Layered Architecture Research Articles

An Adaptive Xception Model for Classification of Brain Tumors

Classification of different brain tumors is challenging due to unpredictable variations in intra-inter-classes. Unlike existing methods which are not effective for images of complex backgrounds, the proposed work aims at accurate classification of diverse types of brain tumors such that an appropriate model can be used for disease identification. This study considers glioma, meningioma, no tumor, and pituitary tumors for classification. To achieve an accurate classification, we explore the Xception architecture layer, which involves flattening, dropout, and dense layer operations. The model extracts features based on shapes, spatial relationships, and structure of the image, discriminating between the different brain tumor images. The model is evaluated on a dataset of 7023 MRI images for classification. The results of a large dataset and comparative study with the existing methods show that the proposed method is better than state of the art in terms of classification rate. Specifically, our method achieves more than a 90% average classification rate, which is better than state of the art. The results on noisy and blurred datasets show that the proposed model is robust to noise and blur.

A novel strategy for flood flow Prediction: Integrating Spatio-Temporal information through a Two-Dimensional hidden layer structure

In recent years, neural network models have been extensively applied in flood prediction due to their superior performance. However, most studies aimed at enhancing models have simply stacked basic models, overlooking the need for designs specific to the physical attributes of hydrological data. While stacking basic models can enhance flood prediction to a degree, they often introduce significant unnecessary complexity redundancy. Moreover, improvement strategies are challenging to generalize across different models. To address this issue, a widely applicable Two-Dimensional Hidden Layer (Td) architecture is proposed. Unlike single hidden layer architectures, this design specifically tackles hydrological data’s spatiotemporal characteristics. Discretizing spatiotemporal information in flood and rainfall data yields an abstract, two-dimensional representation of the hydrological data to enhance the model’s ability to utilize hydrological information. Using a Td structure instead of the single hidden layer in Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network (CNN), the TdRNN, TdLSTM, TdBiLSTM, and TdCNN models are developed to assess the novel structure’s impact on foundational models. Experiments were conducted using empirical flood process data from the LouDe Station in Shandong, China, and the NingXiang Station in Hunan, China. The results indicate that, compared to benchmark models, network models incorporating a Td structure exhibit superior adaptability across various lead times. For instance, at the LouDe Station, with a one-hour lead time, the TdCNN model showed improvements in the Nash-Sutcliffe Efficiency Coefficient (NSE), Kling-Gupta Efficiency Coefficient (KGE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) performance metrics by 26.24%, 33.48%, 62.51%, and 56.88%, respectively, compared to the CNN model. The new models also demonstrated significant performance enhancements under other conditions. The experimental results demonstrate that the Td structure is an effective and versatile model improvement strategy. It enhances the prediction accuracy by strengthening the model’s ability to utilize information. This study may contribute to the development of a flood prediction system and provide data to support subsequent reservoir scheduling and disaster mitigation planning.

Intracellular nonenzymatic in situ growth of layered nanosheet DNA architectures based on palindrome-chained dumbbell probes for miRNA imaging

MicroRNA (miRNA) represents a class of important potential biomarkers, and their intracellular imaging is extremely useful for fundamental research and early diagnosis of human cancers. Hybridization chain reaction (HCR) has been shown to be effective in detecting miRNA in living cells. However, its practical applications are still hampered by inefficient reaction kinetics and poor biological stability under complex intracellular conditions. To address these issues, we report a palindrome-mediated multiple hybridization chain reaction (P-HCR) system to better visualize intracellular miRNAs. In the presence of the target miRNA, a layered nanosheet DNA architecture (LSDA) can be assembled in situ via the palindrome-mediated multiple HCR process. We demonstrate that the biological stability of this reaction system could be significantly improved by designing the probes to dumbbell-shaped structures and the distance of hairpins was effectively decreased due to palindrome-chained effect. Consequently, miRNA can be quantitatively identified even at extremely low concentrations of 4.7 pM. The P-HCR system can effectively differentiate the expression levels of miRNA in different tumor cells and normal cells, as demonstrated in live cell tests and the results were in agreement with the PCR, which is considered the gold standard. The new (P-HCR) system has the potential to revolutionize miRNA imaging in living cells.

