Computer Science and Information Technologies (E-Journal)
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149 research outputs found
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Electro-capacitive cancer therapy using wearable electric field detector: a review
Full text linkElectro-capacitive cancer therapy (ECCT), a less invasive and more targeted approach using wearable electric field detectors, is revolutionizing cancer therapy, a complex process involving traditional methods like surgery, chemotherapy, and radiation. The review aims to investigate the safety and efficacy of electric field exposure in vital organs, particularly in cancer therapy, to improve medical advancements. It will investigate the impact on cytokines and insulation integrity, as well as contribute to improving diagnostic techniques and safety measures in medical and engineering fields. Wearable electric field detectors have revolutionized cancer therapy by offering a non-invasive and personalized approach to treatment. These devices, such as smart caps or patches, measure changes in electric fields by detecting capacitance alterations. Their lightweight, comfortable, and easy-to-wear nature allows for real-time monitoring, providing valuable data for personalized treatment plans. The portability of wearable detectors allows for long-term surveillance outside clinical settings, increasing therapy efficacy. The ability to collect data over extended periods provides a comprehensive view of electric field dynamics, aiding researchers in understanding tumor growth and progression. Technology advancements in electro-capacitive therapy, including wearable devices, have revolutionized cancer treatment by adjusting electric field intensity in real-time, enhancing personalized medicine, and improving treatment outcomes and patient quality of life
Video shot boundary detection based on frames objects comparison and scale-invariant feature transform technique
Full text linkThe most popular source of data on the Internet is video which has a lot of information. Automating the administration, indexing, and retrieval of movies is the goal of video structure analysis, which uses content-based video indexing and retrieval. Video analysis requires the ability to recognize shot changes since video shot boundary recognition is a preliminary stage in the indexing, browsing, and retrieval of video material. A method for shot boundary detection (SBD) is suggested in this situation. This work proposes a shot boundary detection system with three stages. In the first stage, multiple images are read in temporal sequence and transformed into grayscale images. Based on correlation value comparison, the number of redundant frames in the same shots is decreased, from this point on, the amount of time and computational complexity is reduced. Then, in the second stage, a candidate transition is identified by comparing the objects of successive frames and analyzing the differences between the objects using the standard deviation metric. In the last stage, the cut transition is decided upon by matching key points using a scale-invariant feature transform (SIFT). The proposed system achieved an accuracy of 0.97 according to the F-score while minimizing time consumption
Acoustic echo cancellation system based on Laguerre method and neural network
Full text linkAcoustic echo cancellation (AEC) is a fundamental requirement of signal processing to increase the quality of teleconferences. In this paper, a system that combines the Laguerre method with neural networks is proposed for AEC. In particular, the signal is processed using the Laguerre method to effectively handle nonlinear transmission line system. The results after applying the Laguerre method are then fed into a neural network for training and acoustic echo cancellation. The proposed system is tested on both linear and nonlinear transmission lines. Simulation results show that combining the Laguerre method with neural networks is highly effective for AEC in both linear and nonlinear transmission lines system. The AEC results obtained by the proposed method achieves a significant improvement in nonlinear transmission lines and it is the basis for building a practical echo cancellation system
Company clustering based on financial report data using k-means
Full text linkStock investment is the act of providing funds or assets to obtain future payments for gifts given. In its application, novice investors often make mistakes, one of which is not knowing the health condition of the company they want to target. By applying the machine learning clustering method based on company financial report data, it was found that 2 clusters were formed. This can show the current condition of the company so that it can be a consideration for investors, such as clusters of companies that have a profit trend that is always stable and increasing, or clusters of companies that are in the process of developing their business and groups of companies that have large amounts of debt from year to year
Safeguarding data privacy: strategies to counteract internal and external hacking threats
Full text linkIn the digital age, the protection of data privacy has become increasingly important. Hackers, whether internal or external to an organization, could cause significant damage by stealing sensitive data, causing financial loss, compromising the privacy of individuals, or damaging the organization's reputation. This scientific research aimed to make substantial contributions by emphasizing the importance of addressing both internal and external hacking threats to protect sensitive information. The main theme of their work revolved around building a multi-layered defense system that included technological solutions like firewalls, encryption, and intrusion detection systems. The specific goals of their design and development approach were to establish clear policies and procedures for data handling, access control, and incident response, as well as to enhance data privacy strategies to stay ahead of evolving hacking techniques. The authors also highlighted the significance of employee awareness and training programs, collaboration with cybersecurity experts, and staying up-to-date with regulatory requirements to create a robust data privacy framework
