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Evaluating the risk for any investment is a challenging task. It requires the determination of various factors impacting the asset under consideration. The value-at-risk (VaR) measure has been widely accepted as an effective tool for risk analysis. This study provides a risk analysis of a stock portfolio from the sectors of Oil and gas, Consumer goods and retail, Engineering products and services, Finance and Banking, and Healthcare. The stocks were selected based on their market capitalization in the National Stock Exchange (NSE), India. VaR is computed using various techniques, such as the historical method, parametric method, Monte-Carlo method, GARCH and historical conditional VaR (CVaR). Additionally, the optimal weights of each stock in the portfolio have been determined. This work excluded the period from March 2020 to December 2020 from the VaR analysis, as it witnessed a significant variation in stock prices due to the COVID-19 wave. The estimated VaR indicated that the historical conditional VaR showed the fewest violations, while GARCH estimated the minimum VaR compared to the other techniques and thus a relatively higher percentage of violations during backtesting. This work highlights the dynamic nature of financial risk and underscores the need for adaptive risk management strategies that can evolve with the changing market conditions. [ABSTRACT FROM AUTHOR]

This paper describes the dual-band microstrip antenna at 5.9 GHz and 25 GHz in the mm-wave range for DSRC and V2V communication. The design comprises a 1 x 2 series-fed array running on a single-layer substrate co-printed with a 2 x 2 differentially fed patch array operating at 5.9 GHz. Specifically, the 5.854-6.001 GHz (DSRC band) low-profile shared aperture array has a bandwidth of 147 MHz and a gain of 10.63 dB that is suitable for V2V communication. Additionally, it operates in the 5G mm-wave spectrum at 25.626-26.362 GHz with a bandwidth of 736 MHz and a gain of 9.778 dB. With an isolation of more than 50 dB between the 5G mm-wave band port and the DSRC band port, the array's ports are very well isolated. [ABSTRACT FROM AUTHOR]

Wind turbine is an alternative way of generating energy from the power available in wind. Efficient extraction of energy is the prime importance that depends on the design of the blade. Wind turbine blades undergo various environmental conditions as well as high operational loads. In this work firstly an accurate CAD model is prepared for new optimized blade geometry and then structural integrity verification is performed through FEA under several operating conditions. The blade is designed with the objective of achieving maximum power coefficient at the designed TSR = 7.05 by applying the BEMT. The blade is made by using an airfoil from N.A.C.A. 65(4)-221. A 14m blade is taken for this purpose. A large HAWT, RRB V27-225 kW is taken for study. Two different blade materials E-glass-fibre and carbon-fibre were taken for study. Two cases were discussed, first with shear web and second without shear web. Results show better structural strength for the material carbon-fibre containing shear web. [ABSTRACT FROM AUTHOR]

The use of solar air heaters is efficient as solar energy is converted into thermal energy, which then is transferred to a fluid in order to be used for heating. Solar energy is collected at the surface of the absorber plate, transformed into thermal energy, and then used. It has been determined that the low effectiveness of the flat plate SAH is caused by a low convective HT coefficient. Increasing the effectiveness of the SAH by including roughened components on the bottom of the absorbing area is the strategy that has shown to be both the most popular and the most successful. An increase in the radiation intensity that is received by the absorber plate as well as an efficient removal of heat contribute to an increase in the SAH's thermal performance. The findings of the experiments conducted for this study reveal that the HT of the absorber plate can be amended by intentionally roughening the side of the plate that faces the air flow. HT coefficient and thermal efficiency properties of SAHs were tested in the field using artificial roughness created in the form of discrete double arc reversal on the plate. The parameters employed were W/H = 8, p/e = 10, e/Dh = 0.027, α = 60°, and Re = 2975-13739. In addition, the solar radiation intensities during the outdoor test ranged from 860 W/m2 to 895 W/m2 and lasted from eleven in the morning (just before noon) until two in the afternoon. Thermal efficiency and coefficient of HT across the smooth duct were increased by 1.16 and 2.8 times respectively. [ABSTRACT FROM AUTHOR]

