International Journal of Informatics and Communication Technology (IJ-ICT)
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Spth-FCM: decision support tool for speech therapist based on fuzzy cognitive mapping
Get PDFThe development and integration of medical information systems into a unified information space is a significant focus in the field of information technologies. It is essential to develop decision support systems (DSS) to enhance the effectiveness of medical and diagnostic procedures. This article presents a novel decision support tool for speech therapists, which is based on fuzzy cognitive maps (FCM). The latter is a method of modeling complex systems using knowledge of human existence and experience. The proposed tool is composed of three phases. The first phase focuses on entering patient information into the graphical interface developed in JAVA based on the most precise observations. An FCM will be automatically constructed, describing the type of disorder and the patient’s case during the second phase. Finally, in the third phase, FCM-based scenarios were built during the execution of the inference process under FCM expert. The system is presented and demonstrated using a real cases study for eight weeks. The results show that the tool makes it possible to display, guide, assist, and confirm the medical decision of the speech therapist for an appropriate diagnosis and treatment
Electric load forecasting using ARIMA model for time series data
Get PDFAny country's economic progress is heavily reliant on its power infrastructure, network, and availability, as energy has become an essential component of daily living in today's globe. Electricity's distinctive quality is that it cannot be stored in huge quantities, which explains why global demand for home and commercial electricity has grown at an astonishing rate. On the other hand, electricity costs have varied in recent years, and there is insufficient electricity output to meet global and local demand. The solution is a series of case studies designed to forecast future residential and commercial electricity demand so that power producers, transformers, distributors, and suppliers may efficiently plan and encourage energy savings for consumers. However, load prognosticasting has been one of the most difficult issues confronting the energy business since the inception of electricity. This study covers a new one–dimensional approach algorithm that is essential for the creation of a short–term load prognosticasting module for distribution system design and operation. It has numerous operations, including energy purchase, generation, and infrastructure construction. We have numerous time series forecasting methods of which autoregressive integrated moving average (ARIMA) outperforms the others. The auto–regressive integrated moving average model, or ARIMA, outperforms all other techniques for load forecasting
Optimized ultra-low power and reduced delay GNR Ternary SRAM using a 7-transistor architecture
Get PDFGreater need and evolution in electronics require a memory device that can go with a decreased power delay, SRAM plays an important role as a storage element in digital circuit design. Power and delay are vital problems faced by today’s RAM technology. It is necessary to lessen the power and increase the speed. There is a need to reduce power utilization and time delay. The proposed method is seen in the Electronics technical tool H-Spice technology. The technique proposed on DRG 7T- transistors SRAM consumes less power and delay. After the analysis and enhancement of the circuit, this approach gives the power delay product of the graphene nanoribbon (GNR) 7T SRAM as 80% at 0.7 V, 59% at 0.8 V, 34 % at 0.9 V, which is much less when compared to conventional SRAM power delay product
Classification of breast cancer using a precise deep learning model architecture
Get PDFBreast cancer is an important topic in medical image analysis because it is a high-risk disease and the leading cause of death in women. Early detection of breast cancer improves treatment outcomes, which can be achieved by identifying it using mammography images. Computer-aided diagnostic systems detect and classify medical images of breast lesions, allowing radiologists to make accurate diagnoses. Deep learning algorithms improved the performance of these diagnoses systems. We utilized efficient deep learning approaches to propose a system that can detect breast cancer in mammograms. The proposed approach adopted relies on two main elements: improving image contrast to enhance marginal information and extracting discriminatory features sufficient to improve overall classification quality, these improvements achieved based on a new model from scratch to focus on enhancing the accuracy and reliability of breast cancer detection. The model trained on the digital database for screening mammography (DDSM) dataset and compared with different convolutional neural network (CNN) models, namely EfficientNetB1, EfficientNetB5, ResNet-50, and ResNet101. Moreover, to enhance the feature selection process, we have integrated adam optimizer in our methodology. In evaluation, the proposed method achieved 96.5% accuracy across the dataset. These results show the effectiveness of this method in identifying breast cancer through images
Incremental prioritization using an iterative model for smallscale systems
