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Quadriplegics are people who cannot use their extremities. Existing aiding devices are costly, complex, not user friendly and they are guardian dependent for movement. In this paper, a Raspberry Pi enabled wheelchair movement control by head movement detection is presented. A head movement detection technique based on Open - Computer Vision image processing is used which detects the user's facial movement in real time using the camera. Using this, the wheelchair motors are controlled and are driven in the indicated direction. Also, numerous sensors such as temperature sensor, pulse rate sensor, accelerometer sensor and fire sensor are added to monitor patient health, safety and well-being.

Magnetic Levitation System (MLS) is a nonlinear system and it is used extensively in many areas. The system's goal is to use the non-contact principle to magnetize the coil and cause the objects to float to a specific height. The magnetic force and the current flowing through the coil have a nonlinear relationship. In this paper first design of the back stepping controller is done to attain the wanted floating in presence of system related uncertainties and system behavior is witnessed. Then a Sliding Mode Controller (SMC) based on back stepping procedure is formulated. The control gains in this controller are designed in such a way that the characteristics polynomial whose coefficients are control gains is strictly Hurwitz and the closed loop system's asymptotic stability is assured. Simulations are performed and the magnetic ball tracking is observed considering step signal as reference in presence of disturbances. More accuracy is observed in terms of following the reference by the magnetic ball when it is used Sliding Mode Controller based on Back stepping technique instead of back stepping controller by considering pulse type disturbance. By simulation and analyzing results it is confirmed that the proposed control strategy is more effective.

The concept of Natural Language Processing that deals with problems of identifying the sentiment from the voice or text of a speaker or writer and then use that analysis further for making predictions, market survey, customer service, product satisfaction, precision targeting etc. is called Sentiment analysis. From one viewpoint, it is an abstract evaluation of something dependent on close to home observational experience. It Is mostly established in target realities and incompletely governed by feelings. Then again, a sentiment can be deciphered as a kind of measurement in the information in regards to a specific subject. It is a lot of markers that mix present a point of view, i.e., perspective for the specific issue. So as to enhance the accuracy of sentiment analysis/classification, it is imperative to appropriately recognize the semantic connections between the various words and phrases that are describing the subject or aspect. This can be done by applying semantic analysis with a syntactic parser and supposition vocabulary. This research will discuss different sets of approaches for application or domain specific problems and then compare them to obtain the best possible approaches to the problem of sentiment analysis.

Massive MIMO is an extension of traditional MIMO with the exception that the BSs in massive MIMO are equipped with large number of antennas, usually hundred or more. This large number of antennas provide several positive advantages towards wireless communication with respect to increasing volume of data traffic. Each antenna is capable of serving multiple users simultaneously leading to reduction in power consumption as well as data rate amplification. Additionally, narrow and more focused beams are pointed to individual user devices located at the cell edge thereby upgrading of downlink signal quality. Using massive MIMO technique also increases reliability of the links, reduces noise effects, and mitigates and interference. With increasing number of users gets service, the throughput of the system also increases.

he project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Preprocessing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall.

When somebody, usually a teenager, abuses or harasses individual on the internet and other digital places, mainly on social networking platforms, this is termed as cyberbullying. Cyberbullying, like all types of bullying, produces psychological, emotional, and physical distress. Every individual's reaction to being bullied is diverse, but research has discovered certain common patterns. In a recent study, we introduced a technique called Hybrid Firefly Artificial Neural Networks (HFANN) to combat cyberbullying. Nevertheless, without considering the sentiment analysis features, accuracy of cyber bullying identification is lowered in this study. The Sentiment Analysis and Deep Learning based Cyber Bullying Detection (SADL-CDD) approach is used in the suggested research approach to address this issue. The punctuations, urls, html tags, and emoticons from the input tweet comments are removed first in this study project. Sentiment feature extraction is performed after pre-processing to improve classification accuracy. The Modified Fruit Fly Algorithm (MFFA) is used to choose the best features from the extracted features. Following feature selection, cyber bullying detection is carried out using a Hybrid Recurrent Residual Convolutional Neural Network (HRecRCNN). The experimental outcome of this study indicates the efficiency of the suggested approach. In comparison to current algorithms, the SADL-CDD method delivers improved classification performance with respect to reduced time complexity, greater precision, recall, f-measure, and accuracy.

In today’s world, a Phasor Measurement Unit (PMU) is a crucial component of our power network for managing, controlling, and monitoring. PMU can provide synchronized voltage current, and frequency measurements in real time. We can't put a PMU in every bus in the electrical grid because it's not viable from a productivity and economic standpoint, and it's also not practical for handling huge data. As a result, it's critical to reduce the amount of PMU in the power network while also increasing the power network's observability. The optimal PMU placement problem is solved using a variety of methodologies. The paper's main goal is to provide a brief overview of synchrophasor technology, phasor measurement units (PMU), and optimal PMU placement in order to reduce the number of PMUs in the system while maintaining complete observability and application in today's power systems.

This paper deals with dynamic decoupling and its intelligent PID control method of multivariable qua-drone. Up to this time, many sophisticated intelligent algorithms and control methods for drone systems have been mentioned. However, almost many cases have been focusing on single loop control methods and general multivariable systems. Therefore, we cannot guarantee its stability and optimal response by PID control used in multivariable qua-drone. Herein, this paper suggests a novel control method for PID control for multivariable qua-drone. As first step, this paper decouples dynamic of multivariable qua-drone using diagonal method of system matrix and then applies intelligent method PSO and GA to single loop obtained by decoupling method to obtain optimal response.

