Medical image segmentation is crucial due to its essential role in disease therapy. Various challenges such as hair artifacts, illumination variations, and different imaging acquisitions complicate this task. In this paper, we introduce a novel convolutional neural network (CNN) architecture designed to address these challenges. We also compare our method with two well-known architectures, Unet and FCN, to demonstrate the superiority of our approach. Our results, evaluated using four metrics, accuracy, dice coefficient, Jaccard index, and sensitivity show that our method outperforms the other two. We employed Jaccard distance and binary cross-entropy as the loss functions and used SGD+Nesterov as the optimization algorithm, which proved more effective than the Adam optimizer. In the preprocessing step, we included image resizing to speed up the process and image augmentation to enhance the evaluation metrics. As a postprocessing step, we applied a threshold technique to the algorithm's outputs to increase the contrast of the final images. This method was tested on two well-known and publicly available medical image datasets: PH2 for melanoma detection and Chest X-ray images for detecting chest lesions or COVID-19.

The latest issue of the disease called COVID-19 has become famous all over the world. Hence, through this problem, it found that this disease have the same symptom with other diseases such as Influenza and also normal flu. Detecting diseases at early stage can enable to overcome and treat them appropriately. This is because many of peoples does not know and does not aware of the symptom of this various diseases. In an effort to address those problems, an Expert System for Corona Earlier Detection has been proposed to help the doctors to detect those various diseases in human body. Through this research, the researcher will held an interview with the doctors to collect data and information about those diseases’ symptoms and also search for the related articles to make sure this research going successfully. The method that will be used in this research is Certainty Factor. To conclude, this system will be useful to the healthcare department as it will give earlier detection when the patient are positively exposed to the disease that is known by Corona.

This manuscript addresses the design and implementation of a portable PC-based ECG device. Three electrodes are often employed for ECG recording, with two of them being connected to the patient's chest via the ECG amplifiers' differential inputs. Therefore, every stage of the design takes into account factors like low cost, low power consumption, portability, and simplicity of usage. In this system, the ATMEL Company's ATMEGA 328 low-power microcontroller is investigated for signal processing and delivering digital format to a PC through a serial connection, where it is then displayed utilizing LabVIEWTM SP1 software ( The released version in Feb. 2022). A portable tool that can capture, amplify, filter, and analyze biological signals is this one ECG. The intended device's target beneficiary was the intensive care unit.

This paper intends to conceptualise an optimisation solution for vehicle routing that can get the best routing result and release the most optimal route to the driver, namely WeRoute. The objectives of the paper are to manage the data efficiently, save time, reduce cost, enhance customer satisfaction, and decrease the emission of carbon. Moreover, this is also known as the vehicle routing problem, which deals with a range of variables, including drivers, stops, roads, and customers. The method, Genetic algorithm, was developed to improve the efficiency of generating feasible routes for a project. A team of drivers and several stops are needed to generate the solution of optimising the vehicle routing. It can be said that the more drivers or stops, the more complicated the problem becomes, such as cost controls and vehicle limitations. Thus, a route optimisation tool slowly becomes the key to ensuring the delivery business as efficiently as possible.

The document search function in the quality assurance unit's information system uses the Knuth-Morris-Pratt algorithm. This KMP algorithm is a search algorithm that will match the pattern or arrangement of words to be searched from left to right at the beginning of the text and then shift the order of words until the word order is at the end of the text. The KMP algorithm has the advantage of matching matches on large files. The system development method used is the Rapid Application Development method. This RAD method is a life cycle strategy aimed at providing development that is much faster and gets results with better quality compared to results achieved through traditional cycles. Application of School Administration Information System is expected to be able to overcome problems arising from conventional document control processes to make it easier and more precise.
 Abstrak: Fungsi pencarian dokumen pada sistem informasi unit jaminan mutu menggunakan algoritma Knuth-Morris-Pratt. Algoritma KMP ini merupakan algoritma pencarian yang akan mencocokkan pattern atau susunan kata yang akan dicari dari kiri ke kanan pada awal teks dan kemudian menggeser susunan kata sampai susunan kata tersebut berada di ujung teks. Algoritma KMP memiliki keunggulan pencarian kecocokan pada file yang berukuran besar. Metode pengembangan sistem yang digunakan yaitu metode Rapid Application Development. Metode RAD ini adalah strategi siklus hidup yang ditujukan untuk menyediakan pengembangan yang jauh lebih cepat dan mendapatkan hasil dengan kualitas yang lebih baik dibandingkan dengan hasil yang dicapai melalui siklus tradisional. Aplikasi Sistem Informasi Administrasi Sekolah ini diharapkan dapat mengatasi permasalahan yang timbul dari proses pengendalian dokumen secara konvensional agar lebih mudah dan tepat.

Vehicle Routing Problem (VRP) relates to the problem of providing optimum service with a fleet of vehicles to customers. It is a combinatorial optimization problem. The objective is usually to maximize the profit of the operation. However, for public transportation owned and operated by the government, accessibility takes priority over profitability. Accessibility usually reduces profit, while increasing profit tends to reduce accessibility. In this research, we look at how accessibility can be increased without penalizing the profitability. This requires the determination of routes with minimum fuel consumption, the maximum number of ports of call and maximum load factor satisfying a number of pre-determined constraints: hard and soft constraints. To solve this problem, we propose a heuristic algorithm. The results from this experiment show that the algorithm proposed has better performance compared to the partitioning set.