To perform complex subjects in perception, decision making and motion planning, driverless vehicles depend on machine learning. However, automotive software safety standards have not completely matured to handle machine learning safety concerns such as interpretability, verification, and performance limits. In this paper, we review recent developments in planning, decision-making, and perception for autonomous cars that resulted in significant functional gains, with numerous prototypes currently operating on our streets and roads. However, issues endure in ensuring assured performance and dependability in all driving conditions. Many businesses are evolving self-driving automobiles, which are likely to be widely used in the near future. Considering this, there is no consensus on effective techniques for testing, debugging, and certifying the system performance. One key challenge is that many unmanned driving systems use ML (machine learning) elements like DNN (deep neural networks), which seem to be hard to interpret in formal terms. It is necessary to develop planning solutions that provide safe and scheme performance in complex, crowded situations, such as predicting the unanticipated interaction with other traffic participants. [ABSTRACT FROM AUTHOR]

The supply of defect-free, high-quality items is a critical factor to calculate for the long-term competitiveness of industries. Quality control is important in factories to form the item defect-free as well as to meet the needs of customers. With later advancements in deep learning and computer vision advances, it has ended up identifying different flaws from the images with near-human exactness. By introducing an insightful assembling framework, defects can be limited, and human expenses can be brought down to empower feasible development. The smart production line employs production equipment, functional testing equipment, and defect detection equipment. This paper is presented with a flaw identification system for application in smart factories based on deep learning. Training with deep learning method, EfficientDet to naturally identify deformity from ultrasonic pictures. The test results show that it was able to classify flawed items rapidly with high precision in a real-world manufacturing environment. The proposed model during 5-fold cross-validation accomplished 96% of mean accuracy was a noteworthy advancement compared to a few comparative strategies that were already utilized for this task. EfficientDet detected all the flaws present in the material during assessment and performance analysis showed it was comparatively good in detecting flaws. [ABSTRACT FROM AUTHOR]

Breast Cancer (BC), more than any other malignancy, is one of the main reasons of mortality amongst women. Due to disease's intricacy, varying patient population samples and changing treatment protocols, accurate diagnosis of breast cancer is extremely challenging. Better diagnostic procedures are critical for tailored care and treatment, as well as reducing and controlling cancer recurrence. This study investigates the early identification of breast cancer using a variety of ensemble methodologies. The dataset that has been utilized in this work is chosen to forecast breast cancer based on nine individual variables, including BMI, age, insulin, glucose and a homeostasis model evaluation. This research provides a suggested strategy based on feature selection that uses Principal Component Analysis (PCA) as well as the ensemble technique for dramatically lessening the dimensionality of features and enhance breast cancer classification. The suggested method is tested using the Wisconsin Breast Cancer Dataset, which is open to the public (WBCD dataset). This study focuses on evaluating and comparing the efficacy of several ensemble approaches based on PCA feature selection in early diagnosis of BC. LogitBoost is the most precise model amongst all the ensemble approaches, according to the findings. The LogitBoost algorithm beats the other ensemble methods, with an accuracy of 72.46 percent. The findings of data analysis show that ensemble classifiers outperform state-of-the-art approaches in detecting BC. [ABSTRACT FROM AUTHOR]

With the support of Information and Communication Technology (ICT), industrial sectors have transitioned from mechanization to digitalization. This shift has ushered in the era of Industry 4.0 or smart manufacturing, made possible by technological advancements and the Internet of Things (IoT). Industry 4.0 encompasses a wide array of transformative technologies such as big data, IoT, cloud computing, robotics, blockchain, artificial intelligence (AI), business software, autonomous vehicles, and others, all of which are reshaping the landscape of the manufacturing industry. However, Industry 4.0 is a relatively recent concept, and it suffers from a considerable degree of confusion, a lack of understanding, and limited research in the domain of performance evaluation and quality management. Meanwhile, manufacturing companies are still grappling with the abundance of Industry 4.0 technologies. The current study examines and evaluates several Machine Learning (ML) strategies for applying Key Performance Indicators (KPIs) in smart manufacturing firms in order to anticipate highly efficient production systems for reducing production costs as well as to improve product quality. The Neuro-Fuzzy based Subtractive Clustering (NFSC) approach and the Artificial Neural Network (ANN) technique is utilized for forecasting manufacturing process for KPIs. Models generated using the NFSC approach for six KPIs has lower Mean Absolute Percentage Error (MAPE) than those established utilizing ANN technique. In this investigation, the results suggest that the NFSC approach has greater prediction ability. [ABSTRACT FROM AUTHOR]