The Transmission part of the power sector is very much important as it deals with the huge losses of the complete power sector. Transmission substations play a key role in the power transmission process since the power generation at the power plants to the final stage at the consumer end utilization. The utmost care should be taken to protect these transmission substations as same as the care is taken to reduce the losses. The substation needs to be protected from blackouts which is the big problem of substations. For this, the substation high voltage equipment’s like GIS & Power Transformers are to be properly installed & energized according to the standards & care is taken for its maintenance. So, during the commissioning process which is after installation & before energization, there are different tests which are performed on high voltage equipment’s, especially the Gas Insulated Switchgear, in which the High Voltage & Partial discharge has huge impact as the ageing and the life time of GIS can be easily assessed & the GIS can be saved from the damage, tripping and the blackout of a substation. The procedure of the high voltage test and the partial discharge tests are clearly explained in this paper.

The Nusselt number, overall heat transfer, and convective heat transfer coefficients of glycerol-water-based Cu nanofluids flowing in a parallel flow double pipe heat exchanger are estimated using CFD analysis. Single-phase fluid approach technique is used in the analysis. Ansys 19.0 workbench was used to create the heat exchanger model. Heat transfer tests with nanofluids at three flow rates (680

A hybrid structured asymmetric switching capacitor multilevel inverter (ASC-MLI) is suggested in this work. The notion behind presenting this topology is to reduce the device count and DC sources as compared with conventional MLI. The step by step operating mode of single phase ASC-MLI is presented and by doing slight modifications the same configuration is used in three phase utility application and electric drive. The proposed configurations utilize major benefits of self-voltage balancing capability of capacitor voltage, which is independent from different load type and modulations index. To generate the switching pulse for corresponding switches the multi-carrier based sinusoidal pulse width modulation (MCS-PWM) technique is used; in addition to this simulation result are obtained using MATLAB/Simulink 2016b software version. Simulation results of an induction motor drive connected as three phase load highlights good performance of 17-level MLI.

The paper aims to examine the effects of financial technology (Kisan Credit Card and ATM) on India's Poverty and empirically analyze the influence of financial technology on poverty scores. We consider data from twenty-two Indian states for three fiscal years, i.e., from 2018 to 2020. The current study uses panel data analysis to test the hypothesis. Results suggest a negative association between financial technology (Kisan credit card and ATM) and its influence on the poverty score in India. The current study suggests that the government should pay more attention to expanding financial technology, including increasing economic growth and financial stability and eliminating poverty by investing in infrastructure that would enhance banking services.

Stone mastic asphalt (SMA) is a gap graded mix which is categorised by more quantity of coarse aggregate, high asphalt content and fibre. Due to stone on stone contact and presences of high filler content, it acts as a stiff matrix and reduces the rutting due to heavy traffic load. This research presents a study on fatigue performance RAP replaced SMA mixes using VG 30 as binder along with elastomer as a modifier and results were compared with conventional SMA mix. The specimens prepared were tested using several laboratory test procedures: Marshall mix design, indirect tensile strength, moisture susceptibility, drain down test and Repeated load fatigue test. Test results showed Marshall Properties of the RAP mix improved up to a RAP content of 30% without elastomer modifier and RAP content up to 60% with elastomeric modifier. From the moisture susceptibility test results, the elastomeric modified SMA mix showed high resistance to moisture damage when compared to conventional mix and 30% RAP replacement mix. Repeated load fatigue test was conducted for different stress load and temperature and results showed elastomeric modified SMA mix offered high resistance to deformation across all stress level and temperature when compared to conventional and optimum RAP mix. As a fatigue loading increased resulted in decrease of number of fatigue cycles and increased in the initial tensile strain of the mix. As the percentage of RAP addition increased the initial tensile strain decreased.

The Enhanced Oil Recovery (EOR) process is one of the ways in the petroleum exploitation process so that thick oil can be lifted to the surface and produced. The EOR process referred to in this study is the EOR process carried out in previous studies at the EOR laboratory of UPN Veteran Yogyakarta Indonesia by adding biosurfactants and adjusting the temperature. In laboratory experiments, each time an amount of biosurfactant concentration is added and the temperature is adjusted, the calculation must be done repeatedly to determine the amount of viscosity, interfacial tension (IFT), and density. This experiments takes a long time, requires high cost and variety limitation of the condition. The previous research has succeeded in building a model with multivariate polynomial regression equations to predict the value of the physical properties of crude oil from existing data then classify it into three categories using Naive Bayes, i.e., light oil, medium oil, and heavy oil. The physical properties of petroleum measured in the research are viscosity, interfacial tension, and density. The model uses laboratory data which are taken from the test results of Pertamina's KW-55 well as validation. The validation result shows that Multivariate Polynomial Regression has succeeded in predicting the value of viscosity, interfacial tension, and density with error values ranging from 0% to 1% from the sample data. With a low error value, the application can make forecasting with more variable conditions. The model still cannot be used independently without the Python environment, so to be used easily by more users, the model must be built into an independent application that can be installed on the user's device. In this research, the prediction application of petroleum physical properties has been built. The application is made using the Multivariate Polynomial Regression method according to the model in the previous study to predict the physical properties of petroleum, then uses Naïve Bayes to classify the oil. The application completed the several adjustment to shift from model to application, including user interface, system, and database adjustments.

Elliptic Curve Cryptography is gaining attraction in providing a high security level in data transmission with low cost, small key size and smaller hardware realization. High-speed implementation is a significant factor in ECC applications such as smart cards, network servers, wireless sensor based networks, Internet of Things and Radio Frequency Identification. These applications require low-cost and lightweight implementations. In the resource constrain application, lightweight cryptography has emerged as the desired one because of limited energy in devices and the scarce computational resources. Design options and a wide range of parameters affect the overall implementation of the ECC system. Implementation target device, coordinate system, underlying finite fields and modular arithmetic algorithms are key design parameters that impact the overall implementation outcome. A statistical study is conducted on a large collection of published work based on the design parameters. The basic question that arises is how to select the appropriate flexibility-efficiency tradeoff. The subjects of generator, versatile, reconfigurable, dedicated and general purpose scalar multipliers are addressed. A review of various algorithms to perform scalar multiplication on prime and binary fields has been done more effectively. The results of ECC implementation on different FPGA platform is compared and analyzed with the various performance parameters. Besides, a classification of the previous works in terms of flexibility, performance, scalability and cost effectiveness is presented.

