the data availability section. This dataset is a critical resource for training and fine-tuning machine learning models to identify the foundational triggers of individual mental health issues, offering valuable insights for practical interventions and future research in this domain.

Methylene blue (MB) is a water-soluble dye that has a number of medical applications. Methicillin-resistant Staphylococcus aureus (MRSA) was selected as a subject for research due to the numerous serious clinical diseases it might cause and because there is a significant global resistance challenge. Our main goal was to determine and analyze the antibacterial effects of MB against S. aureus both in vitro and ex vivo to enhance treatment options. A total of 104 MRSA isolates recovered from various clinical specimens were included in this study. Minimum inhibitory concentration (MIC) values of MB against MRSA isolates were determined by the agar dilution method. One randomly selected MRSA isolate and a methicillin-susceptible S. aureus strain (S. aureus ATCC 25923) were employed for further evaluation of the antibacterial effects of MB in in vitro and ex vivo time-kill assays. A disc diffusion method-based MB + antibiotic synergy assay was performed to analyze the subinhibitory effects of MB on ten isolates. MICs of MB against 104 MRSA isolates, detected by the agar dilution method, ranged between 16 and 64 µg/mL. MB concentrations of 4 and 16 µg/mL showed a bactericidal effect at 24 h in the ex vivo time-kill assays and in vitro time-kill assays, respectively. We observed a significant synergy between cefoxitin and methylene blue at a concentration of 1–2 μg/mL in two (20%) test isolates. Employing MB, which has well-defined pharmacokinetics, bioavailability, and safety profiles, for the treatment of MRSA infections and nasal decolonization could be a good strategy.

Background Artificial intelligence (AI) has gained momentum in behavioural health interventions in recent years. However, a limited number of studies use or apply such methodologies in the early detection of depression. A large population needing psychological—intervention is left unidentified due to barriers such as cost, location, stigma and a global shortage of health workers. Therefore, it is essential to develop a mass screening integrative approach that can identify people with depression at its early stage to avoid a potential crisis. Objectives This study aims to understand the feasibility and efficacy of using AI-enabled chatbots in the early detection of depression. Methods We use Dialogflow as a conversation interface to build a Depression Analysisn (DEPRA) chatbot. A structured and authoritative early detection depression interview guide, which contains 27 questions combining the structured interview guide for the Hamilton Depression Scale (SIGH-D) and the inventory of depressive symptomatology (IDS-C), underpins the design of the conversation flow. To attain better accuracy and a wide variety of responses, we train Dialogflow with the utterances collected from a focus group of 10 people. The occupation of the focus group members included academics and HDR candidates who are conscious, vigilant and have a clear understanding of the questions. In addition, DEPRA is integrated with a social media platform to provide practical access to all the participants. For the non-clinical trial, we recruited 50 participants aged between 18 and 80 from across Australia. To evaluate the practicability and performance of DEPRA, we also asked participants to submit a user satisfaction survey at the end of the conversation. Results A sample of 50 participants, with an average age of 34.7 years, completed this non-clinical trial. More than half of the participants (54%) are male and the major ethnicities are Asian (63%), Middle Eastern (25%), and others 12%. The first group comprises professional academic staff and HDR candidates, the second and third groups comprise relatives, friends, and volunteers who were recruited via social media promotions. DEPRA uses two scientific scoring systems, QIDS-SR and IDS-SR to verify the results of early depression detection. As the results indicate, both scoring systems return a similar outcome with slight variations for different depression levels. According to IDS-SR, 30% of participants were healthy, 14% mild, 22% moderate, 14% severe, and 20% very severe. QIDS-SR suggests 32% were healthy, 18% mild, 10% moderate, 18% severe, and 22% very severe. Furthermore, the overall satisfaction rate of using DEPRA was 79% indicating that the participants had a high rate of user satisfaction and engagement. Conclusion DEPRA shows promises as a feasible option for developing a mass screening integrated approach for early detection of depression. Although the chatbot is not intended to replace the functionality of mental health professionals, it does show promise as a means of assisting with automation and concealed communication with verified scoring systems.

