The research is to study the patient's length of stay in intensive care unit (ICU) admissions each year with their cost and health expenditure. Forecasting in the clinical Decision Support System (DSS) is being developed in the study to anticipate and enhance hospital equipment for patients' health analysis. The most crucial examination is to give appropriate technology and quality drugs to analyze the patient's health, which is then recorded in electronic medical records. To achieve the best exactness, this research study employed the innovative Feed Forward Neural Network and Deep Belief Network to accomplish the operations. The study gathered 47 samples from two groups of calculation with a G-power of 80% and their Patient electronic health records investigations were collected from a variety of online sources, with recent research findings and a 0.05% threshold, confidence interval of 95% mean and standard deviation. The unique Feed Forward Neural Network approach obtained 93.65% accuracy in predicting ICU analysis; consequently, The Deep Belief Network method in machine learning should be upgraded for improved accuracy in health prediction in this study. This study discovered a 90.07% accuracy for ICU analysis utilizing the Deep Belief Network method, with a significant value of two-tailed tests of 0.006 (p0.05) and a 95% confidence range. This study reveals that the innovative Feed Forward Neural Network method outperforms the Deep Belief Network algorithm for ICU analysis of patients.
In this paper has given Archimedean exponential operation law of intuitionistic multi-fuzzy (A_EOL_IMFN) numbers and intuitionistic multi-fuzzy sets (IMFS)which are valuable enhancements to the current intuitionistic multifuzzy collection strategies. Then, we looked into a number of the properties of A_EOL_IMFN and IMFS and gave an example to illustrate how the corresponding aggregation method can be used
Most of the applications in industries require medium and high voltage so hence multilevel inverters (MLI) are gaining significant importance [1]. This paper gives an insight of different modulation strategies such as phase shift, level shift used for single phase five level cascaded H-bridge (CHB) MLI for reducing total harmonic distortions with high-power handling capability by increasing its voltage level. Also, emphasis more on multicarrier PWM strategy for the CHB MLI. A simulation model of five level cascaded H-bridge multilevel inverter is developed using MATLAB/SIMULINK and its total harmonic distortions has been analyzed.
In this paper to get the best possible result of game theory, the opinion of more than one expert has been taken. An effective method for solving bi-matrix game with triangular fuzzy payoffs based on the opinion of more than one expert is proposed. An aggregation operator has been used to aggregate the opinion of different experts. The aggregated fuzzy game matrix has been solved by using no-linear programming method in which payoffs are Triangular Fuzzy Numbers (TFNs). Furthermore, the proposed method reduces the impact of biasness on the final results given by single expert. Moreover, this method will be more applicable to solve any type of optimization problems. A Numerical example has given to show the applicability and superiority of the proposed method.
Emotion is a behavioral phenomenon that involves many levels of neural as well as chemical integration. Though this parameter has huge variations, it is crucial for us to identify them as emotions act as roots to mental health. Given that music happens to be a way to treat mental illness, alongside clinical medications, it is very much essential to tune the chords of music along with one's emotions. In the following approach, a user- specific data set was created to predict the emotions by making use of facial recognition. Furthermore, the predicted emotion is made use of to play a song from the user's playlist, the same has been achieved with 100% accuracy by making use of the SVM classification algorithm, along with PCA and a polynomial kernel.
Several industries are currently seeing significant growth in the study of bipolar fuzzy sets. Bipolar fuzzy matrices are also a highly recognised issue in several academic displines such as multi-criteria decision making, information science, engineering and quantum computing. In this article, define value matrix and score matrix of bipolar fuzzy matrix. Using the operation bounded sum with value of BFM, obtain an real life application which enables the land sliding, flood, cultivation, power cut and transport problem. Illustrate this idea with an example that helps in finding the most affected area in flood damage.
The colonoscopy is the most reliable method for monitoring the digestive tract. Colonography can detect a variety of conditions, including polyps in the colon. Despite advancements in technology, many colorectal polyps still go undetected in the early stages. When polyps are detected at an early stage, the severity of the disease can be mitigated with the use of polyp segmentation. Coherence transfer and contrast-limited adaptive histogram equalization were two of the image pre-processing approaches used by the researchers in this work to address these issues. Following this, a U-Net based deep learning segmentation model was utilized to isolate the polyp in the image. Using a bottleneck attention module and a residual network, the BAMRes encoder-decoder component of the Unet framework's architecture is combined with feature concatenation on the same layer. With the publicly accessible Kvasir-SEG dataset, we were able to empirically validate the model, which yielded a dice coefficient of 92.27%.
