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Indian food recognition can be considered as a case of fine-grained type visual recognition, where the several photos of same category generally have significant variability. Therefore, effective segmentation and classification technique is required to identify the particular cuisines and fine-grained analysis. The paper aims to discuss this issue.

In this paper, the authors provided an effective segmentation approach through the proposed edge adaptive (EA)-deep convolutional neural networks (DCNNs) model, where each input images are divided into patches in order to provide much efficient and accurate structural description of data.

EA-DCNNs starts with developing a coarse map of feature that obtained through DCNN, afterwards EA model is applied to construct the final segmented image.

The training model of EA-DCNN consists of pooling, rectified linear unit and convolution, which help convolutional network to optimize the performance of segmentation in a significant extent, which is much practical and relevant in the context of food image segmentation.

Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team.

This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance.

The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system.

The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work.

This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.

This paper aims to review the shape sensing techniques using large area flexible electronics (LAFE). Shape perception of humanoid robots using tactile data is mainly focused.

Research papers on different shape sensing methodologies of objects with large area, published in the past 15 years, are reviewed with emphasis on contact-based shape sensors. Fiber optics based shape sensing methodology is discussed for comparison purpose.

LAFE-based shape sensors of humanoid robots incorporating advanced computational data handling techniques such as neural networks and machine learning (ML) algorithms are observed to give results with best resolution in 3D shape reconstruction.

The literature review is limited to shape sensing application either two- or three-dimensional (3D) LAFE. Optical shape sensing is briefly discussed which is widely used for small area. Optical scanners provide the best 3D shape reconstruction in the noncontact-based shape sensing; here this paper focuses only on contact-based shape sensing.

Contact-based shape sensing using polymer nanocomposites is a very economical solution as compared to optical 3D scanners. Although optical 3D scanners can provide a high resolution and fast scan of the 3D shape of the object, they require line of sight and complex image reconstruction algorithms. Using LAFE larger objects can be scanned with ML and basic electronic circuitory, which reduces the price hugely.

LAFE can be used as a wearable sensor to monitor critical biological parameters. They can be used to detect shape of large body parts and aid in designing prosthetic devices. Tactile sensing in humanoid robots is accomplished by electronic skin of the robot which is a prime example of human–machine interface at workplace.

This paper reviews a unique feature of LAFE in shape sensing of large area objects. It provides insights from mechanical, electrical, hardware and software perspective in the sensor design. The most suitable approach for large object shape sensing using LAFE is also suggested.

This paper aims to illustrate the increasingly important role played by tactile sensing in robotics by considering three specific fields of application.

Following a short introduction, this paper first provides details of tactile sensing principles, technologies, products and research. The following sections consider tactile sensing applications in robotic surgery, collaborative robots and robotic grippers. Finally, brief conclusions are drawn.

Tactile sensors are the topic of an extensive and technologically diverse research effort, with sensing skins attracting particular attention. Many products are now available commercially. New generations of surgical robots are emerging which use tactile sensing to provide haptic feedback, thereby eliminating the surgeon’s total reliance on visual control. Many collaborative robots use tactile and proximity sensing as key safety mechanisms and some use sensing skins. Some skins can detect both human proximity and physical contact. Sensing skins that can be retrofitted have been developed. Commercial tactile sensors have been incorporated into robotic grippers, notably anthropomorphic types, and allow the handling of delicate objects and those with varying shapes and sizes. Tactile sensing uses will inevitably increase because of the ever-growing numbers of robots interacting with humans.

This study provides a detailed account of the growing use of tactile sensing in robotics in three key areas of application.

The largest share of a building maintenance budget goes towards preventing or repairing building defects. Also, building defects shorten a building’s lifetime, impact the user’s safety and health, prevent the buildings from performing their functions well and repairing building defects generates waste. Therefore, this study aims to specify the factors that affecting the number of building defects and how to reduce their negative impacts.

A case study was used as a research strategy and convergent parallel mixed methods were used as research design. Quantitative and qualitative data were collected concurrently, followed by independent analyses of the quantitative and qualitative data, and then merged the two sets of results according to the procedure of using the convergent parallel design. Descriptive statistics analysed quantitative data, whilst qualitative data was analysed by the content analysis technique.

The findings of this study explored the factors that affect the number of defects in buildings, the significant factors were related to the building’s life cycle in terms of design, construction, operation and maintenance phase; relevant attributes were construction teams, building users and maintenance teams. The study also addressed the approaches to minimise the negative impacts of those factors. Their negative impacts mainly contributed to increased building defects that increase maintenance costs, affect users’ safety and health, reduce buildings’ lifespan and cause environmental impact due to resource extraction.

The existing studies have not adequately addressed the significant factors that affect the number of building defects. Also, emerging technologies and environmental sustainability considerations related to building defects have not been linked in previous related work. Therefore, the present study has contributed to filling this gap.