Abstract Purpose It was aimed to investigate the damage caused by VCD toxicity in the ovary, which women working in the industrial field are frequently exposed to, and to show the relationship between gap junction protein, oxidative stress, and apoptosis, which is thought to be effective in the emergence of this damage. Methods Rats were divided into three groups as control, sham, and VCD. Histological stainings were performed for histopathological evaluations in ovary. Serum AMH level was measured with the ELISA. Then, iNOS, caspase 3, connexin 43 protein, and mRNA expression levels were analyzed by immunohistochemistry and RT‐qPCR methods. Results As a result of the analyses, different amounts of degenerations such as hemorrhage, vacuolization, and fibrosis were observed in the ovary. VCD group AMH level decreased compared to control. In VCD group, iNOS and caspase 3 expressions increased, while connexin 43 expression decreased. Conclusions It was shown that VCD caused damage to all ovarian tissue. Also, it was revealed for the first time that VCD triggered apoptosis by increasing oxidative stress in the ovary and suppressed connexin 43 which was also effective in the survival of granulosa cells. The devastating effect of exposure to occupational chemicals such as VCD on fertility was demonstrated in this study.

This paper proposed a novel privacy model for Electronic Health Records (EHR) systems utilizing a conceptual privacy ontology and Machine Learning (ML) methodologies. It underscores the challenges currently faced by EHR systems such as balancing privacy and accessibility, user-friendliness, and legal compliance. To address these challenges, the study developed a universal privacy model designed to efficiently manage and share patients’ personal and sensitive data across different platforms, such as MHR and NHS systems. The research employed various BERT techniques to differentiate between legitimate and illegitimate privacy policies. Among them, Distil BERT emerged as the most accurate, demonstrating the potential of our ML-based approach to effectively identify inadequate privacy policies. This paper outlines future research directions, emphasizing the need for comprehensive evaluations, testing in real-world case studies, the investigation of adaptive frameworks, ethical implications, and fostering stakeholder collaboration. This research offers a pioneering approach towards enhancing healthcare information privacy, providing an innovative foundation for future work in this field.

Standard environmental condition is one of the main inputs in designing a vessel especially in assessment of stability condition. The performance based minimum stability requirements are determined by assessing vessels' dynamic failure modes. Winds as well as wind generated waves are the main factors that affect a specific vessel’s dynamics. Wind generated waves in rivers though are usually small in comparison with ocean waves may play a crucial role behind inland vessels accidents. The river condition of a crucial location in Bangladesh inland river routes is assessed where wind velocities have been taken for a specific duration from a reliable secondary source. A narrow fetch model that considers the wave generation in off-wind direction for estimating wind wave parameters has been used to consider the spiral shape of Bangladesh inland routes. The Bretschneider energy spectrum model for short term wave state is compared with the fetch limited model JONSWAP for the estimated wave condition. This study indicates the rationality of conforming the safety level of Bangladesh inland vessels equivalent to river-sea vessels as defined by other nationals and the classification societies. The wave parameters that are estimated in this study can be used to form a limited wave scatter table for predicting short term environmental conditions to assess the dynamic stability failure modes of the vessels.

The COVID-19 epidemic has a catastrophic impact on global well-being and public health. More than 27 million confirmed cases have been reported worldwide until now. Due to the growing number of confirmed cases, and challenges to the variations of the COVID-19, timely and accurate classification of healthy and infected patients is essential to control and treat COVID-19. We aim to develop a deep learning-based system for the persuasive classification and reliable detection of COVID-19 using chest radiography. Firstly, we evaluate the performance of various state-of-the-art convolutional neural networks (CNNs) proposed over recent years for medical image classification. Secondly, we develop and train CNN from scratch. In both cases, we use a public X-Ray dataset for training and validation purposes. For transfer learning, we obtain 100% accuracy for binary classification (i.e., Normal/COVID-19) and 87.50% accuracy for tertiary classification (Normal/COVID-19/Pneumonia). With the CNN trained from scratch, we achieve 93.75% accuracy for tertiary classification. In the case of transfer learning, the classification accuracy drops with the increased number of classes. The results are demonstrated by comprehensive receiver operating characteristics (ROC) and confusion metric analysis with 10-fold cross-validation.