Cyber security aims to prevent the illegal use of such things. By leveraging existing software weaknesses and bugs, hackers break into networks using software systems and human tactics. Offensive tactics, vulnerability ideas, and defensive methods must be carefully studied to ensure adequate protection.0 Good results could be achieved by creating custom updates for each organization and repairing technological flaws by being informed of the safety software of each system. The study also found that some other intrusion protection measures, such as Windows Defender, virus protection, and routers, were insufficient. Therefore, applying the Nmap and Metasploit technologies, this research examines several penetration assessment techniques to identify sensitive endpoints and applications. A virtualized system with various Windows and Linux OS versions was used as a testing phase
The aim of the work was to test the freshness of apples using non-destructive sensing methods. Among the various electrical methods available, the dielectric capacitance method was chosen for this study. The experimental set up for the capacitance measurement was developed and it consists of two aluminum plates which act as parallel plates, and a stand which acts as a holding system. The apple is placed between the two aluminum plates, and an input voltage of 10 V dc is applied to the plates. The entire test is carried out at ambient temperature (30±3 °C). The capacitance value was recorded and a correlation between the capacitance and the condition of fruit was obtained.
The basic functioning of heart can be read through Electrocardiogram (ECG) Signal, this signal gives an idea whether the functioning of heart is normal or abnormal and type abnormality can also be identified, which helps to diagnose the patients in time. This work investigates a deep-learning model using 2DCNN to classify various category of ECG signal. This proposed CNN model is trained and tested to classify three different classes of heart arrhythmia such as cardiac arrhythmia (ARR), congestive heart failure (CHF) and normal sinus rhythms (NSR). The time domain ECG signal is preprocessed and further it is transformed in to time-frequency scalogram by utilizing continuous wavelet transform (CWT), these scalogram is remodeled and saved as RGB images with necessary dimensions. Later these converted RGB images are fed to the input of various 2DCNN models such as alexnet, vgg16, squeezenet and googlenet to classify arrhythmia type. ECG Recordings from MIT BIH database were chosen and used for training and testing dataset. The performance of proposed scheme is evaluated on various CNN networks, a reasonable classification accuracy of 99.33 % was acheived by alex net.
The future iteration of cellular networking systems will be very dynamical and variable due to the incorporation of telecommunications with varying sizes, diverse wireless access, and connectivity resources. Emergent applications e.g., machine-to-machines communications, industrial automations and autonomous driving, have more complex requirements based on latency and reliability. The complex requirements pose critical issues to resource orchestration, management of networks, and architecture design in next-generation wireless networking systems. Beginning with demonstrating these issues, this research contributions focusses on providing a critical understanding on the general infrastructure of the next-generation wireless networking systems and research directions under this general infrastructure. The first part of our discussion introduces infrastructure centered on network slicing and specifies location and the reason as to why AI should be deployed there. Generally, this research defines the potentials and benefits of artificial intelligence-based methods in the study of next-generation wireless networking systems.
In cloud computing environment DDoS attacks are continually evolving with intelligent strategies. Low-rate DDoS attack is one such strategy that make it difficult to detect attack. At the same time, cloud infrastructure is also evolving rapidly. Container based technology enables cloud computing to have lightweight approaches in resource utilization and flexibility in scaling services. The existing DDoS attack detection methods used in cloud computing are not adequate when adversaries employ the modality known low-rate DDoS attack. There is need for an approach that not only detects the attack but also defeat the attack as much as possible. Towards this end, in this paper, we proposed a framework named Low-Rate DDoS Attack Detection Framework (LRDADF). Since low-rate DDoS attacks are difficult to be defeated, we proposed a mathematical model to realize mitigation strategy besides employing deep learning methods to have effective means of detecting such attacks. We proposed an algorithm named Hybrid Approach for Low-Rate DDoS Detection (HA-LRDD). The algorithm uses an Artificial Intelligence (AI) enabled methods comprising of deep Convolutional Neural Network (CNN) and deep autoencoder. Another algorithm known as Dynamic Low-Rate DDoS Mitigation (DLDM) is used to minimize the effect of the attack after detection or even defeat the attack by ensuring the smooth functioning of cloud infrastructure which is under attack. Extensive simulation study revealed that the proposed framework is able to detect low-rate DDoS attacks and also mitigate the attacks to ensure there is acceptable quality of service in cloud computing environments.
The Internet of things (IoT) becomes a new era for the imminent industry to provide an intelligent environment to control systems in real-time. Considerably, for accomplishing the delay analysis in the IoT system, it is necessary to consider the appropriate control system for the precise identification of IoT terminals and the efficient regulation of their access to the network. Therefore, the study considers about different control strategies such as PID (proportional integral derivative) $2^{\text{nd}}$ process of Ziegler's Nichols, and PID Pole placement technique for finding the critical delay values under an IoT network environment. The control system is designed by considering different network constraints. Based on the network constraints and the controllers, a significant model is designed for calculating the maximum delay concerning sensor and controller along with controller and things. The controllers are premeditated using the transfer function of the particular plant i.e thing. The planned method designed here is to come across the value of delay via different design techniques using a PID controller. Finally, simulation results confirm the effectiveness of the proposed controller under MATLAB/Simulink environment.