In sugar industry, the sugar processing done in different sections, but to increase total crushing per day (TCD) capacity, the milling section takes a vital role in the sugar industry. The sugar industry aims to extract the maximum amount of juice from sugarcane. In the milling section, the processed sugarcane is fed in between the three-roller shaft from the different arrangements, there are different loads applied on each part roller shafts. When load between all rollers varies then there is a chance of bending it is analyzed to check the roller shaft condition. The modeling is done on roller shaft with the help of CATIA V5. After modeling, we analyze the condition of the rollers, when different stress or forces are applied to different sections of the roller shaft it analyzed with the help of Finite element method using ANSYS WORKBENCH software. We were selecting titanium alloy materials for the roller shaft to analyze the variation in results. When comparing the calculated and software-based results using Maximum Shear stress and Total deformation for top, feed, and discharge rollers said the roller shafts are safe to use in the sugar industry and titanium alloy is the best material for these roller shafts.

Cloud computing is internet computing that offers metering-based services to customers. It implies accessing data from a consolidated pool of computer resources that may be requested and consumed on-demand. It also offers computer resources via virtualized over the internet. The data center is the most essential in cloud computing which includes a group of servers on which Business records are kept and applications operate. The data center which contains servers, cables, air conditioning units, networks, etc. uses more electricity and emits a large quantity of Carbon-di-oxide (CO2) to the atmosphere. One of the most significant problems encountered in cloud technology is the optimizing of Energy Usage. Hence the idea of cloud computing originated. It is a concept for allowing all over, on-demand access to a shared pool of customizable computer resources[ Wanneng Shu et. A.,2014].

Due to the wide utilization of a 3-phase, squirrel-cage, self-excitation generator in renewable energy and isolated areas application, the paper deals with detailed design procedure of this type of generator, in a sequence and systematic manner. The design steps are obtained and executed in a Matlab file of the computer program to suit the newly designed constructions and parameters of the generator. In the new construction, the generator can be operated in a stable and saturation region. Due to the similarity in the construction of induction motor and generator, the formulas used in the calculation of equivalent circuit components of an induction motor may be utilized to calculate the parameters of the induction generator. To obtain optimized induction generator parameters and construction dimensions an ant colony algorithm is used to optimize these construction dimensions and generator parameters. The main objective parameters used in this algorithm are the generator efficiency, the excitation-capacitance, winding temperature rise, and minimum generator size is for 5 HP, 400 V, 50 Hz, and star connection generator.

In this study, the main objective is to clarify the relationship between the material properties of the work material and the grooving properties for various work materials from hard and brittle materials to metallic materials. In this paper, in order to investigate the grinding characteristics of diamond electroplated wire tools, including the wear characteristics, we conducted grooving experiments with borosilicate glass (Pyrex), which is a kind of hard and brittle material, and aluminum alloy (A5052), and tough pitch copper (C1100), a kind of metallic material, using diamond electroplated wire tools in a work material rotation method. As a result of the grooving experiments, it was clarified that the grooving characteristics of the work materials were influenced by the hardness and brittle behavior of the materials. The groove depth is influenced by the hardness and brittleness behavior of the material. When machining hard materials, the groove depth increases slowly in the initial stage of machining due to the poor bite of the wire tool, but increases rapidly as the machining progresses. On the other hand, the groove width does not depend on the machining time or speed, but is influenced by the hardness of the material and the ease with which plastic deformation occurs. The wear of the wire tool is also influenced by the hardness and brittleness of the material. In the machining of hard materials, the wear caused by stray wire and vibration in the early stages of machining was significant. The grinding ratio calculated from the ratio of the groove depth to the amount of grinding has a very different trend for hard and brittle materials and metallic materials. In the machining of hard and brittle materials, the amount of machining increased rapidly as machining progressed, so the grinding ratio also increased, but in metallic materials, the amount of machining itself was small and the grinding ratio did not increase. For A5052, the grinding ratio tended to decrease as machining progressed. Future work In the future, it is necessary to clarify the machining conditions to reduce the wear caused by stray wire tools and vibration during the initial machining of hard materials.

Pain in yam cultivation has been identified as critical areas in yam cultivation. The aim of the study was to compare the manual and mechanised yam minisett planting. To address this problem, heart rate (HR) of workers during mounding and ridging, and planting were measured as well as assessing economic feasibility in mechanising yam planting. Polar watch (RS 800 CX) was used to measure HR of the operations. Hoe and cutlasses were used for mounding and ridging. The HR of tractor operator during planting was determined. Descriptive statistics and analysis of variance (ANOVA) were done using GenStat software (VSN International, 2011). Statistical significance was carried out at p

Foreseeing assumes an indispensable part in setting an exchanging methodology or deciding the ideal opportunity to purchase or sell stock. We propose an element combination long transient memory-convolutional neural organization (LSTM-CNN) model, which joins highlights gained from various presentations of similar information, i.e., stock timetable and stock outline pictures, to anticipate stock costs. The proposed model is created by LSTM and CNN, which extricate impermanent and picture components. We assessed the proposed single model (CNN and LSTM) utilizing SPDR S&P 500 ETF information. Our LSTM-CNN combination highlight model surpasses single models in foreseeing evaluating. Also, we track down that the candle graph is the most precise image of a stock diagram that you can use to anticipate costs. Subsequently, this examination shows that prescient mistake can be viably decreased by utilizing a blend of transitory and picture components from similar information as opposed to utilizing these provisions independently.