Social Media has become an integral part of our daily life. Not only it enables collaboration and flow of information but has also become an imperative tool for businesses and governments around the world. All this makes a compelling case for everyone to be on some sort of online social media platform. However, this virtuousness is overshadowed by some of its shortcomings. The manifestation of antisocial behaviour online is a growing concern that hinders participation and cultivates numerous social problems. Antisocial behaviour exists in its various forms such as aggression, disregard for safety, lack of remorse, unlawful behaviour, etc. The paper introduces a deep learning-based approach to detect and classify online antisocial behaviour (ASB). The automatic content classification addresses the issue of scalability, which is imperative when dealing with online platforms. A benchmark dataset was created with multi-class annotation under the supervision of a domain expert. Extensive experiments were conducted with multiple deep learning algorithms and their superior results were validated against the results from the traditional machine learning algorithms. Visually enhanced interpretation of the classification process is presented for model and error analyses. Accuracy of up to 99% in class identification was achieved on the ground truth dataset for empirical validation. The study is an evidence of how the cutting-edge deep learning technology can be utilized to solve a real-world problem of curtailing antisocial behaviour, which is a public health threat and a social problem.

Diabetes Mellitus, or Diabetes, is a disease in which a person's body fails to respond to insulin released by their pancreas, or it does not produce sufficient insulin. People suffering from diabetes are at high risk of developing various eye diseases over time. As a result of advances in machine learning techniques, early detection of diabetic eye disease using an automated system brings substantial benefits over manual detection. A variety of advanced studies relating to the detection of diabetic eye disease have recently been published. This article presents a systematic survey of automated approaches to diabetic eye disease detection from several aspects, namely: i) available datasets, ii) image preprocessing techniques, iii) deep learning models and iv) performance evaluation metrics. The survey provides a comprehensive synopsis of diabetic eye disease detection approaches, including state of the art field approaches, which aim to provide valuable insight into research communities, healthcare professionals and patients with diabetes.

Wireless body area networks (WBANs) are a new advance utilized in recent years to increase the quality of human life by monitoring the conditions of patients inside and outside hospitals, the activities of athletes, military applications, and multimedia. WBANs consist of intelligent micro- or nano-sensors capable of processing and sending information to the base station (BS). Sensors embedded in the bodies of individuals can enable vital information exchange over wireless communication. Network forming of these sensors envisages long-term medical care without restricting patients’ normal daily activities as part of diagnosing or caring for a patient with a chronic illness or monitoring the patient after surgery to manage emergencies. This paper reviews WBAN, its security challenges, body sensor network architecture and functions, and communication technologies. The work reported in this paper investigates a significant security-level challenge existing in WBAN. Lastly, it highlights various mechanisms for increasing security and decreasing energy consumption.

Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user’s mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatio-temporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather.

There is a Bengali tradition of consuming palm 'jaggery' (unrefined sugar made from the sap of Indian date palms, Phoenix sylvestris) as a sweetener. To obtain jaggery, people tap Indian date palms to extract the energy-rich phloem sap during the winter. In Bangladesh, people commonly cultivate Indian date palms in traditional agroforestry. We explored which bird species capitalize on the jaggery tradition by consuming sap from tapped Indian date palms on Nijhum Dweep, an island in the Bay of Bengal. Once each day for 30 d between December 2019 and February 2020, we quantified the presence of birds on 120 tapped palms along a 1 km transect. We observed 37 bird species in the palms, and 18 of them (49%) were seen to consume sap. Seven species had not previously been recorded as sap feeders. Among the 18 sap-consuming species, we categorized 5 species (Chestnut-tailed Starling Stumia malabarica, Asian Pied Starling Gracupica contra, Jungle Myna Acridotheres fuscus, Red-vented Bulbul Pycnonotus cafer, and Black Drongo Dicrurus macrocercus) as constant consumers of date palm sap; 11 species as accidental sap consumers, and 2 species as accessory sap consumers. Insectivorous and omnivorous species accounted for 78% of the sap consumers (39% each), with granivorous and frugivorous species accounting for the remaining 22%. This study highlights date palm sap as a potentially significant winter food source for resident birds, and demonstrates birds' ability to utilize tapped palms as an anthropogenic food resource. Key words: avian guild, Bangladesh, foraging behavior, Phoenix sylvestris, sap feeding, tropical agriculture. [phrase omitted], Phloem sap, an energy-rich dietary resource, is consumed by several animal groups (Douglas 2006, Nunez Montellano et al. 2013). Sap consumption by birds presents an interesting ecological phenomenon, particularly observed [...]

