A Multi-Stage Security Solution for Medical Color Images in Healthcare Applications

This paper presents a robust multi-stage security solution based on fusion,encryption,and watermarking processes to transmit color healthcare images,efficiently.The presented solution depends on the features of discrete cosine transform(DCT),lifting wavelet transform(LWT),and singular value decomposition(SVD).The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks.During watermark embedding,the host color medical image is transformed into four sub-bands by employing three stages of LWT.The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD operation.Furthermore,a fusion process is used for combining different watermarks into a single watermark image.This single fused image is then ciphered using Deoxyribose Nucleic Acid(DNA)encryption to strengthen the security.Then,the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked image.The main contribution of this work is embedding multiple watermarks to prevent identity theft.In the presence of different multimedia attacks,several simulation tests on different colormedical images have been performed.The results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio(PSNR)values and high correlation between the extracted and original watermark images.Moreover,the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.

Fuzzy Based Decision-Making Approach for Estimating Usable-Security of Healthcare Web Applications

Usability and security are often considered contradictory in nature.One has a negative impact on the other.In order to satisfy the needs of users with the security perspective,the relationship and trade-offs among security and usability must be distinguished.Security practitioners are working on developing new approaches that would help to secure healthcare web applications as well increase usability of the web applications.In the same league,the present research endeavour is premised on the usable-security of healthcare web applications.For a compatible blend of usability and security that would fulfill the users’requirments,this research proposes an integration of the Fuzzy AHP-TOPSIS method for assessing usable-security of healthcare web applications.Since the estimation of security-usability accrately is also a decision making problem,the study employs Multiple Criteria Decision Analysis(MCDA)for selecting the most decisive attributes of usability as well as security.Furthermore,this study also pinpoints the highest priority attributes that can strengthen the usable-security of the healthcare web applications.The effectiveness of the suggested method has been tested on the healthcare web applications of local hospitals in Mecca,Saudi Arabia.The results corroborate that Fuzzy AHP-TOPSIS is indeed a reliable technique that will help the developers to design a healthcare web applications that delivers optimum usable-security.

Artificial Intelligence in Healthcare:Review and Prediction Case Studies

Artificial intelligence(AI)has been developing rapidly in recent years in terms of software algorithms,hardware implementation,and applications in a vast number of areas.In this review,we summarize the latest developments of applications of AI in biomedicine,including disease diagnostics,living assistance,biomedical information processing,and biomedical research.The aim of this review is to keep track of new scientific accomplishments,to understand the availability of technologies,to appreciate the tremendous potential of AI in biomedicine,and to provide researchers in related fields with inspiration.It can be asserted that,just like AI itself,the application of AI in biomedicine is still in its early stage.New progress and breakthroughs will continue to push the frontier and widen the scope of AI application,and fast developments are envisioned in the near future.Two case studies are provided to illustrate the prediction of epileptic seizure occurrences and the filling of a dysfunctional urinary bladder.

Blockchain-as-a-Utility for Next-Generation Healthcare Internet of Things

The scope of the Internet of Things(IoT)applications varies from strategic applications,such as smart grids,smart transportation,smart security,and smart healthcare,to industrial applications such as smart manufacturing,smart logistics,smart banking,and smart insurance.In the advancement of the IoT,connected devices become smart and intelligent with the help of sensors and actuators.However,issues and challenges need to be addressed regarding the data reliability and protection for signicant nextgeneration IoT applications like smart healthcare.For these next-generation applications,there is a requirement for far-reaching privacy and security in the IoT.Recently,blockchain systems have emerged as a key technology that changes the way we exchange data.This emerging technology has revealed encouraging implementation scenarios,such as secured digital currencies.As a technical advancement,the blockchain network has the high possibility of transforming various industries,and the next-generation healthcare IoT(HIoT)can be one of those applications.There have been several studies on the integration of blockchain networks and IoT.However,blockchain-as-autility(BaaU)for privacy and security in HIoT systems requires a systematic framework.This paper reviews blockchain networks and proposes BaaU as one of the enablers.The proposed BaaU-based framework for trustworthiness in the next-generation HIoT systems is divided into two scenarios.The rst scenario suggests that a healthcare service provider integrates IoT sensors such as body sensors to receive and transmit information to a blockchain network on the IoT devices.The second proposed scenario recommends implementing smart contracts,such as Ethereum,to automate and control the trusted devices’subscription in the HIoT services.