The massive deployment of Internet of Things (IoT) is allowing Smart City projects and initiatives all over the world. Whereas IOT deals with intricate systems that integrates multiple disperse components towards their synergetic use. Due to resource constraints, there will be a problem in the future to provide all the services to the residents. To continue to serve and improve the standard of living of the growing population, it is necessary to develop smart cities. The Smart City aims to make optimal and sustainable use of all resources, while maintaining an appropriate balance between social, environmental and economic costs. The wireless sensors are attached to street lamp, water tank, dustbin. Sensors are then attached to Arduino microcontroller board where each and every necessary parameter for city are monitored and updated to cloud. The cloud is connected with Blynk server in turn which is connected to build in Blynk application of user's Android phone. The technology we use is IOT in this model a system of interconnected smart modules is developed where each and every parameter necessary for a city is monitored and updated to the cloud. Emphasis is given on how sensing and communication technologies of IOT can effectively be used in smart city monitoring. The project aims at developing a system which facilitates aids in collection of data with the help of interconnected modules consisting of multiple sensors useful for smart city monitoring. Further this project also includes controlling of some parameters like water, light and dust bin.

In recent years researchers are intensely using machine learning and employing AI techniques in the medical field particularly in the domain of cancer. Breast cancer is one such example and many studies have proposed CAD systems and algorithms to efficiently detect cancer cells and tumors. Breast cancer is one of the dreadful cancers accounting for a large portion of deaths caused due to cancer worldwide mostly affecting women, needs early detection for proper diagnosis, and subsequent decrease in death rate. Thus, for efficient classification, we implemented different ML techniques on Wisconsin dataset [1] namely SVM, KNN, Decision Tree, Random Forest, Naive Bayes using accuracy as a performance metric, and as per observance, SVM has shown better results when compared to other algorithms. Also, we worked on Breast Histopathology Images [2] scanned at 40x which had images of IDC which is one of the most common types of breast cancers. And to work with the image dataset along with EDA we used high-end techniques like a mobile net where smote a resampling was used to handle imbalanced class distribution, CNN, SVC, InceptionResNetV2 where frameworks like Tensor Flow, Keras were loaded for supporting the environment and smoothly implement the algorithms.

Customer satisfaction is the backbone of any business entity and supply chain optimization plays a vital role in customer satisfaction efforts. Supply chain inventory control is one of the scientific supply chain optimization methods for determining proper inventory levels at different stages or echelons of the supply chain to meet the requirements of the customers. The intention is to supply right type of material at exact time in appropriate quantities and at competitive rates. Supply chain inventory costs consist of costs to store, track and insure materials. Inventories that mishandled create substantial financial problems for a business, whether the mismanagement results in an inventory accumulation or an inventory shortage. Therefore, an examination of the right quantities to be kept in stock to meet the requirements, the strategic location, storage facilities and recordings of the goods or items should be done systematically such that the desired degree of service can be provided at competitive prices or at minimum ultimate cost. Major objective of inventory control in a multi echelon supply chain is to optimize inventory cost elements like transportation cost, carrying cost, holding cost and all other inventory related costs at all supply chain stages with an elevated service level at the end customer point. The supply chain inventory control becomes tough when the handling material is a perishable one as its deterioration rate is variable rather than constant. This article provides the study results of the deterioration rate of a perishable edible inventory at different selected environmental conditions. The focus of this article is to introduce a mathematical equation for the deterioration rate of the selected perishable inventory which is inevitable for the formulation of inventory models for its supply chain echelons.

PV solar energy is the upcoming king of the energy source in the world, which is the fastest growing, most available, sustainable, clean, and environmentally friendly renewable energy. The essential characteristic of PV solar energy is generating the maximum power at mid-day. At the same time, the energy demand is high during the daytime. Due to this, PV solar energy replaces the conventional energy demand at peak periods. The sun is the source of PV solar energy, and it changed into electricity directly by using solar cells, which are made from semiconductor materials called silicon. Therefore, PV solar energy plays a crucial role in providing usable energy, and as well as reducing carbon dioxide emissions. However, the solar energy generation systems not achieved the desired efficiency yet, because of many unsolved problems like weather conditions, losses, materials made by and so on. The aims of this paper is to review the current literature on the improvement of the PV solar energy generation system's overall performance. First, to figure out the existing challenges, like environmental factors and natural phenomena that affect the PV solar modules efficiency. Then it presents the techniques that are used to enhance the PV solar modules overall performance. Finally, to propose the best ways and techniques to improve the PV modules efficiency and suggest to further studies.

Cloud computing the technology which have the capability of modifying the method computing strongly, and storage resources will be accessed shortly. User Identification is an entity to detect the user who using the system or website. In information technology the protection of information consistently become a major issue to handle. The data might place in various locations in the world since it become particularly serious. The two main factors regarding cloud technology are information protection and security. The cloud operators can easily reach the sensitive information that affects the data security and protection measures. Therefore, this research protocol mainly focuses on secure data storage that always been a significant feature of quality of service. To guarantee the ‘rightness of users’ information in cloud storage system a Protection Aware User Identity and Data Storage (PAUIDS) algorithm is proposed that separates the document and independently stores the user information in the cloud storage servers. The proposed algorithm reduces the encryption and decryption time in a cloud storage system and providing secure and efficient data storage in cloud environments

The risk presented by world-wide weather transformation is known and so the government panel about weather transformation prompts that guidelines need to be consider limiting world-wide median temperature boost. I expect effective strategies meant for the statistic and administration of GHG exhausts for aim for positioning and determining the accomplishment of climate change minimization activities. ‘Carbon footprints’ are progressively being acknowledged as an important signal in the arena of GHG and carbon exhausts supervision. During COVID-19 pandemic the GHG percentage dropped considerably for a moment worldwide, but there is an immense need of lowering carbon foot printing with development of new hybrid renewable energy sources. This paper presents the innovative development in the same direction by development of rose model for automation of energy plants to reach its installed capacity with identification of MPPT along with optimum ratio of demand-supply. Paper also presents the analysis of proposed developments.