In both of her novels, Arundhati Roy focuses on specific fatal incidents – either deliberate or accidental – which have catastrophically changed the lives of the major characters, including the children. In The God of Small Things, the unexpected death of Sophie Mol and brutal killing of Velutha exposed those matrices of oppression which, lying unchallenged apart from jeopardizing Ammu and Velutha, problematized the psychic development of Rahel and Estha. Likewise, in The Ministry of Utmost Happiness, Anjum’s deadly experience in the Gujarat massacre, the brutal rape of Revathy, and the killing of Miss Jebeen in Kashmir shed light on those dreadful socio-political extremities, which ostensibly beckon an endangered future for the generation to come. Focusing on Roy’s novels, this paper attempts to exhibit how the predominance of the socio-political upheavals has not only changed the lived experience of the child characters in a cataclysmic way, but also exposed them to a world of cruelty, injustice, and futurelessness.

INTRODUCTION: Diabetic eye disease (DED) is a group of eye problems that can affect diabetic people. Such disorders include diabetic retinopathy, diabetic macular edema, cataracts, and glaucoma. Diabetes can damage your eyes over time, which can lead to poor vision or even permanent blindness. Early detection of DED symptoms is therefore essential to prevent escalation of the disease and timely treatment. Research difficulties in early detection of DEDs can so far be summarized as follows: changes in the eye anatomy during its early stage are often untraceable by the human eye due to the subtle nature of the features, where large volumes of fundus images put tremendous pressure on scarce specialist resources, making manual analysis practically impossible.OBJECTIVES: Therefore, methods focused on deep learning have been practiced to promote early detection of DEDs and address the issues currently faced. Despite promising, highly accurate identification of early anatomical changes in the eye using Deep Learning remains a challenge in wide-scale practical application.METHODS: We present conceptual system architecture with pre-trained Convolutional Neural Network combined with image processing techniques to construct an early DED detection system. The data was collected from various publicly available sources, such as Kaggle, Messidor, RIGA, and HEI-MED. The analysis was presented with 13 Convolutional Neural Networks models, trained and tested on a wide-scale imagenet dataset using the Transfer Learning concept. Numerous techniques for improving performance were discussed, such as (i) image processing,(ii) fine-tuning, (iii) volume increase in data. The parameters were recorded against the default Accuracy metric for the test dataset.RESULTS: After the extensive study about the various classification system, and its methods, we found that creating an efficient neural network classifier demands careful consideration of both the network architecture and the data input. Hence, image processing plays a significant role to develop high accuracy diabetic eye disease classifiers.CONCLUSION: This article recognized specific work limitations in the early classification of diabetic eye disease. First, early-stage classification of DED, and second, classification of DR, GL, and DME using a method that causes permanent blindness afterward. Lastly, this study was intended to propose the framework for early automatic DED detection of fundus images through deep learning addressing three main research gaps.

A systematic and comprehensive review of security and privacy-preserving challenges in e-health solutions indicates various privacy preserving approaches to ensure privacy and security of electronic health records (EHRs) in the cloud. This paper highlights the research challenges and directions concerning cyber security to build a comprehensive security model for EHR. We carry an intensive study in the IEEE, Science Direct, Google Scholar, PubMed, and ACM for papers on EHR approach published between 2000 and 2018 and summarized them in terms of the architecture types as well as evaluation strategies. We surveyed, investigated, and reviewed various aspects of several articles and identified the following tasks: 1) EHR security and privacy; 2) security and privacy requirements of e-health data in the cloud; 3) EHR cloud architecture, and; 4) diverse EHR cryptographic and non-cryptographic approaches. We also discuss some crucial issues and the ample opportunities for advanced research related to security and privacy of EHRs. Since big data provide a great mine of information and knowledge in e-Health applications, serious privacy and security challenges that require immediate attention exist. Studies must focus on efficient comprehensive security mechanisms for EHR and also explore techniques to maintain the integrity and confidentiality of patients' information.

