Journal of Science & Technology (JST)
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Bi-Modal Oil Temperature Forecasting in Electrical Transformers using a Hybrid of Transformer, CNN and Bi-LSTM
Get PDFPower consumption prediction is a tough task because of its fluctuating nature. If the expected demand is excessively high in comparison to the existing demand, the transformer may damage. Predicting the temperature of transformer oil is an efficient approach to verify the transformer's safety status. As a result, in this study, we offer a bimodal architecture for predicting oil temperature given a sequence of prior temperatures. Our model was tested using the Ettm1, Ettm2, and Etth1 datasets and achieved an RMSE of 0.41375, MAE of 0.3031 and MAPE of 8.292% on Ettm1 test dataset, an RMSE of 0.4105, MAE 0.3090 and MAPE of 6.678% on Ettm2 test dataset and an RMSE of 0.6762, MAE 0.4690 and MAPE of 11.23% on Etth1 test dataset
MQTT-Based Distributed Brokers Internet of Things Platform
Get PDFSeveral different types of services for the Internet of Things across large geographic areas have been set up. Most IoT services need the transmission of a large number of very small data packets across long distance networks. This calls for a simplification of the transfer processes. MQ Telemetry Transport is a viable contender for usage as a transfer mechanism (MQTT). In this work, we suggest a virtual ring design for a distributed MQTT broker. ISO/IEC JTC 1/SC 41 describes an IoT Data Exchange Platform, and this design follows those specifications. This paper describes the functionality of a distributed broker architecture that use a virtual ring network for realtime communication, and it demonstrates the architecture's superiority via a performance study using queuing models. Key word
Some Morphometric Aspects of Drainage Pattern of Newaj Watershed, Rajgarh District, M.P.: Hydrological Implications.
Get PDFMorphometric analysis of drainage network in the Newaj Watershed, in the Rajgarh district of M.P. has been carried out using Spatial Technology with a view to understand the hydrological condition of the basin. Stream ordering carried out for the analysis of bifurcation ratio, drainage texture, drainage density, relief ratio and circulatory ratio. The inferences drawn from this analysis indicate that the area has not suffered any major deformation. As the value of bifurcation ratio is low the result of analysis of these parameters also indicate that the drainage texture is fine to medium and drainage density 2.55 km / sq. km which is low indication moderate run off. The runoff and infiltration which signify that the area needs some measures to be taken to maintain and to improve groundwater conditions of area for sustainable development
Hydrogeological condition in the Newaj Watershed using thematic layers generated by the spatial Data.
Get PDFThe present study is an attempt to describe the hydrogeological condition of Newaj Watershed of Rajgarh District, Madhya Pradesh, by the themes generated using spatial data such as geology, geomorphology, lineament, drainage, and landuse / landcover maps. The geological map corealeted with field data show that the basic volcanic rocks present in the area have no primary porosity but because of weathering and due to presence of fractures and joints a secondary porosity has been developed which favours the movement and accumulation under the surface. Similarly presence of surface landform also favour the groundwater conditions as the landform like Pediplain, pediments and deeply weathered plateau seems primarily favourable site for groundwater percolation and accumulation. Lineaments density, drainage texture and present landuse/landcover also favour the groundwater movement and accumulation hence, the analysis of these themes give primary information regarding hydrological study which may be used before detailed investigations for groundwater
Effective And Efficient Detection Of Phishing Emails Using Machine Learning
Get PDFEmails are widely used for personal and professional communication,often involving the transmission of sensitive information like banking details,credit reports,and login data.Consequently,these emails become valuable targets for cyber criminals who seek to exploit such knowledge for their own malicious purposes.Phishing, a deceptive technique employed by these individuals,involves impersonating well-known sources to deceive and extract sensitive information from unsuspecting individuals.The sender of a phishing email uses false pretenses to persuade recipients into disclosed personal information.In this work,the detection of phishing emails is learning methods to categorize emails as either genuine or phishing attempts.LMT classifiers have proven highly effective in accurately classifying emails,achieving optimal accuracy in email classification tasks
SPECTROSCOPIC MARK AND QUANTUM CHEMICAL INVESTIGATION FOR PHOTONIC/BIOLOGICAL APPLICATIONS
Get PDFLGHCl - Semi-organic single crystals of L-Glutamic acid hydrochloride, optical material have been grown by the solvent evaporation route. Powder X-ray diffraction and FT-IR analysis confirmed the formation of LGHCl crystals. The optical second harmonic generation (SHG) conversion efficiency of LGHCl was determined by the Kurtz-Perry powder technique, and it is found to be 1.62 times that of potassium dihydrogen phosphate. Through the SHG dependency of average particle sizes, the relative SHG efficiency of the crystal and the phase matching characteristic of the crystal were explored. The dielectric behaviour of LGHCl single crystal as a function of temperature was examined (300–350 K). The crystal's photoconductive properties were investigated in order to determine its photocurrent and dark current responses. The Vickers microharness analysis at room temperature was used to evaluate the mechanical hardness of the generated LGHCl single crystal. Natural bond orbital (NBO) analysis was used to investigate the molecule's stability as a result of hyperconjugation and charge delocalization interaction. Furthermore, the total and partial density of states in the title compound was also determined. The obtained results indicate that the molecule is thermodynamically and optically stable, with a hyperpolarizability that is comparable to other molecules in its class