Now a day’s supported visual and audio cues for automatic depression assessment may be a fast emerging research subject. This comprehensive evaluation of existing methodologies focuses on machine learning (ML) algorithm and image processing (IP) algorithm, as documented in over sixty articles over the last ten years. There is a visual indicator of depression, several data collection procedures are used, and finally examined the previous year or existing datasets. In this article describes techniques and algorithms as well as methods for dimensionality reduction, visual feature extraction, regression approaches, and classification decision procedures, and also various fusion tactics. A significant meta-analysis of published data is given, based on performance indicators that are robust to chance, to identify general trends and important pressing concerns for further research using visual and verbal cues alone or in combination with signals for automated depression evaluation The suggested work also used deep learning and natural language processing to estimate depression levels based on current video data.&nbsp

Ascertaining infections in maize plants is through observation of the crop plant for visual indications which a farmer is able to relate to specific diseases. The perception of the farmer is prone to human error which may sometimes link some symptoms to the wrong disease and could impact the application of suitable preventive and curable routines to combat the identified diseases. Hence, accurate identification of crop plant disease is of high importance to a farmer to aid response to diseases. The objective of this article is to apply fuzzy set and interpolation technique to develop an expert system to carry out field-based identification and yield forecast for the maize plant. For this study, some associated factors were recognized for maize plant diseases and confirmed by a professional Botanists. For this study, a number of associated factors were identified for maize plant diseases and validated by experienced Botanists. Further to this, triangular membership functions was used to develop the fuzzy inference system model following the preprocessing of identified factors and related output. 32 inferred rules were formulated using IF-THEN statements which adopted the values of the factors as antecedent and the yield of maize plant as the consequent part of each rule for classification of the yield of maize plant. The Fuzzy model was simulated for each of the identified five factors. The simulation results showed that the risk factors identified; black moldy growth on kernels and ears, blights on leaves, rotten cobs, infected husks and black kernels and seed decay have noticeable influence on the maize plant yield if timely remedy is not administered. The study established that the utilisation of fuzzy technique is helpful to appraise the yield of maize such that the lesser the manifestation of identified associated features then the higher the yield of the maize plant.

In this paper Analysis and Design of different Structural elements of the football stadium are presented, with particular emphasis on the Combination of Steel Truss without and with Shell roof cover and its interȧction with the underlying reinforced concrete structures. The Football stadium considered for the study is of rectangular plan, with 85 m width and 140 m length and height of 19.5 m. The plan of Football Stadium is generated in AutoCAD 2016 software. The Stadium structure is composed of special moment – resisting framed. Wind velocity is taken as 39 mph and Seismic zone IV in this study. The proposed stadium is analysed using Equivȧlent static and dynamic ȧpproach by Reṣponse ṣpectrum ȧnd Time Hiṣtory ȧnȧlysis. In anȧlysing the ṣtructure, 21 load combinations are used. The grandstand ṣtructure is made of reinforced concrete and the roof is of ṣtructural steel using Pipe and Tube sections. Deȧd loȧdṣ, live loȧdṣ, wind ȧnd ṣeismic loȧdingṣ data are considered bȧsed on IS-875 (PART 1-3) 1987 ȧnd IS:1893 (Part 1):2016. IS456:2000 and SP16:1987 code is used for Design of R.C.C components such as Beȧm, Column, Seating Platform, Footing and IS 800:2007 code is used for Design of End Beȧring Plate connection with Truss member. Analysiṣ of truss and other elements is carried out with software program of Staad. Pro V8i SS6 and also the designs are carried out as per provisions of relevant Indian standards. On introduction of Shell-like roof for Open Stadium which is used not only to protect the Game from Glare of Sunshine and Rain but also appears unique and attractive. From the obtained results it is observed that the displacement due to Wind action in both X and Z direction reduces significantly by the introduction of Shell roof. Also, due to RSA and THA there is reduction in the displacement on introduction of Shell-like roof to an Open Stadium. Keywords

External public debt and foreign exchange reserve (FER) are performing a crucial role in the growth and development of countries. To examine the short-run and long-run dynamics among external public debt (EPD) and FER in Ethiopia, the study used 39 years data (1981 to 2019) from National bank of Ethiopia (NBE) and World Bank data sets. The Autoregressive Distributed Lag (ARDL) model with error correction model (ECM) was employed after checking the possible assumptions of economic series. The results of ADF test statistics confirms our economic series are stationary with a mixture of level form and first difference form. Bounds co-integration test suggests the existence of co-integration among the variables. According to the descriptive method of data analysis, on average, in Ethiopia the trend for service sector indicated that an ever improvement of the sector throughout the periods and supplementing the notion of change from agriculture base to service sector. On the other hand, according to ARDL model in the short -run on average trade tariff rate, share of manufacturing sector from the GDP, and lagged value of EPD itself predicts the EPD significantly at least at less than 10% level of significance . Moreover, the ECM revealed that in the long-run, financial development indicator, debt service payment, and average trade tariff rate were predicting the stock of FER for Ethiopian economy. Finally, the concerned body, the government of Ethiopia, should limit or reduce the amount of external debt (ED) inflows, and recheck the budget sources for financing different projects especially manufacturing industries rather than highly basing on external sources in the form of EPD, among others.

