Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

As the most prevalent cancer in women worldwide, breast cancer requires early detection to have the best possible treatment results. However, conventional screening techniques, like mammography and clinical examinations, can take time and effort. In this paper, we proposed a predictive model for the identification of breast cancer by combining state-of-the-art model BERT with machine learning approaches. Several machine learning algorithms, such as K-nearest neighbours, decision tree, random forest, neural network, and BERT, were tested on the Breast Cancer Wisconsin (Diagnostic) Dataset for early prediction of the diseases. The BERT model's accuracy was improved by using hyperparameter optimisation techniques. For the proposed work's evaluation, we used metrics like accuracy, F1-score, precision, and recall on the standard publically available datasets. With an accuracy of 0.98 across various splits and an area under the curve (AUC) of 0.98 in receiver operating characteristic (ROC) curves, our results show that BERT consistently works better than other models. These findings highlight the value of early and reliable identification in improving patient outcomes, highlighting the promise of machine learning algorithms, notably BERT, inaccurate breast cancer prediction.

Runway irregularities induce vibrations in the fuselage of aircraft during take-off, taxiing, and landing, leading to fatigue stresses in the airframe. These vibrations impacts passenger comfort and affect the functioning of instruments. To reduce fuselage vibrations in the Fokker-70 aircraft, an optimisation of parameters is conducted, aiming to lower the peak of the frequency response at resonant conditions and minimise the time difference between the fuselage and tire stabilisation after touchdown. This prevents airframe failure due to excessive vibration at resonance. MATLAB s Nelder-Mead simplex algorithm is used for optimisation. Additionally, a PID controller is implemented in the landing gear model to further mitigate vibrations. The controller s effectiveness is tested using a runway model with various bumps, adhering to Boeing s runway roughness criteria. Results show the controller smoothens fuselage response to runway excitations, reducing vibration and enhancing the airframe s fatigue life.

The research mainly focuses on the three different types of laparoscopic instruments such as grasping, laparoscopic forceps, and suction irrigation devices with their design modifications and robot assistance activities. Some mechanical components such as links, sliders, gears, pivots, etc. are assisted in the working mechanism of the laparoscopic instruments. Despite of its potential demand, certain complexities are faced by surgeons due to its design characteristics. From the analysis, it is clear that several researchers are focused for optimise the design of forceps and handles in laparoscopic surgery. The process of suction and irrigation contributes well to laparoscopic surgery; however, the interest toward such a process is limited by the researchers. The existing researches of laparoscopic instrumental enhancement between the year 2011 and 2020 are symbolised in this work. This research promotes direction to several surgical device designers by adopting the additional innovative methodology for designing the novel laparoscopic instruments.

In this paper, a global thermal node model is developed for describing no-load indirect solar dryer with double-glass collector. The dryer is formed with drying unit and double air pass collector mounted with two absorber plates separated by confined air. The nodal model takes into account the time variations of solar flux, convection heat losses to the ambient and radiated flux from the soil surface. The mathematical model describing the transient heat transfer process is derived from the node theory, while the surface soil fluxes are approximated using the boundary layer similarity theory. The resulted equations are solved numerically using the iterative finite difference method. The results obtained indicate excellent agreement of the global node model proposed when compared predictions to experimental values of the literature. The simulation reveals significant effect of glass cover thickness and glass covers confined air thickness on the thermal behaviour of the dryer.

Internet of things (IoT) is a transformative technology, reshaping the landscape of connectivity and information exchange. It represents an intricate network of physical devices, vehicles, appliances, and other objects embedded with sensors, software, and connectivity, enabling them to collect and exchange data effortlessly. The paper focuses on measuring the efficacy of optimisation algorithms, namely the hybrid simulated annealing-local search algorithm (SA-LSA), genetic algorithm (GA), differential evolution (DE), and simulated annealing (SA), in addressing multi-objective optimisation challenges and complex function minimisation scenarios. It aims to provide a comprehensive understanding of selecting appropriate algorithms for diverse optimisation challenges, considering factors such as solution space complexity, exploration-exploitation trade-off preferences, and convergence speed. The potential of this work lies in contributing valuable insights into the performances of optimisation algorithms, specifically in navigating trade-offs and converging towards optimal solutions. This work conducts a comparative analysis of algorithms, evaluating the overall performance to provide insights into their strengths and weaknesses, facilitating the selection of optimisation approaches for specific applications spanning multi-objective scenarios and complex function minimisation tasks.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The research mainly focuses on the three different types of laparoscopic instruments such as grasping, laparoscopic forceps, and suction irrigation devices with their design modifications and robot assistance activities. Some mechanical components such as links, sliders, gears, pivots, etc. are assisted in the working mechanism of the laparoscopic instruments. Despite of its potential demand, certain complexities are faced by surgeons due to its design characteristics. From the analysis, it is clear that several researchers are focused for optimise the design of forceps and handles in laparoscopic surgery. The process of suction and irrigation contributes well to laparoscopic surgery; however, the interest toward such a process is limited by the researchers. The existing researches of laparoscopic instrumental enhancement between the year 2011 and 2020 are symbolised in this work. This research promotes direction to several surgical device designers by adopting the additional innovative methodology for designing the novel laparoscopic instruments.

In middle- and low-income countries, cardiovascular illnesses (CVDs) constitute the leading cause of death, with heart attacks and strokes accounting for around 80% of CVD-related fatalities. Enabling early intervention and treatment planning, effective cardiac irregularity prediction and the design of trustworthy heart disease prediction systems eventually lower death rates. This research investigated the viability of predicting cardiac disease using tabular data and convolutional neural networks (CNN). We first retrieved pertinent data from the collection of records, which was then abridged to 14 characteristics; each record is converted into heatmaps, and PNG files of the heatmaps are stored for further CNN processing and visualisation to DenseNet121, ResNet50 and VGG19. Using 10-fold cross-validation, we discovered that DenseNet121, in addition to the optimisation method stochastic gradient descent (SGD), performed better with 97% accuracy while the other two VGG19 54.39% and ResNet50is 51.00% models, performed low as compared to DenseNet121 in addition with the use of accuracy of 54.39% and 51.00%, respectively. Our research demonstrates that deep learning models are capable to correctly forecast heart disease from tabular data. In this paper, it is concluded that tabular data can be given as input to deep learning models to achieve better accuracy and good results can be observed for further study in the field of disease prediction.