International Journal of Advances in Applied Sciences
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MIDI-based generative neural networks with variational autoencoders for innovative music creation
Get PDFBy utilizing variational autoencoder (VAE) architectures in musical instrument digital interface (MIDI)-based generative neural networks (GNNs), this study explores the field of creative music composition. The study evaluates the success of VAEs in generating musical compositions that exhibit both structural integrity and a resemblance to authentic music. Despite achieving convergence in the latent space, the degree of convergence falls slightly short of initial expectations. This prompts an exploration of contributing factors, with a particular focus on the influence of training data variation. The study acknowledges the optimal performance of VAEs when exposed to diverse training data, emphasizing the importance of sufficient intermediate data between extreme ends. The intricacies of latent space dimensions also come under scrutiny, with challenges arising in creating a smaller latent space due to the complexities of representing data in N dimensions. The neural network tends to position data further apart, and incorporating additional information necessitates exponentially more data. Despite the suboptimal parameters employed in the creation and training process, the study concludes that they are sufficient to yield commendable results, showcasing the promising potential of MIDI-based GNNs with VAEs in pushing the boundaries of innovative music composition
Insights into pour point depressants: a brief review of their impact on the behavior of waxy crude oil
Get PDFA persistent challenge in the petroleum industry involves paraffin wax deposition from crude oils during low-temperature conditions, complicating pipeline flow assurance due to the intricate rheological behaviors exhibited by waxy crude oil gels. These behaviors include viscoelasticity, yield stress, and thixotropy, contributing to issues like flow reduction and pipeline obstruction, adversely affecting overall pipeline performance. Mitigating this problem requires a combination of mechanical and chemical processes, with pour point depressants (PPDs) emerging as an effective chemical solution. PPDs operate by interacting with wax crystals in the oil, disrupting their formation into a continuous network and preventing flow blockage at lower temperatures. The performance of PPDs depends on factors such as base oil type, PPD concentration, and application temperature. Recent advancements in PPDs focus on developing new polymers with enhanced performance and reduced environmental impact, including those derived from renewable resources, biodegradable PPDs, nano-structured PPDs, or hybrid PPDs. Polymeric additives, such as crystalline-amorphous copolymers, ethylene-vinyl acetate copolymers, comb polymers, and nanohybrids, are employed to modify wax crystallization behavior. Understanding the molecular structure of these additives, fluid composition, and pipeline conditions is crucial for optimizing formulations tailored to specific petroleum fluid compositions and transport conditions, ensuring effective wax deposition mitigation
A review of the literature on "determinants of insurers' capital structure"
Get PDFCapital structure plays an essential role in the financial decision-making of a company by strengthening financial performance and worth. This research aims to provide a literature review to identify the factors that affect insurance companies’ capital structure. The paper focuses on articles published from 2010 to 2021 on insurance companies’ capital structure in developing countries were reviewed. Three theories were identified as having common determinants: trade-off, pecking order, and agency cost. These independent determinants include seven firm-specific determinants: company size, age, profitability, growth, liquidity, tangibility, and risk along with two macroeconomic determinants: economic growth and inflation rate. The research found that the leverage ratio is the primary measurement of capital structure used as a dependent variable. Furthermore, previous studies have shown that the static data model was the most appropriate framework in most research. This research provides future researchers with information on understanding the determinants of capital structure in the insurance field
A model for classifying breast masses in ultrasound images
Get PDFThe most frequent type of cancer among women is breast cancer. Artificial intelligence (AI) researchers are developing automated systems to assist in the detection and classification of breast cancer. This study explores machine learning (ML) and deep learning (DL) as two AI methods for identifying benign and malignant breast tumors in ultrasound images. The investigation assesses the performance of various computer-aided detection and diagnosis (CAD) systems, which utilize either handcrafted features or deep features extracted from DL models. Furthermore, three models for CAD deep learning-based systems were implemented using convolutional neural networks (CNN), convolutional autoencoders (CAE), and deep features with CNN models, and compared with three traditional ML models based on handcrafted (texture) features. The results indicate that the deep features of the CNN model are promising, achieving a mean accuracy of 95% with a standard deviation of 1.1%
Factors influencing the intention to use m-commerce in Malaysian: an extended IS success model
Get PDFThe progress of mobile technology has undergone substantial development in recent years, leading to the emergence of new and creative ideas. This paper investigates the factors influencing consumers' intentions to use mobile commerce in Malaysia. The DeLone and McLean updated information system success model served as the basis for this study's proposed model. A convenience sampling method was employed to collect 310 surveys from smartphone owners who conduct mobile commerce activities in Malaysia. A “two-stage structural equation modeling” (SEM) technique assessed the research model and the study's assumptions. The findings revealed that “information quality”, “service quality”, “system quality”, and “trust” significantly influence consumers' “intention to use mobile commerce in Malaysia”. The findings further reveal that “system quality” is the strongest factor influencing the “intention to use mobile commerce in Malaysia”. Therefore, the research outcomes will benefit academicians, researchers, policymakers, and practitioners in the mobile commerce industry in Malaysia. To the best of the authors’ knowledge, this is the first empirical study that expanded the “information system success model” by including “trust” in the context of Malaysian mobile commerce users’ “intentions”. However, further research is recommended to explore the factors influencing consumers' “intention to use mobile commerce in Malaysia”