Online propagation of untrue information has been and is becoming an increasing problem. Understanding and modeling the diffusion of information on Online Social Networks (OSN's) of voluminous data is the prime concern. The paper provides the history of the epidemic spread and its analogy with untrue information. This paper provides a review of untrue information on online social networks and methods of detection of untrue information based on epidemiological models. Open research challenges and potential future research directions are also highlighted. The paper aimed at aiding research for the identification of untrue information on OSNs.

Recently a rapid increase has been seen in the technology and health care became an unavoidable sector because an enormous amount of data is collected day to day. The Big data analytics has many uses and it is associated to big data. It gives an important judgment in the area of health care. This paper aims to present a review of big data in health care that how big data can aid the healthcare. In this paper multiple research paper has been reviewed and the limitations and research gaps of these papers has been discussed accordingly.

This research paper analyzes the current technology adoption in the context of the COVID-19 pandemic. This worldwide health emergency condition has led to people being confined, and due to this confinement, the closure of education centers and face-to-face education led to online education. Teachers are forced to adapt to their custody, new methodological approaches, and dizzying pace, which present excellent stress levels. In the field of managing motivational and emotional processes, neuroeducation contribution leads to students’ meaningful learning. The symbiosis of neuroeducation and Communication Technology (ICT) creates a significant contribution in shifting of paradigm active today. Technology Company’s contribution to the COVID-19 pandemic is dependent on a relatively digital ecosystem while attracting various investors despite capital outflows. Moreover, this contribution is connected to the technology company’s capability to quickly pivot towards new models and services and deliver goods and services to the underserved in many cost-effective ways.

customer satisfaction is the main focus area in supply chain management and the distribution of goods plays a vital role in customer satisfaction. cost optimization and in time delivery leads to customer satisfaction. Optimization of Vehicle route plan is the method generally applied to deal it. such plans shall consider the minimization of pollution emissions. This paper proposes a optimization method to handle the vehicle routing problem(vrp). Genetic algorithm and fuzzy clustering algorithm are applied in the method.

The use of wireless sensor technology in various Internet of Things (IoT) applications is growing rapidly. With the exponential increase of smart devices and their applications, collecting and analyzing data is gradually becoming one of the most difficult tasks. As sensor nodes are powered by batteries, energy efficiency is essential. To that intention, before passing the final data to the central station, a sensor node should reduce redundancies in the received data from neighbor nodes. There will be some redundancy in the data because different sensor nodes typically notice the same phenomenon. Data aggregation is one of the most important approaches for eliminating data redundancy and improving energy efficiency, as well as extending the life time of wireless sensor networks. Furthermore, the effective data aggregation technique might help to reduce network traffic. In this paper we have proposed cluster based data aggregation using intelligent agents. The performance of the proposed scheme is compared with Centralized Data Aggregation (CDA) mechanism in IoT.

Nanotechnology is steadily transgressing from the laboratory to the commercial sphere and is enhancing products in a variety of sectors. Nanotechnology R&D has evolved from foundational discoveries aimed at understanding and exploiting nanoscale behaviour to an enabling technology. Nanomaterials are materials which are sized between 1 to 100 nm. Due to the basic characteristics of nanomaterials such as optical properties, reflection, transmission, absorption, and light emission, which are different from those of bulk materials, nanomaterials are useful in a variety of applications in different fields. In this paper the different types of nanomaterials have been outlined based upon their dimensions and applications in the field of electronics such as Quantum dots (QD’s) in solar cells and Carbon Nanotubes and graphene in FETs.

Crowdsourcing frameworks have been receiving a lot of enthusiastic acceptance these days, and there has been a rise in significant raise in concern as well. Crowdsourcing frameworks are excellent tools that can coordinate the human insights of specific instances, and organizations together globally and help comprehend and collaborate intricate chore. Notwithstanding, these central structures subject to the inadequacies of the trusted standard like standard financial foundations. For example, single motivation behind dissatisfaction, higher organizations cost, and security disclosure. An idea is to use a shared structure for crowdsourcing basis blockchain, wherein the work of the requester is directed by the swarm of workers without relying on central freely supporting systems or foretold customers that are inclined to organizations with enrolling certifiable characters. In thought, the proposed framework design would permit Users to enroll, post, or get an endeavor securely. The design would likewise furnish clients with undeniable degree of protection, and security, and the proposed system additionally has low administration costs. By extending the adaptability and adaptability of publicly supporting the reason for existing is to show the crowdsourcing logic with intelligent contract. As per this structure, a requester needs to deposit the venture money, while posting task. The stored deposited amount is escrowed with the framework, and on the concurred task fruition, the laborer will get the undertaking amount from the escrow. Because of this system, a requester won't need to pay more than what an assignment merits, as indicated by a rule unveiled when errand is posted; and every laborer will unquestionably get an installment dependent on the rules.

The current scenario e-learning approach got prominence during the covid-19 pandemic. Many institutions were greatly dependent on e-learning platforms like Edx, Coursera, Udemy, Swayam, Webex, Zoom, Google meet etc. to teach their students and also it became a major part of the education system. The purpose of the study is to identify the perception of learners and accordingly suggest faculty community and technology developing companies. In this context, the current study aims to measure the learners' perception towards e-learning with five dimensions are Usefulness, Ease-of-Use, Self-Efficacy, Attitude & Intention to Use, especially to measure at capital city of Telangana, namely Hyderabad. The learners' awareness of e-learning platforms is also examined along with the above variables. The hypothesis of the study is only one, 'which is there is no significance of users' perception found towards Usefulness, Ease-of-Use, Self-Efficacy, Attitude & Intention to Use etc. aspects of electronic Learning Approach’. Research has been carried out as a descriptive study basing on primary and secondary data sources and also with the help of a structured questionnaire, 121 learners have been examined. These learners are identified based on their availability and conducted a structured interview, in some cases its telephone interview and mail response. Thirty research articles were reviewed in the construction of a theoretical framework. To collect the responses, the convenience non-random sampling technique was used and has applied both the descriptive statistics and t-test to analyze data. The greater part of responders felt it is more useful, easy to use, it improves self-efficacy; they also have shown a positive attitude towards the e-learning approach. A high level of intention is shown towards the e-learning approach and platforms.