Iris-based human identification detection is used in iris recognition technology to authenticate and verify the identity of individuals for security reasons. It entails recording and analyzing the particular patterns found in a person's iris, which remain steady throughout time. Deep learning approaches also play an important part in iris-based human identity recognition, harnessing the capacity of neural networks to increase accuracy and reliability of the identification process. Deep learning enables iris identification algorithms to extract high-level representations and understand complicated patterns within iris images, hence increasing their discriminatory skills. In this article, a learning technology called CNN. This adapt and capture complex patterns and subtle variations in iris texture, resulting in more robust and accurate identification. CNN can be trained on large datasets to learn the different individuals' irises, enabling them to differentiate between individuals with a high precision. Moreover, CNN enable end-to-end learning, where the entire iris recognition pipeline, from image preprocessing to feature extraction and classification, can be optimized jointly, further improving overall system performance. The flexibility and capability of CNN make it a powerful tool in advancing iris-based human identity detection systems, contributing to more reliable and efficient security applications. [ABSTRACT FROM AUTHOR]

Wireless camera sensor networks have gained significant popularity in various applications, ranging from surveillance to environmental monitoring. As the number of camera nodes increases, load balancing becomes crucial to ensure optimal performance, efficient resource utilization, and prolonged network lifetime. This review article presents a comprehensive analysis of various load balancing strategies employed in wireless camera sensor networks. We discuss the implementation details, complexities, applications, advantages, and disadvantages of each strategy. Furthermore, a tabular comparison with bullet point differences based on different subjective parameters is provided to facilitate the selection of the appropriate load balancing strategy for specific scenarios. [ABSTRACT FROM AUTHOR]

Uncontrolled infrastructure expansion depletes natural resources, and waste plastics are regularly thrown into the environment and oceans, putting nature, humans, and aquatic life at risk. These risks can be mitigated by properly disposing of plastic waste. Plastic waste management, such as reusing building waste materials, reduces pollutants that cause pollution and the demand for natural resources. The primary purpose of this research is to look into the possibility of using waste plastic as a target material for partial volume reduction in concrete blocks. The single-use polyethene terephthalate (PET) trash is placed in a PET container and compressed securely with a tampered rod. The PET bottle's opening is sealed with a polypropylene plastic cap. A concrete mix grade of (M25) is employed for this experiment. [ABSTRACT FROM AUTHOR]

Second-order unstable processes are frequently encountered in process industries. The higher-order process dynamics are approximated to second-order processes in order to design a controller. In industrial processes, Proportional Integral Deriva-tive(PID) controllers are still widely used controllers. PID, cascaded with filters provides satisfactory closed-loop performance for unstable processes. The present work proposes a PID controller cascaded with a fourth-order filter for an unstable Second Order Process with Time Delay(SOPTD). A polynomial approach is used to estimate the controller and filter parameters analytically. For the time delay approximation, a higher-order Laguerre shift is utilized. The Maximum Sensitivity(MS) criterion is used in order to calculate the tuning parameter(λ). To reduce servo response overshoots, a set point filter is chosen. The present method is compared with other reported methods using various performance indices. [ABSTRACT FROM AUTHOR]

The present paper aims at the design of a Proportional-Integral-Derivative(PID) with a fourth-order filter for controlling Second Order Process with Time Delay (SOPTD). The PID and fourth-order filter parameters are obtained analytically using the polynomial approach. The selection of the sole tuning parameter (λ) depends on Maximum Sensitivity (MS) criteria. As it is inevitable to adopt approximation of time delay in the process of deriving unknown controller parameters, a higher-order approximation of time delay i.e second order Pade's shift is used to ensure more accuracy relatively with the existing methodologies. The controller introduces zeros in the overall transfer function of close loop control system. These zeros may result in overshoot in the response. In order to bring down this overshoot, a set point filter is employed. The proposed method is compared to other existing techniques and the performance evaluation is presented in terms of various performance indices. The proposed method is also validated on a real-time process i.e a DC servo motor. The relation between speed and voltage of DC servo motor is obtained with the help of input out put data. The proposed method is then utilised to derive the controller parameters. [ABSTRACT FROM AUTHOR]