A METHOD OF PREDICTING WITH MODELLING OF CYBER HACKING AND BREACHES
Get PDFAnalyzing cyber incident data sets is an important method for deepening our understanding of the evolution of the threat situation. This is a relatively new research topic, and many studies remain to be done. In this paper, we report a statistical analysis of a breach incident data set corresponding to 12 years (2005–2017) of cyber hacking activities that include malware attacks. We show that, in contrast to the findings reported in the literature, both hacking breach incident inter-arrival times and breach sizes should be modeled by stochastic processes, rather Than by distributions because they exhibit autocorrelations. Then, we propose particular stochastic process models to, respectively, fit the inter-arrival times and the breach sizes. We also show that these models can predict the inter-arrival times and the breach sizes. In order to get deeper insights into the evolution of hacking breach incidents, we conduct both qualitative and quantitative trend analyses on the data set. We draw a set of cyber security insights, including that the threat of cyber hacks is indeed getting worse in terms of their frequency, but not in terms of the magnitude of their damage
Ensemble Model-based Clinical Decision Support System for Inherited Retinal Diseases in Pediatric Age
Get PDFIn recent years, advancements in healthcare technology have paved the way for innovative approaches to clinical decision support systems (CDSS), especially in the context of inherited retinal diseases (IRD) affecting the pediatric population. IRD represent a group of genetically transmitted conditions that affect the structure and function of the retina, leading to visual impairment or blindness. The pediatric age group is particularly vulnerable to these diseases, necessitating early and accurate diagnosis for effective intervention and management. Traditional diagnostic systems for IRD often rely on a single approach, such as genetic testing, clinical examinations, or imaging techniques. While these methods have contributed significantly to our understanding of these diseases, their limitations in handling the complexity of genetic variations and the heterogeneity of disease manifestations underscore the need for a more sophisticated and integrated approach. Ensemble models offer a departure from the limitations of traditional systems by combining the strengths of various models, thereby improving diagnostic accuracy and reliability. Therefore, this research proposes an ensemble model based CDSS for inherited retinal diseases in the pediatric age group. By harnessing the power of diverse models, this approach can provide clinicians with a more comprehensive and accurate assessment of the underlying genetic factors and disease progression. Additionally, the proposed ensemble model based CDSS integrates diverse predictive models to enhance diagnostic accuracy and aid in the management of IRD in pediatric patients. Ultimately, the significance extends to improving patient outcomes, enabling earlier interventions, and contributing to the development of targeted therapies for IRD in the pediatric population
DL BASED IOT ENERGY AUDIT ANALYTICS FOR DETECTING AND IDENTIFYING CYBER-PHYSICAL ATTACKS
Get PDFInternet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber-attacks. This paper proposes the first energy auditing and analytics based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber-attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances
Machine Learning-based Detection of Malaria Infection through Blood Sample Analysis
Get PDFMalaria, a life-threatening disease caused by Plasmodium parasites transmitted through infected mosquitoes, remains a significant public health concern in many regions worldwide. Early and accurate detection of malaria infection is crucial for timely treatment and disease management. The automated malaria detection system can be integrated into portable diagnostic devices, enabling healthcare professionals to perform rapid and accurate malaria tests in remote or resource-limited settings. The system can assist researchers and health organizations in tracking malaria prevalence and monitoring its spread, contributing to epidemiological studies and efficient resource allocation. Conventional methods for malaria detection involve manual examination of blood smears under a microscope by trained technicians. Although reliable, this process is time-consuming, labor-intensive, and dependent on the expertise of the microscopist. The regression-based examination of blood smears introduces the potential for errors, leading to false-negative or false-positive results. In recent years, machine learning-based approaches have shown promising results in automating the detection of malaria parasites through blood sample analysis. This work presents an advanced machine learning-based method for the automated detection of malaria infection, leveraging image processing techniques to achieve high accuracy and efficiency