Dividend has been a matter of concern since the concept of limited liability of the firms came to existence. The uncertainty regarding dividend policy is supposed to hunker down unless we move away from whack-a-mole approach to a long-winded solution to the problem of dividend policy. The issue gets exacerbated because of inclination towards the valuation by the people who call the shots in the firms. This paper explores the association of dividend with the rising NPAs in the Indian banks. The findings of the paper are the testimony to the fact that dividend policy in the banks has issues and need to be checked and corrected. It is recommended in the paper to decouple dividends from the NPA by linking dividend policy with free cash flow. It is also recommended to adopt DVR shares to cater to the aspirations of those investors who do look for consistent dividend policy irrespective of the performance of the banks in a particular year.

Security is a prerequisite of each device that provides physical access to anyone and is logically expose to communication network attacks. The Internet of Things (IoT) must assure energy-saving provision due to the unique characteristics of IoT devices that comprise cost-effective, low power, and data delivery capacity. A Key-based Authentication scheme is a need without creating a bottleneck of communication for security in IoT integration. Security solutions viz., Authentication, Access control, and Key management are essential for the protection of communication in IoT applications. Public Key Cryptography (PKC) encapsulates multiple security functionalities and applications in conventional networks. The proposed Elliptic Curve Cryptography (ECC) Blended Authentication and Session Key Establishment Technique (EBASKET), an enhanced HPAKE scheme secures the IoT device interactions using Hash and Public Key Cryptography conjoined with a Stochastic Number. EBASKET authenticates and establishes Session Key for communicating IoT Devices using ECC that enhances the security resisting Key Disclosure, Man-in-The-Middle (MiTM), Relay threats. It incorporates an Elliptic Curve of 256 bits to achieve the 128 bits security level. EBASKET accomplishes Key Establishment utilizes Nonce as the Fragmentary Key after authenticating the intercommunicating Devices. It decreases the overall delay incurred reducing the communication overhead minimizing the quantity and magnitude of the messages exchange for Authentication. A secure Key Establishment for the Session uses a Stochastic, Hashing function, and ECC. The interactions throughout the Predeploying, Authenticating, and Key Establishing process cause a delay. The performance graph depicts that Key Establishment and authenticating the IoT devices using ECC and reducing communicational cost enhance security than Enhanced, Hybrid, and Lightweight Authentication Schemes.

Automatic Image Caption Generation is one of the core problems in the field of Deep Learning. Data Augmentation is a technique which helps in increasing the amount of data at hand and this is done by augmenting the training data using various techniques like flipping, rotating, Zooming, Brightening, etc. In this work, we create an Image Captioning model and check its robustness on all the major types of Image Augmentation techniques. The results show the fuzziness of the model while working with the same image but a different augmentation technique and because of this, a different caption is produced every time a different data augmentation technique is employed. We also show the change in the performance of the model after applying these augmentation techniques. Flickr8k dataset is used for this study along with BLEU score as the evaluation metric for the image captioning model.

The Chain of Custody is an intrinsic part of any inspection. Maintaining and evaluating the integrity of evidence procured from a crime scene is an important part that needs to be done properly by following a certain set of protocols to make the evidence admissible in the court. Keeping track of the evidence right from the moment it was collected from the crime scene till the time it reaches court is also a major task. It is important for the investigator to know how, where and who handles the evidence during analysis at each phase in order to safeguard the integrity of the evidence. Over a period of time, various tools and technologies have been created to handle evidence. Researchers from across the globe have presented various techniques on how evidence should be handled. Many researchers have even incorporated blockchain technology with the chain of custody or life cycle of evidence to make the process stronger. The growth in this domain has been at a rapid pace. This paper presents a method on “Maintaining and Evaluating the Integrity of a Digital Evidence in Chain of Custody” using a global positioning system. The methodology focuses on the use of global positioning system tags or chips which when embedded with the collected evidence enables an investigator to track the evidence throughout its life cycle. The proposed methodology aims to help the investigators to keep track of the evidence throughout its life cycle using very basic tools like FTK Imager and technology like a global positioning system.&nbsp

The cold formed steel section are increasingly used as a structural elements in low rise buildings in recent times, due to the advantages like economic production, easy transportation, low labor cost and high strength to weight ratio. In the current work a G+3 storied building frame is studied for seismic and wind load using Staad-pro software. Light gauge section is taken for beam and slab elements. The seismic analysis is carried out by Equivalent static method. After analysis the results such as story displacement, story drift, Base shear and time period are compared for different models. The building frame is also analyzed for wind load. In the current work cross bracing using Light gauge element used as a retrofitting technique. The retrofitted building frame is analyzed for wind and seismic loads and results obtained for time period, storey displacement, storey drift and base shear values are compared with the corresponding values of base frame. The results of retrofitted frame showed shortening in time period, storey displacement and story drift values in X&Z directions indicating the increased ductility, Stiffness and strength of structure. Hence the proposed retrofitting technique could be believed to achieve success results in increased strength and ductility values required by wind and seismic loading.