In general, high density lipoprotein is considered one of the anti-plaque acellular organisms that can reduce atherosclerotic risk by reverse cholesterol transport property. This study motivates us to propose an atherosclerosis model with Atangana-Baleanu-Caputo fractional order derivative and discuss the inflammatory and control criteria in the presence of memory function. The model includes non-linear diffusion terms to reflect the effect of porous media, as our intima is the porous media type. This research mainly focuses on mathematical analysis and simulation of the atherosclerosis inflammation process. The existence of the solution for the proposed model is discussed with the help of Picard's recursive method, and qualitative analysis is obtained. We also define the uniqueness of the proposed model. All of these analyses are presented for the corresponding kinetic system. As compared to the ordinary differential equation model, the proposed fractional-order mathematical model is more successful at determining the precise parameter values. This study clearly illustrate that atherosclerosis inflammation will be easier to identify with the use of the fractional-order mathematical model for a disease. The numerical results are demonstrated for validating the analytical results. [ABSTRACT FROM AUTHOR]

Microfluidic devices build benefits such as capacity to manipulate 10-9 to 10-18 L sample volume, fast analysis and on-spot detection are required for separation and analysis of biomolecules for earlier clinical assays. This paper presents the electrophoresis based separation of platelets from whole blood for the early diagnosis of illness such as thrombocytophenia and thrombocytosis. Simple cross flow microfluidic channel, utilizing focusing and filtration channels, is designed for separation based on electrophoresis and electro-osmosis principle. Blood and analyte solution is introduced into the device using the capillary forceand the separation is achieved by applying the electric potential. The concentration of different molecules along the channels is analyzed for the designed structure. [ABSTRACT FROM AUTHOR]

In water-driven power plants, slurry erosion of hydro turbine components is a major problem globally, especially in those located in the Himalayan area of India. In the current study, Al2O3 and Cr2O3 coatings were coated on EN31 substrate material using a flame spray technique, and their slurry disintegration testing was performed while varying the settings of a specially designed and built slurry disintegration analyzer. The slurry disintegration tests were led by taking factors: SOD (15 mm and 20 mm) and slurry fixation (15000 ppm and 20000 ppm). The samples' reduced mass has been calculated, and SEM and EDS analysis has been performed on every sample. In addition, X-ray diffraction analysis has been performed on the sample under investigation (XRD). The analysis revealed that the Al2O3 coating provides the highest disintegration obstruction across the board. [ABSTRACT FROM AUTHOR]

Slurry erosion of hydro turbine parts in water driven power plants is an extreme issue everywhere throughout the world transcendently in those arranged in Himalayan area of India. In the present work, two kind of coatings Al2O3 and Cr2O3 were deposited on EN31 substrate material by flame spray process and their slurry disintegration testing was done affected by changing parameters on the slurry disintegration analyzer which has been structured and created for this reason under this exploration work. The slurry disintegration tests were led by taking factors: SOD (15mm and 20mm) and slurry fixation (15000ppm and 20000ppm). The weight reduction of the samples has been determined and all the example has been examined by SEM and EDS. Additionally, the covered example has been examined by X-RAY diffraction (XRD). From the examination, it has been discovered that the Al2O3 covering gives most extreme disintegration obstruction affected by all the differing parameters. [ABSTRACT FROM AUTHOR]

This research investigates the wear behavior of glass fiber reinforced with nanoparticles of Al2O3 through an experimental analysis using artificial neural network (ANN). Three different percentages of Al2O3 reinforcement, namely 1%, 3%, and 5%, were used to research the wear properties of the prepared material. The wear experiment was planned using Taguchi design, with composition (C), load (L), speed (Vs), and distance of sliding (Ds) as input parameters, and specific rate of wear (Rw) and friction coefficient (Fc) as output responses. The Taguchi optimization technique was used to optimize the responses. The results show that the wear resistance and friction coefficient of the composite increased with increasing percentage of Al2O3 reinforcement. The Taguchi optimization technique was effective in optimizing the input parameters, and the ANN model developed to predict the responses showed higher accuracy. The ANN model was able to predict the wear properties of the composite with high precision, indicating that it could be used to optimize the wear behavior of similar composites. Overall, the study provides valuable insights into the wear behavior of glass fiber reinforced with nanoparticles of Al2O3 and highlights the effectiveness of Taguchi optimization and ANN modelling in predicting the wear properties of composite materials. The findings of this research could be useful in developing more durable and wear-resistant materials for various industrial applications, including aerospace, automotive, and biomedical industries. [ABSTRACT FROM AUTHOR]