Since they are now being used in more contexts and fields, the growth of artificial intelligence and machine learning technologies has given judges new alternatives. Since it entails determining the kind and gravity of illegal action, forming judgments is an important responsibility for judges. Intelligent classification techniques are crucial in the decision-making process when it comes to judgment categorization, which is a multi-label text categorization assignment. The problem for the classification must be described before model design and development can proceed. In the field of judicial administration, these systems have been studied in various ways. For many reasons, such as the use of standard feature sets combined with machine learning methods and the use of sparse large data sets, court case judgments cannot be examined using prior judicial information. Furthermore, due to bad predictor selections and a lack of domain specialists, ML models are less successful at classifying and forecasting civil suits. LSTM is used to classify motor vehicle accident judgments to address these challenges. Convolutional Neural Networks (CNNs) were used to analyze court judgments effectively in the proposed Bi-LSTM model by preserving long-term interrelationships. The suggested method offers both optimal feature extraction and Bi-LSTM and CNN layering for categorizing motor vehicle accident verdicts from previous judicial recordings.
A Single Complementary Slotted Ring Resonator (CSRR) based Co-Planar Waveguide (CPW) antenna operating between 3.4 - 4 GHz is presented in this article. The proposed CSRR inspired antenna is printed on 1.6 mm thick widely used FR4 substrate. As communication technology advances and 5G telecommunications become more widespread, there has been an increase in the need for antennas that are economical, thin, compact, and broad-band in frequency range. In light of this, the research presented here proposes an antenna that is inspired by metamaterials and has operating frequencies that encompass the 5G operating bands. The antenna achieves overall efficiency up to 88–90% with substantial gain around 2.8 - 3.2 dBi in the desired spectrum of operation. Besides, the antenna has omnidirectional radiation pattern which helps to cover all ranges around 5G devices. The results evident that the antenna proposed in this research could be a right contender for future 5G cellular communications.
Nowadays, pulmonary vascular disorders, which might result in pulmonary emboli or pulmonary hypertension, affect majority of patients. To diagnose alterations in vascular trees, a manual and automatic study of the ill person's chest CT imaging is performed. The manual analysis of CTPA scans is time-consuming, non-standardized, and exhausting. Therefore, semi-automatic and automatic vascular tree separation in CTPA scans is increasingly used, which enables medical professionals to precisely identify aberrant conditions. Different techniques for pulmonary vascular disease identification and classification using deep learning and machine learning methods have been carried out recently. Here we are using deep learning algorithms like Resnet50,Densenet121 and VGG19 for automatic classification of pulmonary vessels for detecting pulmonary diseases with increased accuracy.
Errors are part and parcel of Computer Vision applications like Optical Character Recognition(OCR). Unfortunately, the noise produced by these errors only proliferates further down the stages of Natural Language Processing pipelines. Among the reported works for post-processing of OCR texts, most involved Lexical approaches, Feature-based machine learning models, Merging OCR outputs, or using other language Models. This paper proposes an Isolated-Word-based approach to detect OCR errors that rely on the principles of the Artificial Immune System(AIS). The problem of OCR error detection is treated as a classification problem where OCR errors are treated as pathogens and correct words as host cells. The Negative Selection Algorithm is used to classify any new token as an OCR error (pathogen) or good term (host cell). A series of experiments illustrate that it is possible to construct such a system to help identify OCR errors independent of the language.
Malaria, caused by Plasmodium parasites in the bloodstream spread by infected mosquitoes, is a highly severe and sometimes deadly disease. Image analysis and machine learning can enhance diagnosis by quantifying parasitemia on blood slides. The building of an autonomous, accurate, and effective model can significantly reduce the need for trained laborers. This article discusses computer-assisted approaches for finding malaria parasites in blood smear images. These procedures consist of obtaining the dataset, preprocessing the images, segmenting the red blood cells, extracting and choosing features, and classifying the images. The approach is based on well-known Convolutional neural network (CNN) models of Plasmodium parasites and erythrocytes. The trained CNN and VGG-19 are given images of infected and uninfected erythrocytes from the same dataset. VGG 19 gives 96% detection accuracy where CNN achieves 94%.
Solar Power Generation is being done extensively across the globe. The solar irradiation is variable with time and the irradiation change can be easily observed form sunrise to sunset. So there is a need to operate the PV panel at the maximum power operating point and this can be easily done using P&O technique. P&O algorithm may take an incorrect decision when the insolation increases or decreases and a shift occur in the tracking. The duty cycle has to be corrected when change in insolation occurs so that we can reduce the shift. To achieve this, an improved P&O technique using changes in current is proposed in this paper. A Single Ended Primary Inductance Converter (SEPIC) is used in the paper to implement this technique. The SEPIC Parameter calculations are also presented in this paper. The simulation results on traditional and improved P&O techniques showed that the proposed technique has better tracking and shift reduction. The simulation is performed in MATLAB/Simulink environment. The model proposed in this paper can be used for charging the batteries.