Synergistic Biofilter Tube for Promoting Scarless Tendon Regeneration.

Inspired by the imbalance between extrinsic and intrinsic tendon healing, this study fabricated a new biofilter scaffold with a hierarchical structure based on a melt electrowriting technique. The outer multilayered fibrous structure with connected porous characteristics provides a novel passageway for vascularization and isolates the penetration of scar fibers, which can be referred to as a biofilter process. In vitro experiments found that the porous architecture in the outer layer can effectively prevent cell infiltration, whereas the aligned fibers in the inner layer can promote cell recruitment and growth, as well as the expression of tendon-associated proteins in a simulated friction condition. It was shown in vivo that the biofilter process could promote tendon healing and reduce scar invasion. Herein, this novel strategy indicates great potential to design new biomaterials for balancing extrinsic and intrinsic healing and realizing scarless tendon healing.

Smart prison technology and challenges: a systematic literature reviews

<span lang="EN-US">The rapid rise of intelligent technology, particularly in government, is igniting a new phase of the industrial revolution around the world. As governmental entities, prisons oversee upholding social order and lowering current crime. The concept of the smart prison has not received much attention but is gaining traction. The goal of this research is to conduct a literature review to identify current prison technologies and to analyse the challenges associated with implementing smart prisons using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol. Nine credible publishers were looked up between October 2022 and December 2022. The initial search yielded 362 articles, of which 25 were included in the final phase. This research provides the current state of prison according to technology-organization-environment (TOE). Some challenges arise in the context of TOE, such as the high cost of smart technology, inadequate technology design, poor management, ineffective service, overcrowding, ageing facilities, increasing violence, disease spread, and ethical problems. This study also classifies smart prison technology based on the internet of things (IoT) architecture layer. By providing the first comprehensive review on smart prison technology, this study makes an essential contribution to the subject of prisons.</span>

Malaria cell identification using improved machine learning and modified deep learning architecture

Malaria continues to be a serious problem for public health because of its occurrence in tropical and subtropical areas with inadequate healthcare systems and few resources. For prompt intervention and treatment of malaria, effective and precise diagnosis is essential. Professional pathologists examine blood smear films by hand to get a microscopic diagnosis and another way they will do a rapid antigen malaria test which produces the result of 50% accuracy. Convolutional neural network (CNN) is a type of deep learning (DL) model that has been effectively used for a variety of image recognition applications. Our suggested approach uses, improved machine learning (IML) methods like support vector machine (SVM)+principal component analysis (PCA) fit, SVM+t-distributed stochastic neighbor embedding (t-SNE) fit, and CNN architecture with an accuracy of 86.23%, 88.27%, and 97.16% accuracy respectively, to combine feature extraction, data augmentation, and modify the layers by including the SVM algorithm in the final layer of the CNN architecture. The proposed method will significantly reduce pathologists' burden by automating the identification of malaria and improving diagnosis accuracy in resourceconstrained contexts

Towards the automatic network resource management of OPC UA in 5G private networks

In the realm of Network Function Virtualization (NFV) and Software Defined Networking (SDN), the shift of network functions to the edge presents both challenges and opportunities across various industries. This paper investigates the utilization of Network Resource Management (NRM) within the Third Generation Partnership Project (3GPP) Service Enabler Architecture Layer (SEAL) framework for network operations in different sectors. Leveraging the Open Platform Communications (OPC) Unified Architecture (UA) Publish Subscribe (PubSub) pattern, we enhance edge networking by automating NRM within SEAL, introducing custom Message Queuing Telemetry Transport (MQTT) brokers and a mapping node. To validate our approach, we establish a SEAL NRM test server and implement a translator mechanism for interoperability. We address the integration of non-adaptive applications into the NRM domain and demonstrate our proposal with a practical Robot Operating System (ROS) Digital Twin (DT) application, showcasing dynamic NRM’s potential for optimizing network resources in scenarios like 3D printing.