Artificial intelligence and Machine learning advancements in stimulation of human knowledge to computers has led to the software based application / program called chatbot. A chatbot acts as a conventional agent which uses natural languages for communicating with operators. The chatbots are aware of self learning algorithms, for example natural language processing (NLP). These software programs are made to help people and have a one-on-one interaction with them (either in text or speech format). The proposed chatbot provides a platform for crime registration and also getting information about various types of crimes. We can find chatbots in the field of entertainment, query clarification, social media, e-commerce sites etc. but rarely can we find chatbots serving the purpose of security and crime related functions. Our proposed system collects various information from the victims so that the authorities could cross-verify the information provided. It gives a stage for people to report about the crimes, getting data about different sorts of violations. It gathers different check archives from the casualties so the specialists could cross-confirm the data given. The chatbot works on the principle of natural language processing algorithm. [ABSTRACT FROM AUTHOR]

Lean methodologies have become beneficial in hospitals as it improves the process flow without mistakes, enlargement and other wastes. Increased efficiency, reduction in non-value added and upgrade of the value-added activities, enhanced production flow, effectiveness in cost and time are the key elements in eliminating waste. Objective: To identify the value and non-value-added activities by analyzing and working out the current state, differentiating the use versus need, envisions an improved future state. The patient's total mean time spent in his/her treatment with two major corporate hospitals (Ramesh hospital & Medi cover hospital) located in the Tier 2 cities Guntur and Visakhapatnam. The data collected for a period of seven days analyzing twenty patients each day, focused on General Physician department during the Out-Patient hours. Also, a keyword analysis using VoS Viewer on Value and Non-Value added is extracted. It is identified that the efficiency in hospital needs to be assessed with the incorporation of the lean tools like standard charts, value stream mapping, determining takt time etc. The hospital management across to improve the performance, patient satisfaction and quality treatment need to incorporate lean methodology in hospitals. [ABSTRACT FROM AUTHOR]

In the realm of renewable energy for power generation, wave energy holds great potential because time demanding to design a structure that can replicate any laboratory conditions in order to deliver information to manufacturers and industries. In this research, an experiment was carried out under laboratory settings between a constructed linear electric generator with permanent magnets and a hands-shake generator that transforms vibrational energy to electrical energy to determine the performance of outpower. The findings of this study show that the uncertainty analysis for the devices is achieved 2.23%, and the average power output of a constructed linear electric generator is 3.5W, while the average output of a hand-shake generator is 4.42W. This, is due to the uncertainty in the linear electric generator during production process. In addition, this research reveals that a single cluster of coupled linear electric generators can be used to generate additional electrical power. From the present study, it concludes the areas where the highest wave energy produces are feasible to install the floating wave type. [ABSTRACT FROM AUTHOR]

In the present study the Dy3+ doped Sr1-xAl2O4: xDy (x= 0.05) phosphor (SAD 0.05 phosphor) was synthesized with Sr(NO3)2 and Al(NO3).H2O as precursors utilizing the urea fuel combustion method at 610 °C temperature. After a structural study employing X-ray diffraction, the sample was found to be single phased in nature and to have crystallized into monoclinic phase match with standard JCPDS 34-0379 card. The optical band gap obtained for Dy3+ doped Sr1-xAl2O4: xDy (x= 0.05) phosphor (SAD 0.05 phosphor) is 2.6460 eV in the current scenario, showing that the sample is a wide band gap material. The majority of the particles appear to be irregular spherical grains with a mean size of 19–41 nm, according to transmission electron microscopy (TEM). The photoluminescence study revealed the three emission peaks in emission spectra with 572nm as dominant emission peak, by using 350nm as excitation wavelength. The CIE chromaticity diagram is used to calculate colour temperature 10475K and colour purity 21.95% of sample, the Dy3+ doped Sr1-xAl2O4: xDy (x= 0.05) phosphor (SAD 0.05 phosphor) can be tuned as light blue (0.28, 0.28) emission. The spectra are intense and lie in visible range. [ABSTRACT FROM AUTHOR]