Effect of spacing, concentration of NaCl solution, and biasing of graphite electrodes towards conductometric sensor response
Get PDFAn electrical conductivity (EC) sensor is a conductometric sensor used to measure a solution's ability to transmit electrical charges. However, EC sensing accuracy and stability are not consistent due to many factors, such as gap spacing between electrodes, concentration of the solution, and electrical biassing. This study investigates the influence of the gap spacing between electrodes, the concentration of the solution, frequency, and voltage input applied to the EC sensor electrode on EC sensor measurement and provides insights into the relationship between these parameters and the sensor's performance using the voltage divider rule which is the simpler way to measure the conductivity of the solution. From this investigation, gap spacing between the electrodes, the concentration of the solution, frequency below 50 Hz, and voltage input have been found to directly affect the EC sensor measurement. However, there is no significant change in EC sensor measurement regarding the frequency applied to graphite electrodes when the frequency is above 50 Hz. The findings of this study highlight the complex interplay between the physical setup parameters and EC sensor measurement
Determining the retail sales strategies using association rule mining
Get PDFCompetitive competition in the retail industry requires retailers to maintain improvements and formulate accurate strategies to maintain their competitiveness. A small number of daily visitors visit retail store Y if compared to other retail stores, which leads to decreased store revenue due to the small number of products sold. Therefore, it is crucial to formulate the right business strategy to increase sales by utilizing customer shopping behavior derived from transaction data. The method used is association rule mining (ARM) with a frequent pattern growth (FP-growth) algorithm to determine consumer buying patterns. Data processing results generate five valid rules that meet the specified criteria for an association relationship. Utilization rules are acknowledged by determining retail sales strategies by recommending store layouts, shopping catalogs, and voucher discounts to attract customers
Adsorbent from coffee grounds to reduce cadmium concentration in leachate water
Get PDFWastewater contains a variety of heavy metals, one of which is cadmium (Cd) which causes adverse effects on both the environment and health. Coffee grounds can be used to reduce Cd in wastewater so this study aims to determine the reduction of Cd levels using coffee grounds. The type of research used is an experiment to see the decrease in Cd levels using carbonized and non-carbonized coffee grounds. The samples used were taken from the leachate water source at one of the landfills in Makassar City. Sampling was done using the grab sampling technique. The results showed that the use of uncarbonized and carbonized coffee grounds was effective in reducing Cd levels in Makassar City Tamangapa Landfill leachate water with an average decrease of 0.048 and 0.023 µg/L. Uncarbonized coffee grounds have a higher ability to reduce Cd in leachate so that it can be used as an alternative in the wastewater treatment process
Detecting and identifying occluded and camouflaged objects in low-illumination environments
Get PDFOne of the prevailing areas of contemporary research involves the differentiation and identification of diverse objects within a given scene through automated systems. The field of study under consideration presents a multitude of obstacles, including but not limited to issues such as diminished lighting conditions, occlusion, and camouflage. The captured image exhibits variations in illumination, resulting in uneven brightness, reduced contrast, and the presence of noise. The fundamental basis of computer vision algorithms lies in the process of extracting features from datasets and subsequently discerning these features through neural networks. The task of extracting distinct feature key points from images captured under low lighting conditions is exceedingly challenging. To address this issue, the present study seeks to employ deep learning models to implement image enhancement techniques specifically designed for low-light conditions. The primary emphasis lies in obtaining key feature points that are differentiable, thereby enabling the utilization of this annotated data for specific tasks such as object detection. The task of identifying occluded and camouflaged objects has been successfully accomplished, yielding an impressive accuracy rate of 93% in total. The mean average precision has been achieved as 85% which is reasonably high compared to many earlier works
Effect of cutting process using cutting insert of grade UTi20T
Get PDFIn the metal-cutting industry, the precision of the metal-cutting process is of paramount importance. Errors in the metal-cutting process not only lead to damage to the cutting tool but also result in the production of low-quality materials. The incorporation of insert materials in the cutting process is aimed at maintaining cutting precision and achieving superior results. This research seeks to investigate the impact of the cutting process utilizing grade KC5410 cutting insert under minimum quantity lubrication (MQL) Conditions. In this study, machining tests were conducted using the ALPHA 1350S 2-axis computer numerical control (CNC) lathe machine under MQL conditions, employing cutting tool inserts UTi20T supplied by Mitsubishi. Two types of tools were utilized in the cutting process, namely UTi20T. Critical aspects such as cutting force, total power consumption, surface roughness, and tool life were analyzed to provide comprehensive insights into the efficiency of the cutting process. The findings of this study significantly contribute to the understanding of how the integration of Grade KC5410 cutting inserts under MQL conditions can enhance the overall efficiency of metal-cutting operations. The successful machinability assessment was conducted by implementing sustainable machining practices