A taxonomy of endpoint vulnerabilities and affected blockchain architecture layers

A taxonomy of endpoint vulnerabilities and affected blockchain architecture layers

The Metaverse Flywheel: Creating Value across Physical and Virtual Worlds

This study presents a metaverse flywheel model providing insights into how the emerging layered modular architecture of the metaverse can enable new types of value-creation opportunities for organizations. Based on interviews with early metaverse adopters and innovators, this article identifies three key metaverse affordances: prospection of future conditions, persistence of editable and evolving virtual spaces, and integration between virtual and physical worlds. The findings enhance the nascent metaverse literature by highlighting that new organizing logics are required for metaverse-specific value creation, which goes beyond the previous generation’s isolated 3D models and other interfaces.

Evaluation of Classification Performance of New Layered Convolutional Neural Network Architecture on Offline Handwritten Signature Images

While there are many verification studies on signature images using deep learning algorithms in the literature, there is a lack of studies on the classification of signature images. Signatures are used as a means of identification for banking, security controls, symmetry, certificates, and contracts. In this study, the aim was to design network architectures that work very fast in areas that require only signature images. For this purpose, a new Si-CNN network architecture with existing layers was designed. Afterwards, a new loss function and layer (Si-CL), a novel architecture using Si-CL as classification layer in Si-CNN to increase the performance of this architecture, was designed. This architecture was called Si-CNN+NC (New Classification). Si-CNN and Si-CNN+NC were trained with two datasets. The first dataset which was used for training is the “C-Signatures” (Classification Signatures) dataset, which was created to test these networks. The second dataset is the “Cedar” dataset, which is a benchmark dataset. The number of classes and sample numbers in the two datasets are symmetrical with each other. To compare the performance of the trained networks, four of the most well-known pre-trained networks, GoogleNet, DenseNet201, Inceptionv3, and ResNet50, were also trained with the two datasets with transfer learning. The findings of the study showed that the proposed network models can learn features from two different handwritten signature images and achieve higher accuracy than other benchmark models. The test success of the trained networks showed that the Si-CNN+NC network outperforms the others, in terms of both accuracy and speed. Finally, Si-CNN and Si-CNN+NC networks were trained with the gold standard dataset MNIST and showed superior performance. Due to its superior performance, Si-CNN and Si-CNN+NC can be used by signature experts as an aid in a variety of applications, including criminal detection and forgery.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

ROSPaCe: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System and IoT Networks

Most of the intrusion detection datasets to research machine learning-based intrusion detection systems (IDSs) are devoted to cyber-only systems, and they typically collect data from one architectural layer. Often the attacks are generated in dedicated attack sessions, without reproducing the realistic alternation and overlap of normal and attack actions. We present a dataset for intrusion detection by performing penetration testing on an embedded cyber-physical system built over Robot Operating System 2 (ROS2). Features are monitored from three architectural layers: the Linux operating system, the network, and the ROS2 services. The dataset is structured as a time series and describes the expected behavior of the system and its response to ROS2-specific attacks: it repeatedly alternates periods of attack-free operation with periods when a specific attack is being performed. This allows measuring the time to detect an attacker and the number of malicious activities performed before detection. Also, it allows training an intrusion detector to minimize both, by taking advantage of the numerous alternating periods of normal and attack operations.

Efficient Pipeline Conflict Resolution for Layered QC-LDPC Decoders in OFDM-PON

Efficient Pipeline Conflict Resolution for Layered QC-LDPC Decoders in OFDM-PON

Observation of dichotomic field-tunable electronic structure in twisted monolayer-bilayer graphene

Twisted bilayer graphene (tBLG) provides a fascinating platform for engineering flat bands and inducing correlated phenomena. By designing the stacking architecture of graphene layers, twisted multilayer graphene can exhibit different symmetries with rich tunability. For example, in twisted monolayer-bilayer graphene (tMBG) which breaks the C2z symmetry, transport measurements reveal an asymmetric phase diagram under an out-of-plane electric field, exhibiting correlated insulating state and ferromagnetic state respectively when reversing the field direction. Revealing how the electronic structure evolves with electric field is critical for providing a better understanding of such asymmetric field-tunable properties. Here we report the experimental observation of field-tunable dichotomic electronic structure of tMBG by nanospot angle-resolved photoemission spectroscopy (NanoARPES) with operando gating. Interestingly, selective enhancement of the relative spectral weight contributions from monolayer and bilayer graphene is observed when switching the polarity of the bias voltage. Combining experimental results with theoretical calculations, the origin of such field-tunable electronic structure, resembling either tBLG or twisted double-bilayer graphene (tDBG), is attributed to the selectively enhanced contribution from different stacking graphene layers with a strong electron-hole asymmetry. Our work provides electronic structure insights for understanding the rich field-tunable physics of tMBG.