Construction industry experts are constantly implementing technology to improve construction Safety management at site. We can think of Artificial Intelligence (AI) as a tool to solve existing challenges. This research proposes the creation of an AI platform to help with construction safety through pre-construction danger identification, and recommends the OSHA codes. Construction sites in Chennai are notoriously complicated, with operations, procedures, technologies, individuals engaged, location, geography, and constantly changing site dimensions. High number of fatal and non-fatal incidents on construction sites demonstrates the importance of a comprehensive safety and fire risk assessment. A detail literature review has been carried out and questionnaire surveys have been conducted to gain a clear understanding of the current state of occupational safety, health and environmental standards and compliances in the Chennai construction sector. Project safety and risk assessment is carried out by specialists who make decisions based on their own experiences. There is a lack of a systematic strategy as well as techniques to verify the accuracy of these decisions. We can automate the process and create an environment where possible risks are spotted before they occur. Both residential and commercial building sites have occupational safety and environmental difficulties. Workers, management, and staff will get more information and awareness as a result of the study's conclusions being implemented. The ultimate goal should be to achieve healthy and environmentally friendly building sites in the Chennai district. [ABSTRACT FROM AUTHOR]

Early prediction and cardiac diseases help practitioners to make more accurate decisions about the patient's health and their conditions, Machine learning techniques provide the solution to reduce false and late prediction and understand the symptoms for the particular disease. Heart disease is the major reason for death over the world in the last few decades, so there is a need for reliable and accurate treatment for the patient's at an early stage and within time. Various classification and data mining techniques are used to classify the disease data and predict particular diseases or not. This research work aims is to study different techniques for the health care sector and shows the comparison between them. [ABSTRACT FROM AUTHOR]

The Long short-term memory is one of the scheme in Deep Learning technique that can be used for more accurate time series forecasting. Cities are nowadays facing a huge traffic congestion due to the rapid increase in the number of vehicles. More accurate traffic forecasting is very much essential for an Intelligent Transportation System (ITS). The advancement in the Computational Intelligence (CI) techniques provide best results in the forecasting problems compared to the classical statistical approach. The proposed work is based on the CI technique, the LSTM for the traffic flow forecasting using traffic sensor data that are available as a big data. The experiment shows better forecast metrics with RMSE (Root Mean Squared Error) of 8.4 and 9.86 for training and test data respectively. The MAE (Mean Absolute Error) is 5.52 and 6.83 for training and test data respectively. Thus the proposed work on LSTM based traffic flow prediction results in a superior performance. [ABSTRACT FROM AUTHOR]

The utilization of versatile broadband information has seen an unstable development with the dispatch of Long Term Evolution (LTE) and LTE-Advanced organizations. An ideal waveform for 5G cell communication will give high ghostly effectiveness, low Peak-to-Average Power-Ratio (PAPR), strong against Doppler move to permit versatility, non- concurrent transmission and gathering ability. In this proposition, study has been done on three potential 5G waveform competitors specifically General Frequency Division Multiplexing (GFDM), Universal Filter Multi-Carrier (UFMC), Filter Bank Multi-Carrier (FBMC), and their benefits and disservices. Determination of an appropriate competitor waveform for 5G, recreation of the 5G frameworks dependent on the up-and-comer waveforms has been performed utilizing the MATLAB software and their exhibition is contrasted freely and the current Orthogonal Frequency Division Multiplexing (OFDM) based LTE/LTE-Advanced framework in terms of Spectral Efficiency(SE), Out-of-Band (OOB) Emissions, PAPR, and Bit Error Rate (BER) investigation. In view of the better ghastly productivity, less OOB spillage, and marginally better PAPR, FBMC waveform has been chosen as a superior decision for 5G communications. Reenactment results shows because of the cyclic prefix the FBMC and CP-OFDM endure when communicating short blasts, the UFMC outperforms by 10% for any case and FBMC for the short parcels and it is like FBMC for long successions. In outline, the adaptable idea of FBMC makes this waveform an appropriate possibility for future 5G organizations. [ABSTRACT FROM AUTHOR]