ABSTRACT:- The use of technologies from many fields, such as mass spectrometry, next-generation sequencing, or image processing, is common in experiments in the life sciences. Complex scripts are frequently used to govern data flow, data transformation, and statistical analysis when passing data between such tools. Such scripts not only tend to be platform dependant, but also tend to expand as the experiment goes on and are rarely clearly documented, which makes the experiment harder to reproduce. Workflow systems like KNIME Analytics Platform, which offers a platform for graphically linking tools and ensures the same results across various operating systems, aim to address these issues. systems that are frequently employed in the biological sciences and describe how they compare and contrast with KNIME. KNIME is an open source program that enables programmers and scientists to share their own extensions with the scientific community. The unified data model of KNIME allows for interoperability, and we describe a few additions from the life sciences that make it easier to explore, analyze, and visualize data. In addition, we mention additional workflow. According to the American Cancer Society, skin cancer is the most prevalent form of malignancy in humans. It is typically identified visually, with first clinical screenings, dermoscopic (skin-related) analysis, a biopsy, and histological examinations as potential follow-up steps. Errors (mutations) in the DNA of skin cells are the cause of skin cancer. The cells proliferate out of control and aggregate into a mass of cancer cells as a result of the mutations. In this paper, convolutional neural networks are used to attempt to categorize photos of skin lesions. The deep neural networks demonstrate enormous potential for classifying images while taking into account the extreme environmental heterogeneity. Due to the current state of technology, it is imperative to use machines rather than people to address the widespread problem of skin cancer. One of the best ways to address skin cancer issues is deep learning. Huge data, virtual reality, augmented reality, and miniservices are all used in the new research area of deep learning in contemporary technology. The advent of powerful arithmetic capabilities enabled deep learning applications using Mobile net (CNN) to revolutionize image classification. The various forms of skin cancer can be categorized using deep learning. Transfer Learning was used during the training on many models. The model's best level of accuracy was over 77.333 %. To guarantee the validity and reproducibility of the aforementioned result, the dataset employed is openly accessible.

Keywords: Artificial Intelligence (AI), Convolutional Neural Networks (CNNs), Deep Learning (DL), Skin Cancer, Image Classification, Knime Analytics Platform Software etc.

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Engineering education has a crucial role in the economic and social development of society; this paper aims to investigate the most significant factors that contributing in improving the quality of the engineering education. Furthermore, identify the factors affecting the students’ academic performance. The study was conducted by using statistical Design of Experiments (DOE) approach to investigate the main effects of lecture delivery, assessment, engagement, communication, and interaction with instructors. Minitab software is implemented to perform statistical analysis to identify the significance of each factor, and degree by which each factor influences the students’ academic achievement. Results showed that the students’ academic performance is affected by the availability of course materials, and communications with instructors.

This paper addresses a bi-criteria optimization problem to minimize total flow time and makespan simultaneously for a cellular flowshop with Sequence Dependent Setup Times (FMCSP with SDSTs); A multi-objective Particle Swarm Optimization (MPSO) and a Multi-objective Simulated Annealing (MOSA) Algorithm are proposed to solve the proposed problem. furthermore, an improved algorithm (named as IMPSO-TA), where MPSO is combined with Threshold Acceptance (TA) algorithm to improve the convergence of the obtained Pareto Fronts. The proposed algorithms are evaluated using several Quality Indicators (QI) measures for multi-objective optimization problems. Results showed that proposed algorithms can generate approximated Pareto Fronts in a reasonable CPU time. Furthermore, quality of Pareto fronts generated by IMPSO-TA is better than Pareto fronts found by MPSO and MOSA based on the test problems that are used in this research at the cost of CPU time. Further, the proposed IMPSO-TA performs as best available algorithms in the literature for small and medium test problems with a very minor deviation for best results for large test problems.

Oil spills are an unavoidable risk during exploration, production, and transportation. In the event of an accident, the environmental impact is often severe and may even harm the local environment for decades. Therefore, there is a need to find a method and device that minimizes damage to the environment. Oil spill cleanup and remedy are still a challenge and raise global concern because of the many environmental complications associated with it. This chapter reviews existing methods and devices for oil spill cleanup, particularly the wetting phenomenon for oil absorption and membrane filtration methods for oil spill cleanup, and focuses on the properties of nanofiber materials and their use for manufacturing oil absorbents and cross-flow filtration membranes. In particular, this chapter discusses the performance, effectiveness, and applications of nanofibrous materials fabricated by the solution and hot melt …