Internet of Medical Things Security Frameworks for Risk Assessment and Management: A Scoping Review.

The massive expansion of the Internet of medical things (IoMT) technology brings many opportunities for improving healthcare. At the same time, their use increases security risks, brings security and privacy concerns, and threatens the functioning of healthcare facilities or healthcare provision. This scoping review aims to identify progress in designing risk assessment and management frameworks for IoMT security. The frameworks found are divided into two groups according to whether frameworks address the technological design of risk management or assess technological measures to ensure the security of the IoMT environment. Furthermore, the article intends to find out whether frameworks also include an assessment of organisational measures related to IoMT security. This review was prepared using PRISMA ScR guidelines. Relevant studies were searched in the citation databases Web of Science and Scopus. The search was limited to articles published in English between 2018 and 17 September 2023. The initial search yielded 1341 articles, of which 44 (3.3%) were included in the scoping review. A qualitative content analysis focused on selected security perspectives and progress in the given area was carried out. Thirty-two articles describe the design of risk assessment and management frameworks. Twelve articles describe the design of frameworks for assessing the security of IoMT devices and possibly offer a comparison of different IoMT alternatives. A description of the included articles was prepared from the selected security perspectives. The review shows the need to create comprehensive or holistic frameworks for operational security and privacy risk management at all layers of the IoMT architecture. It includes the design of specific technological solutions and frameworks for continuously assessing the overall level of information security and privacy of the IoMT environment. Unfortunately, none of the found frameworks offer an assessment of organizational measures even though the importance of the organization measures was highlighted in articles. Another area of interest for researchers could be the design of a general risk management database for IoMT, which would include potential IoMT-related risks connected to a particular device.

Memory Guided Transformer With Spatio-Semantic Visual Extractor for Medical Report Generation.

Medicalimaging-based report writing for effective diagnosis in radiology is time-consuming and can be error-prone by inexperienced radiologists. Automatic reporting helps radiologists avoid missed diagnoses and saves valuable time. Recently, transformer-based medical report generation has become prominent in capturing long-term dependencies of sequential data with its attention mechanism. Nevertheless, input features obtained from traditional visual extractor of conventional transformers do not capture spatial and semantic information of an image. So, the transformer is unable to capture fine-grained details and may not produce detailed descriptive reports of radiology images. Therefore, we propose a spatio-semantic visual extractor (SSVE) to capture multi-scale spatial and semantic information from radiology images. Here, we incorporate two types of networks in ResNet 101 backbone architecture, i.e. (i) deformable network at the intermediate layer of ResNet 101 that utilizes deformable convolutions in order to obtain spatially invariant features, and (ii) semantic network at the final layer of backbone architecture which uses dilated convolutions to extract rich multi-scale semantic information. Further, these network representations are fused to encode fine-grained details of radiology images. The performance of our proposed model outperforms existing works on two radiology report datasets, i.e., IU X-ray and MIMIC-CXR.

Multi-Step Multidimensional Statistical Arbitrage Prediction Using PSO Deep-ConvLSTM: An Enhanced Approach for Forecasting Price Spreads