The massive volume of data stored in computer files and databases is rapidly increasing. Users of these data, on the other hand, demand more complex information from databases. The video data have exponential growth towards accessing and storing. The vital problem associated to video data is efficient, qualitative and fast accessing. We talk about how video pictures are clustered. We presume video clips have been divided into shots, each of which is denoted by a collection of key frames. As a result, video clustering is limited to still key frame pictures. In amble database finding the qualified data set (clusters) is quite time-taking job. The video data mining relate to multi–lingual text, numeric, image, video, audio, graphical, temporal, relational and categorical data. It may be any kind of information medium that can be represented, processed, stored, fast accessing or summarization of clusters are required due to which significant frame-set is formed. Due to sampling error and test reliability in video, substantial changes of more than one frame are predicted. The goal of this article is to show how to employ a familiar and easy nonparametric statistical approach (chi-square) to select eligible data/framesets for analysis. The chi-square model illustrated here is a straightforward, sensible, fast, reduce saddle, and easiest method. Skimming/ Summarization and clipping technique are further enhanced by this technique along with video database maintenance technique from simple descriptors to a complex description schemes like spatial and temporal or high dimensional indexing.&nbsp

Composites have proved their usefulness in the automotive industry during recent years. Many automobile companies use them in different parts to reduce weight without hampering strength. In a composite material, Lay-up sequence and orientation highly affects the properties of the laminate. Therefore, it is important to perform design optimization on a component to achieve high strength in minimum weight. This paper deals with the optimization of lay-up for composite Racing Seat using finite element analysis. Different lay-up sequences for laminates including, cross-ply [0/90]n, angle-ply [±α]n, and [0/90/±α]n are analysed. The lay-up sequence, orientation and ply number are optimized using composite material carbon fibre/Epoxy. Driver’s ergonomics and impact sustainability are considered constraints for weight optimization. Driver’s ergonomics were based on 95th percentile male and 5th percentile female rule. Force analysis is performed on the seat according to SFI 39.2 to evaluate the strength requirement. Finite element analysis of composite racing seat is performed via commercial finite element code ANSYS and using the capabilities of ANSYS Composite PrepPost (ACP) to form desired composite lay-up. A finite element code is based on classical lamination theory; including Puck’s failure criterion for first-ply failure. The seat is divided into three portions with a different number of layers considering the values and specific nature of acting forces; which resulted in different thicknesses in different regions. The optimization results show that for all the angles of Angle-ply laminate considered, Angle-ply laminates with an angle of 45⁰ provides a more optimum design. The minimum weight obtained is 10.15 kg.

This paper approaches an IoT based vehicle health monitoring system that is embedded for detecting the condition of a vehicle by monitoring the internal parameters such as heating rate, engine oil level and status of the CO of the vehicle. It is a real time vehicle health monitoring system is designed and developed to detect and identify the actuator and sensor faults created by automatically or manually by the user of the vehicle. Actually, Vehicles need repair after a certain interval of time and if are not repaired at fixed intervals, it can lead to loss of life of the persons travelling on it and there are many key issues which can affect the vehicle. So, the primary objective of this system is developing an IoT based embedded system that can detect the internal condition of a vehicle by evaluating the various parameters that are used to examine in the vehicle’s current health condition. In fact, this is a real time evaluation system that can be used for rapid condition screening. As a result, it provides all reliable information about the vehicle conditions. This IoT based system claims that it can detect and identify actuator and sensor faults with almost minimal detection latency even after lots of disturbances and uncertainties.&nbsp

Text to speech conversion is one of the applications of machine learning. It is widely used in search engines, standalone applications, web applications, chatbots and android applications. But still there is need to upgrade text to speech system so that we can get more interactive and user-friendly application. Traditional text to speech application has monotonous voice as output which does not has emotions in it and seems to be more mechanized. So, there is need to improvise the existing system by embedding the flavour of emotions in it. Existing text to speech cannot be used in story telling applications also it does not provide effective communication. Most of the Text to Speech systems are developed using algorithms such as Support Vector Machine (SVM), Naïve Bayes etc. Emotion Based Text to Speech System will help to improvise the existing Text to Speech system. With the help of machine learning and deep learning algorithm such as Recurrent Neural Network can be used for performing sentiment analysis and semantic analysis on the input text. We are going to use neural network which is more effective and help to maintain a relation between previous word and next word. Emotion based text to speech system will be able to identify four emotions ‘happy’, ‘sad’, ‘angry’ and ‘neutral’. Emotion based text to speech system will be beneficial for educational purpose like listening stories from storytelling applications for young budding children. Emotion based text to speech is going to be serviceable for visually impaired individuals.

There are two aspects to my project, one is optimized bi-directional drone designing and other is its path planning. The optimization in design lies in the fact that my drone can carry load both in +z and –z axis if its direction of motion is in x-y plane. This design optimization helps the drone to carry more payload than drones of same frame(basic chassis) weight category. In other words, my drone has greater payload to its own weight ratio(almost 0.8) than other drones with almost similar or same functionalities. Coming to path planning algorithm, I have taken a mathematical induction approach to solve the problem statement by clearly defining our conditions to follow to remain in the specified path along with constraints lying in the path. My goal as a path planner has to ensure that the drone follows the conditions specified without grappling into obstacles. Also, to achieve the desired goal in least possible time. The paths traversed by the drone would be stored into the memory processing system of the drone for future development of algorithm.&nbsp

In recent years, there has been a lot of research on how to achieve interaction between users and virtual objects using augmented reality. Interaction technologies in augmented reality need to enable users to handle virtual objects intuitively. In addition, since hands are the main means of interaction with objects in real life, it is also necessary to enable interaction operations with hands on virtual objects [1]. In order to make it possible to intuitively handle objects in virtual space using hands in real space, it is necessary to consider whether physical phenomena in real space and virtual space are correctly superimposed (physical consistency). In this study, we proposed a system that allows users to intuitively handle the deformation, movement, and merging of virtual objects in augmented reality. The system was then used by four university students to compare it with existing studies [2].