Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

Fuzzy time series analysis and prediction using swarm optimized hybrid model.

Time series forecasting has an extensive trajectory record in the fields of business, economics, energy, population dynamics, tourism, etc. where factor models, neural network models, Bayesian models are exceedingly applied for effective prediction. It has been exemplified in numerous forecasting surveys that finding an individual forecasting model to achieve the best performances for all potential situations is inadequate. Moreover, modern research endeavour has focused on a deeper understanding of the grounds. Rather than aim for designing a single superior model, it focused on the forecasting methods that are effective under certain situations. For instance, due to the qualitative nature of forecasting, a business can come up with diverse scenarios depending on the interpretation of data. Therefore, the organizations never rely on any individual forecasting model solely, rather focused on sets of individual models to attain the best possible knowledge of the future. The time series forecasting model has a great impact in terms of prediction. Many forecasting models related to fuzzy time series were proposed in the past decades. These models were widely applied to various problem domains, especially in dealing with forecasting problems where historical data are linguistic values. A hybrid forecasting method can be effective to improve forecast accuracy by merging sets of the individual forecasting models. Numerous hybrid forecasting models have been proposed last couple of years that combined fuzzy time series with the evolutionary algorithms, but the performance of the models is not quite satisfactory. In this research, a novel hybrid fuzzy time series forecasting model is proposed that used the historical data as the universe of discourse and the automatic clustering algorithm to cluster the universe of discourse by adjusting the clusters into intervals. Furthermore, the particle swarm optimization algorithm is also examined to improve forecasted accuracy. The proposed method is considered to forecast student enrolment of the University of Alabama. The model achieves a significant improvement in forecast accuracy as compared to state-of-the-art hybrid fuzzy time series forecasting models. It is obvious from the literature that no forecasting technique is appropriate for all situations. There is substantial evidence to demonstrate that combining individual forecasts produces gains in forecasting accuracy. The addition of quantitative forecasts to qualitative forecasts may reduce forecast accuracy. Individual forecasts are combined based on either the simple arithmetic average method or an artificial neural network. Research has not yet revealed the conditions for the optimal forecast combinations. This thesis provides a few contributions to enhance the existing combination model. A set of Individual forecasting models is used to form a novel combination forecasting model based on the characteristics of resulting forecasts. All methods derived in this thesis are thoroughly tested on several standard datasets. The related characteristics of the resulting forecasts are observed to have different error decompositions both for hybrid and combination forecasting model. Advanced combination structures are investigated to take advantage of the knowledge of the forecast generation processes

Dynamic load modeling for bulk load-using synchrophasors with wide area measurement system for smart grid real-time load monitoring and optimization

Bulk data modeling in a smart grid dynamic network has been performed using an automated load modeling tool (ALMT), an on-load tap changer, and exponential dynamic load modeling. However, studies have observed that a small parameter variation may lead to considerable variations in measuring grid big data. Therefore, this study presents dynamic real-time load modeling, monitoring, and optimization method for the bulk load. The case study was conducted on Sarawak Energy Berhad (SEB), Malaysia. The grid system’s real-time data and load modeling achieved the objectives. Dynamic load model was achieved by using load response in MATLAB Simulink environment. This paper also includes new parameter estimations of the load composition at the selected bus. The simulation results of load models were compared with the recorded data by applying an event of bus tripping time interval. The Least Square Error Method was used to converge the estimated parameter values on load composition and compared with the actual recorded data until optimized load models were achieved. This work is a precious and significant contribution to utility research to identify, monitor, and optimize the most appropriate representation of system loads

Distributed denial-of-service attack detection for smart grid wide area measurement system: A hybrid machine learning technique