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

Proactive Personalized Services Through Fog-Cloud Computing in Large-Scale IoT-Based Healthcare Application

With the development of medical sensors and IoT, personalized service assisted elder and patient living is a critical service in IoT-based healthcare application. However, the scale and complexity of personalized service is increasing because of ubiquitous deployment of various kinds of medical sensors, which cause response time increase and resource waste. Therefore, leveraging the advantage of complex event processing(CEP) in data stream processing, we propose a hierarchical fog-cloud computing CEP architecture for personalized service to accelerate response time and reduce resource waste. Firstly, we introduce the proposed architecture, which includes sensor layer, fog layer and cloud layer. Secondly, we propose a series of optimizations for the architecture, there are a partitioning and clustering approach and a communication and parallel processing policy to optimize the fog and cloud computing. Finally, we implement a prototype system based on the architecture named Fog Cep Care. Experimental result shows that Fog Cep Care is superior to the traditional IoT-based healthcare application.

Potential and role of artificial intelligence in current medical healthcare

Artificial intelligence(AI)is defined as the digital computer or computer-controlled robot's ability to mimic intelligent conduct and crucial thinking commonly associated with intelligent beings.The application of AI technology and machine learning in medicine have allowed medical practitioners to provide patients with better quality of services;and current advancements have led to a dramatic change in the healthcare system.However,many efficient applications are still in their initial stages,which need further evaluations to improve and develop these applications.Clinicians must recognize and acclimate themselves with the developments in AI technology to improve their delivery of healthcare services;but for this to be possible,a significant revision of medical education is needed to provide future leaders with the required competencies.This article reviews the potential and limitations of AI in healthcare,as well as the current medical application trends including healthcare administration,clinical decision assistance,patient health monitoring,healthcare resource allocation,medical research,and public health policy development.Also,future possibilities for further clinical and scientific practice were also summarized.

Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical changes.It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally.Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival rates.The significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening methods.Advances in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung cancer.CT scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung cancer.Consequently,there is growing interest in enhancing computer-aided detection(CAD)systems.These algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer diagnosis.This study aims to enhance the effectiveness of CAD systems through various methods.Initially,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual quality.Further refinement is achieved by integrating different optimization strategies with the CLAHE method.The CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and EfficientNet.The study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE algorithm.The empirical findings of the study demonstrate a significant reduction in the false positive rate(FPR)and an overall enhancement in diagnostic accuracy.This research not only contributes to the field of medical imaging but also holds significant implications for the early detection and treatment of lung cancer,ultimately aiming to reduce its mortality rates.

Design of a Biomedical Device to Reduce Anxiety Experienced by Patients Diagnosed with Parkinson’s Disease

Parkinson’s disease (PD) is a neurodegenerative disease that occurs due to loss of nerve cells that produce dopamine in the brain, affecting approximately 4 million people worldwide. PD patients often feel an increase in anxiety levels daily. While there are medications/exercises to help relieve anxiety, there are limited methods to reduce anxiety without the help of a caretaker. As a result, MEDIC Foundation, a non-profit organization in British Columbia, Canada, is designing an automated system that consists of a wristband and an application which uses vi-bration therapy to help reduce anxiety of PD patients. Literature reviews were conducted to document the project’s needs. Phase I of the project focused on de-veloping a prototype for the application and phase II on developing the wrist-band. The team developed prototypes of a wristband that automatically applies vibration near the median nerve as the heart rate variability (HRV) deviates away from the normal threshold of the user, and an application that displays real-time heart rate variability signals as well as provides for relaxation. The development of the prototype is still in early progress. By creating this automated system, we aim to provide a solution to senior PD patients to relieve anxiety independently. .

A Broad Range Triboelectric Stiffness Sensor for Variable Inclusions Recognition

With the development of artificial intelligence,stiffness sensors are extensively utilized in various fields,and their integration with robots for automated palpation has gained significant attention.This study presents a broad range self-powered stiffness sensor based on the triboelectric nanogenerator(Stiff-TENG)for variable inclusions in soft objects detection.The Stiff-TENG employs a stacked structure comprising an indium tin oxide film,an elastic sponge,a fluorinated ethylene propylene film with a conductive ink electrode,and two acrylic pieces with a shielding layer.Through the decoupling method,the Stiff-TENG achieves stiffness detection of objects within 1.0 s.The output performance and characteristics of the TENG for different stiffness objects under 4 mm displacement are analyzed.The Stiff-TENG is successfully used to detect the heterogeneous stiffness structures,enabling effective recognition of variable inclusions in soft object,reaching a recognition accuracy of 99.7%.Furthermore,its adaptability makes it well-suited for the detection of pathological conditions within the human body,as pathological tissues often exhibit changes in the stiffness of internal organs.This research highlights the innovative applications of TENG and thereby showcases its immense potential in healthcare applications such as palpation which assesses pathological conditions based on organ stiffness.