Software-defined networking (SDN) offers a novel paradigm for effective network management by decoupling the control plane from the data plane thereby allowing a high level of manageability and programmability. However, the notion of a centralized controller becomes a bottleneck by opening up a host of vulnerabilities to various types of attacks. One of the most harmful, stealthy, and easy to launch attacks against networked systems is the link flooding attack (LFA). In this paper, we demonstrate the vulnerability of the SDN control layer to LFA and how the attack strategy differs when targeting traditional networks which primarily involves attacking the links directly. In LFA, the attacker employs bots to surreptitiously send low rate legitimate traffic on the control channel which ultimately results in disconnecting control plane from the data plane. Mitigating LFA on the control channel remains a challenge in the network security paradigm with the use of network traffic filtering only. To address this challenge, we propose CyberPulse, a novel effective countermeasure, underpinning a machine learning-based classifier to alleviate LFA in SDN. CyberPulse performs network surveillance by classifying network traffic using deep learning techniques and is implemented as an extension module in the Floodlight controller. CyberPulse was evaluated for its accuracy, false positive rate, and effectiveness as compared to competing approaches on realistic networks generated using Mininet. The results show that CyberPulse can classify malicious flows with high accuracy and mitigate them effectively.

The perineuronal net (PNN) region of the brain’s extracellular matrix (ECM) surrounds the neural networks within the brain tissue. The PNN is a protective net-like structure regulating neuronal activity such as neurotransmission, charge balance, and action potential generation. Shock-induced damage of this essential component may lead to neuronal cell death and neurodegenerations. The shock generated during a vehicle accident, fall, or improvised device explosion may produce sufficient energy to damage the structure of the PNN. The goal is to investigate the mechanics of the PNN in reaction to shock loading and to understand the mechanical properties of different PNN components such as glycan, GAG, and protein. In this study, we evaluated the mechanical strength of PNN molecules and the interfacial strength between the PNN components. Afterward, we assessed the PNN molecules’ damage efficiency under various conditions such as shock speed, preexisting bubble, and boundary conditions. The secondary structure altercation of the protein molecules of the PNN was analyzed to evaluate damage intensity under varying shock speeds. At a higher shock speed, damage intensity is more elevated, and hyaluronan (glycan molecule) is most likely to break at the rigid junction. The primary structure of the protein molecules is least likely to fail. Instead, the molecules’ secondary bonds will be altered. Our study suggests that the number of hydrogen bonds during the shock wave propagation is reduced, which leads to the change in protein conformations and damage within the PNN structure. As such, we found a direct connection between shock wave intensity and PNN damage.

Microtubule-associated protein (MAP) tau is a cross-linking molecule that provides structural stability to axonal microtubules (MT). It is considered a potential biomarker for Alzheimer’s disease (AD), dementia, and other neurological disorders. It is also a signature protein for Traumatic Brain Injury (TBI) assessment. In the case of TBI, extreme dynamic mechanical energies can be felt by the axonal cytoskeletal members. As such, fundamental understandings of the responses of single tau protein, polymerized tau protein, and tau-microtubule interfaces under high-rate mechanical forces are important. This study attempts to determine the high-strain rate mechanical behavior of single tau, dimerized tau, and tau-MT interface using molecular dynamics (MD) simulation. The results show that a single tau protein is a highly stretchable soft polymer. During deformation, first, it significantly unfolds against van der Waals and electrostatic bonds. Then it stretches against strong covalent bonds. We found that tau acts as a viscoelastic material, and its stiffness increases with the strain rate. The unfolding stiffness can be ~50–500 MPa, while pure stretching stiffness can be >2 GPa. The dimerized tau model exhibits similar behavior under similar strain rates, and tau sliding from another tau is not observed until it is stretched to >7 times of original length, depending on the strain rate. The tau-MT interface simulations show that very high strain and strain rates are required to separate tau from MT suggesting Tau-MT bonding is stronger than MT subunit bonding between themselves. The dimerized tau-MT interface simulations suggest that tau-tau bonding is stronger than tau-MT bonding. In summary, this study focuses on the structural response of individual cytoskeletal components, namely microtubule (MT) and tau protein. Furthermore, we consider not only the individual response of a component, but also their interaction with each other (such as tau with tau or tau with MT). This study will eventually pave the way to build a bottom-up multiscale brain model and analyze TBI more comprehensively.

The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network.

Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcareproviders and patients to efficiently store and share their private healthcare information from any place and at any time as per demand. Generally, Cloud services provide the infrastructure by reducing the cost of storing, processing and updating information with improved efficiency and quality. However, the privacy of Electronic Health Records (EHR) is a significant hurdle while outsourcing private health data in the cloud because there is a higher peril of leaking health information to unauthorized parties. Several existing techniques are able to analyse the security and privacy issues associated with e-healthcare services. These methods are designed for single database, or databases, with an authentication centre and thus cannot adequately protect the data frominsider attacks. Therefore, this research study mainly focusses on how to ensure the patient privacy whilesharing the sensitive data between same or different organisations as well as healthcare providers in a cloudenvironment. This paper proposes a multi-layer access control mechanism named MLAC Model to construct asecure and privacy-preserving EHR system that enables patients to share their data with stakeholders. In this paper, we use a Dual layer access control model named Pseudo-Role Attribute based access control (PR-ABAC) mechanism that integrates attributes with roles for the secure sharing of EHR between multiple collaborators. The proposed framework also uses the concept of Provenance to ensure the Integrity of patient data. This work is expected to provide a foundation for developing security solutions against cyber-attacks, and thus contribute to the robustness of healthcare information sharing environments.

Existent literature has limitations regarding the mechanical behavior of axonal cytoskeletal components in a high strain rate scenario, which is mainly due to limitations regarding the structure of some components such as tau protein and neurofilaments (NF). This study performs molecular dynamics (MD) simulations on NFs to extract their strain rate-dependent behavior. It is found that they are highly stretchable and show multiple stages of unfolding. Furthermore, NFs show high tensile stiffness. Also, viscoelastic modeling shows that they correspond to simplified viscoelastic models. This study effectively enhances the existent axonal models focusing on axonal injury.

This cross-sectional study investigates the online education intention of undergraduate students in the largest and oldest public university in Bangladesh during the COVID-19 pandemic. Under convenient sampling, 843 undergraduate students with rural and urban backgrounds participated in an online self-administered questionnaire. Partial least squares structural equation modelling (PLS-SEM) was employed to examine the hypothesized relationships. We found that students' online class intention is significantly influenced by their attitude towards online classes (AOC), perceived usefulness (PU), and facilitating conditions (FC). We further identified that external antecedents have significant indirect effects on the outcome variables. Our findings provide new insights and contribute to a learners' community on online classes during the COVID-19 pandemic. This study extends the technology acceptance model (TAM) and the theory of planned behavior (TPB) to depict the factors influencing undergraduate students' intention to attend online classes (IOC) during the COVID-19 pandemic.

The rapid growth of Global Positioning System (GPS) and availability of real-time Geo-located data allow the mobile devices to provide information which leads towards the Location Based Services (LBS). The need for providing suggestions to personals about the activities of their interests, the LBS contributing more effectively to this purpose. Recommendation system (RS) is one of the most effective and efficient features that has been initiated by the LBS. Our proposed system is intended to design a recommendation system that will provide suggestions to the user and also find a suitable place for a group of users and it is according to their preferred type of places. In our work, we propose the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering the check-in spots of the user’s and user-based Collaborative Filtering (CF) to find similar users as we are considering constructing an interest profile for each user. We also introduced a grid-based structure to present the Point of Interest (POI) into a map. Finally, similarity calculation is done to make the recommendations. We evaluated our system on real world users and acquired the F-measure score on average 0.962 and 0.964 for a single user and for a group of user respectively. We also observed that our system provides effective recommendations for a single user as well as for a group of users.

Software-Defined Networking (SDN) offers an abstract view of the network and assists network operators to control the network traffic and the associated network resources more effectively. For the past few years, SDN has shown a lot of merits in diverse fields of applications, an important one being the Wireless Body Area Network (WBAN) for healthcare services. With the amalgamation of SDN with WBAN (SDWBAN), the patient monitoring and management system has gained much more flexibility and scalability compared to the conventional WBAN. However, the performance of the SDWBAN framework largely depends on the controller which is a core element of the control plane. The reason is that an optimal number of controllers assures the satisfactory level of performance and control of the network traffic originating from the underlying data plane devices. This paper proposes a mathematical model to determine the optimal number of controllers for the SDWBAN framework in healthcare applications. To achieve this goal, the proposed mathematical model adopts the convex optimization method and incorporates three critical SDWBAN factors in the design process: number of controllers, latency and number of SDN-enabled switches (SDESW). The proposed analytical model is validated by means of simulations in Castalia 3.2 and the outcomes indicate that the network achieves high level of Packet Delivery Ratio (PDR) and low latency for optimal number of controllers as derived in the mathematical model.