Smart grid networks face several cyber-attacks, where distributed denial-of-service (DDoS) attacks distract the grid network. The synchrophasor technique protects the wide-area measurement system (WAMS) from the complex problem and addresses different issues in a grid. The DDoS attack detection strategy is complicated due to attack complexities, vendor specifications, and communication standard protocols. Attacker target phasor measurement unit (PMU) data on the phasor data concentrator (PDC) database in WAMS. However, during the cyber-attack, the framework ensures the end application uses the normal PDC datastream. The proposed attack detection technique efficiently verifies PMU-generated data in WAMS. However, numerous machine learning algorithms are used to detect DDoS attacks, but the best detection model is still given open choices. The motivation of this study: (a) which machine learning algorithm will be suitable for DDoS attack detection and (b) what would be the accuracy of training algorithms. This study presents a machine learning-based hybrid technique that achieves 83.23% accuracy. Python compiler is used to execute the proposed model, and the result shows that the proposed detection approach efficiently improves the DDoS attack detection accuracy

DDoS: Distributed denial of service attack in communication standard vulnerabilities in smart grid applications and cyber security with recent developments

Smart grid system is evident with control technologies and digital communications systems. The cyber–physical system is a critical infrastructure connected with complex drives and devices. The modern smart grid’s prominent communication standard adopts high scalability, supports numerous communication devices’ input/outputs, and has multi-vendor interoperability. Cyber-attack leads to an unstable power grid system and enormous economic loss. Throughout different cyber-attack, distributed denial of service attack is mostly destructive to smart grid infrastructure. This paper investigates smart grid cyber security systems and the communication vulnerabilities of other communication protocols. A comprehensive study on distributed denial of service attack techniques and detection approaches is present. Finally, examine a new hybrid machine learning-based distributed denial of service attack detection technique for a sustainable smart grid system

Voltage equalization circuit for retired batteries for energy storage applications

In this paper, an active equalization circuit based on resonant converter is presented. This equalization circuit has been proposed to equalize the direct cell-to-cell voltage in a string. All electrochemical energy storage devices are connected in series. Using this equalization circuit energy transfer from higher energy and charge capacitive cell to lower energy and charge cell in the string. All MOSFET switches are operated by complementary Pulse-Width Modulation (PWM) signal. The working principle of this equalization circuit like that of a switches-capacitor equalization circuit. In this circuit, a single Inductor (L) capacitor (C) energy carrier and bidirectional low voltage MOSFET switches are used so that it can recover maximum energy, reduce conduction loss, and improve the switching loss drawback, reduce the equalization time duration between two cells and achieved zero voltage gap. This equalization circuit is bidirectional and operates under the three modes, namely, charging, discharging, and relaxation mode. The proposed circuit details, the working principle, and the mathematical analyzes are presented. The simulation results are validated by the demonstration of an experimental prototype hardware testbed. The experimental results based on lithium-ion, lead–acid, and super-capacitor are presented. This equalization circuit is low cost and occupies little space with efficiencies of 96%, 94.2%, and 84.43% for Li-ion, lead–acid and super-capacitor respectively

Voltage equalization circuit for retired batteries for energy storage applications

In this paper, an active equalization circuit based on resonant converter is presented. This equalization circuit has been proposed to equalize the direct cell-to-cell voltage in a string. All electrochemical energy storage devices are connected in series. Using this equalization circuit energy transfer from higher energy and charge capacitive cell to lower energy and charge cell in the string. All MOSFET switches are operated by complementary Pulse-Width Modulation (PWM) signal. The working principle of this equalization circuit like that of a switches-capacitor equalization circuit. In this circuit, a single Inductor (L) capacitor (C) energy carrier and bidirectional low voltage MOSFET switches are used so that it can recover maximum energy, reduce conduction loss, and improve the switching loss drawback, reduce the equalization time duration between two cells and achieved zero voltage gap. This equalization circuit is bidirectional and operates under the three modes, namely, charging, discharging, and relaxation mode. The proposed circuit details, the working principle, and the mathematical analyzes are presented. The simulation results are validated by the demonstration of an experimental prototype hardware testbed. The experimental results based on lithium-ion, lead–acid, and super-capacitor are presented. This equalization circuit is low cost and occupies little space with efficiencies of 96%, 94.2%, and 84.43% for Li-ion, lead–acid and super-capacitor respectively