A Comprehensive Review on Medical Diagnosis Using Machine Learning

The unavailability of sufficient information for proper diagnosis,incomplete or miscommunication between patient and the clinician,or among the healthcare professionals,delay or incorrect diagnosis,the fatigue of clinician,or even the high diagnostic complexity in limited time can lead to diagnostic errors.Diagnostic errors have adverse effects on the treatment of a patient.Unnecessary treatments increase the medical bills and deteriorate the health of a patient.Such diagnostic errors that harm the patient in various ways could be minimized using machine learning.Machine learning algorithms could be used to diagnose various diseases with high accuracy.The use of machine learning could assist the doctors in making decisions on time,and could also be used as a second opinion or supporting tool.This study aims to provide a comprehensive review of research articles published from the year 2015 to mid of the year 2020 that have used machine learning for diagnosis of various diseases.We present the various machine learning algorithms used over the years to diagnose various diseases.The results of this study show the distribution of machine learning methods by medical disciplines.Based on our review,we present future research directions that could be used to conduct further research.

Hybrid Computational Modeling for Web Application Security Assessment

Transformation from conventional business management systems to smart digital systems is a recurrent trend in the current era.This has led to digital revolution,and in this context,the hardwired technologies in the software industry play a significant role However,from the beginning,software security remains a serious issue for all levels of stakeholders.Software vulnerabilities lead to intrusions that cause data breaches and result in disclosure of sensitive data,compromising the organizations’reputation that translates into,financial losses as well.Most of the data breaches are financially motivated,especially in the healthcare sector.The cyber invaders continuously penetrate the E-Health data because of the high cost of the data on the dark web.Therefore,security assessment of healthcare web-based applications demands immediate intervention mechanisms to weed out the threats of cyber-attacks.The aim of this work is to provide efficient and effective healthcare web application security assessment.The study has worked with the hybrid computational model of Multi-Criteria Decision Making(MCDM)based on Analytical Hierarchy Process(AHP)and Technique for Order of Preference by Similarity to Ideal-Solutions(TOPSIS)under the Hesitant Fuzzy(HF)environment.Hesitant fuzzy sets provide effective solutions to address decision making problems where experts counter hesitation to make a decision.The proposed research endeavor will support designers and developers in identifying,selecting and prioritizing the best security attributes for web applications’development.The empirical analysis concludes that Robustness got highest priority amongst the assessed security attributes set followed by Encryption,Authentication,Limit Access,Revoke Access,Data Validation,and Maintain Audit Trail.The results of this research endeavor depict that this proposed computational procedure would be the most conversant mechanism for determining the web application security.The study also establishes guidelines which the developers can refer for the identification and prioritization of security attributes to build more secure and trustworthy web-based applications.

Body sensor networks for ubiquitous healthcare

Body sensor networks provide a platform for ubiquitous healthcare, driving the diagnosis in hospital static environment to the daily life dynamic context. We realized the importance of sensing of activities, which is not only a dimension of human health but also important context information for diagnosis based on the physiologic data. This paper presents our ubiquitous healthcare system, uCare. It consists of uCare devices and a server system. Currently, the uCare system is designed for cardiovascular disease (CVD) examination and management. The uCare device has been tested in a trial in Beijing Hospital. The uCare system will be further tested in elderly care at home and exercise management in training to measure heart dynamics during training.

Liquid metal E-tattoo

The past twenty years have witnessed a rapid advancement in medical devices and healthcare techniques.Motivated by the growing demand for personalized,preventive,predictive and participatory medicine,the on-skin portable healthcare system with fascinating merits has attracted great interest.Especially,the electronic tattoo(E-tattoo)that can form intimate contact and deform with the skin movement is regarded to play an important role in further healthcare monitoring and disease treatment.Endowed with the combination of fluidity and metallic properties,liquid metals(LMs)have become an emerging class of functional materials and are regarded as the ideal candidate for soft electronics.Here,we highlighted the key advantages of LM in E-tattoo,classified the LM based conductive inks,and summarized the important pattern technologies in fabrication of LM E-tattoo.The typical applications of healthcare detection and therapy were also discussed.Finally,outlooks were provided for future E-tattoo development.

Dynamic appointment scheduling for outpatient clinics with multiple physicians and patient choice

Mitigating the adverse effects of uncertainty in appointment systems,arising from heterogeneous patient needs and preferences,is critical to the effective use of scarce medical resources and patient satisfaction.This study addresses an online scheduling problem with multiple servers and consideration of patient preference for physicians and their appointment times.The receptionist immediately determines whether a request should be accommodated and offers an appointment time slot for each accepted patient request.The patient may reject an undesirable appointment time slot with a certain probability,or may accept it,but the no-show probability will be higher.A stochastic overbooking model is formulated to maximize the expected profit,which is defined as the revenue generated from accepted requests minus the cost incurred by patients waiting and physicians’overtime.A myopic scheduling policy is developed based on certain structural properties of the objective function.This study advances the study of appointment systems by generating a non-unimodal profit evolution.Moreover,both the decision of accommodating more requests for certain slots and the scheduling of appointments depend on the patient choice rather than the patient type.Further,computational experiments and analysis offer valuable insights into performance improvement in outpatient clinics.