Get PDFTo improve customer satisfaction during the requirement engineering process and create higher consistency in the developed software, there is a growing trend toward the development and delivery of software in an incremental manner. This paper introduces a novel approach to prioritizing the initial development of core subsystems. This prioritization ensures that the most critical subsystems, which contribute significantly to the project’s overall success, are addressed first. Our method involves employing an incremental model with iterative modeling, where each subsystem is assigned a profitability score ranging from 1 to 10. The iterative model is then utilized to identify the most suitable subsystem for the next development stage. The results of our study indicate that utilizing the total profit weight in conjunction with the iterative model effectively identifies the central subsystem of the entire project. This approach proves to be the optimal starting point for development, helping streamline the process and contribute to a more efficient software delivery strategy
Performance analysis of LDPC codes in MIMO-OFDM for next generation wireless systems
Get PDFFifth Generation communication systems overcome the limitations of the fourth-generation systems and ensure improved data rates, lower latency, and higher connection density. 5G technology has the potential to unlock new internet of things (IoT) applications by utilizing the technologies such as multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), and Li-Fi. Low density parity check (LDPC) and polar codes are being preferred for data and control channels respectively in 5G systems as these coding techniques offer good error-detection and correction along with reduced latency. Morever, LDPC codes are power efficient. This paper aims to analyze the bit error rate (BER) performance of LDPC codes in MIMO-OFDM System for different modulation schemes. LDPC codes improve the BER performance of OFDM and MIMO-OFDM systems. MIMO-OFDM systems deliver better BER performance over OFDM system
Malware detection using Gini, Simpson diversity, and Shannon-Wiener indexes
Get PDFThe increasing number of malware attacks poses a significant challenge to cyber security. This paper proposes a methodology for static malware analysis using biodiveristy-inspired metrics that is Gini coefficient, Simpson diversity, and Shannon-Wiener index for malware detection. These metrics are used to build the structural feature representation on the raw binary file as the feature space. The effectiveness of these metrics are evaluated using multilayer perceptron (MLP) neural network and extreme gradient boosting (XGBoost) models. A deterministic algorithm is used to generate these features that represent the feature signature of the executable file. Additionally, we investigated the effectiveness of different byte sizes as the input feature for these two classifiers. According to the results, Gini coefficient with on chunk size of 128 has successfully achieved average F1 score of more than 98.7% by using XGBoost model
Consumer behavior switching from human agents to chatbots in the health service industry
Get PDFArtificial intelligence (AI) technology is used in organizations to replace human services with technology, altering customer service experiences. Only a limited number of studies have explored how consumers change their behavior from human-assisted to technology-assisted services when using AI in frontline and specialty healthcare services. This study examined the elements that impact consumers’ transition from human agents to AI-based conversational agents using the push-pull mooring framework. Data from 147 healthcare users was evaluated using structural equation modeling. The data indicates that push effects, specifically adaptability, have a negative impact on switching behavior, while pull effects, such as responsiveness and accessibility, have a positive impact on the switching behavior of customers
Techniques of deep learning neural network-based building feature extraction from remote sensing images: a survey
Get PDFRecently, due to earthquake disaster, many people have lost their lives and homes, and not able to settle to new locations immediately. Therefore, a framework or a plan should be ready to immediately relocate the people to different locations or do resettlement. Much research has been done in this field but still there are problems of identifying clear building boundaries, rectangular houses, due to the problem of different shapes of the buildings. These techniques were explored for identification of clear building boundaries, rectangular houses, buildings which are more highlighted and smaller size buildings for pre-disaster and post-disaster building extraction scenarios. In this survey of building extraction techniques, most of the approach is training the network, second approach is refining the trained output features, running the trained samples on the predefined models of neural network. Several issues and their assessment are studied in these techniques. These are beneficial to the various researchers for different building extractions
BER and power consumption minimization through optimization in wireless cellular network
Get PDFQuality of service (QoS) of wireless cellular networks affect due to more power consumption, maximum bit error rate (BER), minimum throughput and improper resource allocation. Improvement in QoS can be done by reducing power consumption, BER and enhancing throughput. Hence there is a need to address the approaches for reduction in power consumption, BER, enhancement in throughput and proper resource allocation through different schemes. In this paper grey wolf optimization (GWO) technique is investigated with different database functions and Its outcome is contrasted with alternative methods like particle swarm optimization (PSO) and genetic algorithm (GA), It is evident that the GWO algorithm performs exceptionally well in terms of BER and power consumption minimization than the other techniques. Hence the QoS of the wireless cellular network will not affect due to minimization of the BER and power consumption through our proposed scheme