Improving support vector machine and backpropagation performance for diabetes mellitus classification
Full text linkDiabetes mellitus is a glucose disorder disease in the human body that contributes significantly to the high mortality rate. Various studies on early detection and classification have been conducted as a diabetes mellitus prevention effort by applying a machine learning model. The problems that may occur are weak model performance and misclassification caused by imbalanced data. The existence of dominating (majority) data causes poor model performance in identifying minority data. This paper proposed handling the problem of imbalanced data by performing the synthetic minority oversampling technique (SMOTE) and observing its effect on the classification performance of the support vector machine (SVM) and Backpropagation artificial neural network (ANN) methods. The experiment showed that the SVM method and imbalanced data achieved 94.31% accuracy, and the Backpropagation ANN achieved 91.56% accuracy. At the same time, the SVM method and balanced data produced an accuracy of 98.85%, while the Backpropagation ANN method and balanced data produced an accuracy of 94.90%. The results show that oversampling techniques can improve the performance of the classification model for each data class
The best machine learning model for fraud detection on e-platforms: a systematic literature review
Full text linkThe internet has been instrumental in the development and facilitation of online payment systems. However, its associated fraudulent activities on e-platforms cannot be overlooked. As a result, there has been a growing interest in the application of machine learning (ML) algorithms for fraud detection on financial e-platforms. The goal of this research is to identify common types of fraud on financial e-platform, highlight different machine learning algorithms employed in fraud detection, and derive the best machine learning algorithms for fraud detection on e-platforms. To achieve this goal, the research followed a nine steps systematic review approach to retrieve Journals and conference publications from science direct, Google Scholar and IEEE Xplore between 2018 and 2023. Out of 2,071 articles identified and screened, 44 publications (23 articles and 21 conference proceedings) satisfied the inclusion criteria for further analysis. The random forest algorithm turned out to be the best ML algorithm because it ranked first in the frequency of usage analysis and ranked first in the performance analysis with an average accuracy of 96.67%. Overall, this review has identified the kinds of fraud on financial e-platforms, and proclaimed the best and least ML algorithm for fraud detection on financial e-platform. This can help guide future research and inform the development of more effective fraud detection systems
Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets
Full text linkThe surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu-moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic applications
Collecting and analyzing network-based evidence
Full text linkSince nearly the beginning of the Internet, malware has been a significant deterrent to productivity for end users, both personal and business related. Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply ‘witness’ of a criminal event. Forensic investigations include collection, recovery, analysis, and presentation of information stored on network devices and related to network crimes. These activities often involve wide range of analysis tools and application of different methods. This work presents methods that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstructions of events or action to reach case conclusions. Main aim of network forensic is to gather evidence. Additionally, the evidence obtained during the investigation must be produced through a rigorous investigation procedure in a legal context
Improving the quality of handwritten image segmentation using k-means clustering algorithms with spatial filters
Full text linkOne of the ways to predict human characters is by using handwritten patterns. Graphologists have analyzed handwriting to determine a writer's personality by considering several parameters: writing slopes, spacing, inclination, and writing size. The results of the analysis have been widely used as a reference for psychologists to assess an individual's personality. Moreover, researchers have applied techniques to identify human characters using image processing techniques. However, different styles of handwriting require more research to develop. The process of separating objects from backgrounds needs a segmentation process. This research improves the quality of handwritten image segmentation using k-means clustering algorithms with the spatial filter. This spatial filter consisted of the median and mean filters. This research created various k values to gain the best segmentation results. The results showed that the median filter with a kernel size of 3×3 and the k value = 2 was the best segmentation result because the value of silhouette coefficient was the highest compared to the value of filter type and other k values which reach 99.22%