Due to its effectiveness as a risk-hedging trading strategy in financial markets, futures arbitrage is highly sought after by investors in turbulent market conditions. The essence of futures arbitrage lies in formulating strategies based on predictions of future futures price differentials. However, contemporary research predominantly focuses on projections of single indicators for the subsequent temporal juncture, and devising efficacious arbitrage strategies often necessitates the examination of multiple indicators across timeframes. To tackle the aforementioned challenge, our methodology leverages a PSO Deep-ConvLSTM network, which, through particle swarm optimization (PSO), refines hyperparameters, including layer architectures and learning rates, culminating in superior predictive performance. By analyzing temporal-spatial data within financial markets through ConvLSTM, the model captures intricate market patterns, performing better in forecasting than traditional models. Multistep forward simulation experiments and extensive ablation studies using future data from the Shanghai Futures Exchange in China validate the effectiveness of the integrated model. Compared with the gate recurrent unit (GRU), long short-term memory (LSTM), Transformer, and FEDformer, this model exhibits an average reduction of 39.8% in root mean squared error (RMSE), 42.5% in mean absolute error (MAE), 45.6% in mean absolute percentage error (MAPE), and an average increase of 1.96% in coefficient of determination (R2) values.

Smart and Resilient Mobility Services Platform for Managing Traffic Disruptive Events

This article aims to develop a smart mobility solution to enhance the travel experience of individuals facing traffic disruptive events. Unlike prior research focusing on isolated solutions for managing these events, this study takes a holistic approach combining real-time monitoring, predictive modeling, route guidance, and effective communication to create efficient traffic disruption management. The study introduces the Smart and Resilient Mobility Services Platform (SRMS), specifically designed to address mobility restrictions as a form of disruptive events in the Palestinian territories, West Bank. SRMS empowers users to make well-informed decisions by providing services such as real-time mapping of mobility restrictions, a prompt notification system, informal route mapping, and alternative path suggestions. Moreover, it aims to enhance engagement among travelers and citizens by adopting spatial crowdsourcing as the primary data source for potential restrictions and embracing the User-Centered Design (UCD) approach to enrich users’ interaction with the developed solution. The methodology involves presenting the architectural layering system of the SRMS platform, and detailing the prototyping and design development considering the UCD approach. Results present the practical implementation of the SRMS tailored to the Palestinian context and adopted UCD.

From protocols to countermeasures: A comprehensive survey into IoT safety

The Internet of Things (IoT) has become a field with enormous potential, effect, and development with the emergence of smart cities, smart houses, and intelligent objects. Computer Information System Company (Cisco Inc). Projects that there will be 51 billion devices which are connected by 2021. However, the majority of those IoT gadgets are simple to alter and attack. These IoT devices are generally more susceptible to assaults than other types of endpoints such as desktops, mobile phones, or tablets because of their limitations in terms of storage, computing, and networking capability. The main IoT security concerns are presented and addressed in this study. Along with the methods utilized for connection, interaction, and administration, we examine and classify common security concerns about the IoT layers of architecture. In addition to the most recent assaults, challenges and innovative approaches, we describe the security necessities of the IoT. In addition, we connect and tabulate IoT safety problems with current literature-based remedies. Above all, we explore that blockchain technology, the software that powers Bitcoin, can play a major role in enabling the resolution of numerous IoT security issues. Difficulties along with unresolved research issues related to IoT security are also listed in this study.

An Architecture for Workplace Learning Analytics (WLA) to Support Lifelong Learning in Sustainable Smart Organisations

An environment that supports lifelong learning contributes to the sustainability of the organisations in a Smart City, their stakeholders and ultimately, the city itself. Workplace Learning Analytics (WLA) can provide an organisation’s employees with the opportunity for lifelong learning in the workplace to enhance their skills and knowledge in their current and/or future roles. It uses the data generated by Learning Experience Platforms (LXPs) and other learning platforms to provide support for data-driven decision-making to gain a competitive edge. One of the components of successful and sustainable WLA is a layered architecture. The purpose of this paper is to present the design of a WLA architecture that can be used in organisations to impact successful WLA. This architecture was developed from the results of evaluating four potentially relevant architectures for WLA. The evaluation consisted of three phases. In the first phase, the architectures were evaluated using the DeLone and McLean Model of Information Systems Success. In the second phase, a real-world case of an organisation that provides lifelong learning opportunities to its stakeholders was used to validate the findings of the first phase. The proposed Layered Architecture for WLA was further validated in the third phase where a focus group discussion was held with participants from the real-world context. The architecture can provide valuable guidance to both practitioners and researchers to improve the success of WLA.