In general, two risk factors such as alcohol expectancy and impulsivity have been concerned with alcohol abuse Currently, many people have been addicted to alcoholism and have an Alcohol Use Disorder (AUD) that affects neurons behavior in the human brain. Still, how such risk factors interrelate to estimate the alcoholism. To solve this problem, Fuzzy C-Regression based Alcoholism Detection (FCRAD) method has been proposed that segments the Region-Of-Interests (ROIs) from the human brain image to predict the Gray Matter Volume (GMV) reduction in the right posterior insula in women and the left thalamus in both men and women efficiently. However, it requires the detection of GMV reduction in the other brain image regions. This multi-modality can decrease the fuzziness of the partition and the crisp membership degrees were not derived easily. Therefore in this article, the GMV reduction in other regions of the brain images including right posterior insula in women and left thalamus in both men and women has been detected, an Improved FCRAD (IFCRAD) method is proposed to simplify the segmentation of the brain images by considering the second regularization term in the objective function of the FCR to take into account the noisy data. Also, the Euclidean distance is replaced with the Voronoi distance for computing different fuzzy membership functions. Moreover, new error measure and reward function are used in the objective function of the FCR to reward nearly crisp membership functions and to obtain more crisp partition. So, the brain images are segmented into gray-matter images that derive the ROIs to analyze the GMV reduction with less complexity. Finally, the experimental results illustrate the proposed IFCRAD method achieves higher accuracy than the existing AD methods.

As the unfold boom of Internet get entry to the need for multimedia specifically digital images are widely applicable in various areas such as military, medical, science, education, advertising, entertainment and many others. As human beings around the world exchange these digital images assuring the privacy and reliability of images has turn out to be a principal concern. Thus ciphering the image comes into account. The relationship between the pixel values of neighboring pixels of a plain digital image is strong. The proposed cryptosystem breaks the relationship of neighboring pixel values by altering its position with the use of Arnold cat map equation. Then the shuffled image is diffused using a symmetric keys to yield an encrypted image. The experimental result shows that our cryptosystem effectively cipher/decipher the digital image with external secret key. The experimental result of the proposed cryptosystem was done using MATLAB R2018a and shows it is capable of producing an encrypted image with extremely low relationship coefficients of neighboring pixels. We also found that it is very sensitive to any input key and parameters change. Consequently the proposed cryptosystem has strong encryption quality and invulnerable way for a colored image. Considering the security necessities, this work can have a focus in securing medical end results, country wide secrets and plenty of others which are exchanged over the Internet.

In this paper we have represented new method of optimization of the robot structure for various reverted operation In today’s age the rapid development in robotics is been noticed, robots are developed for multipurpose operations in industrial field. The high inertia load are created due to emergency operation, the gradually applied and suddenly applied loads are developed, the structure should withstand this type of loads Furthermore, the robot has a certain frequency of operation, & the structure frequency needs to be higher than that of robot, to avoid failure due to resonance. The structure has a complex web of members hence finite element analysis is needed to perform analysis. The new structure, the robot is mounted in an inverted manner on the ceiling, this adds to the complexity of the analysis. The proposed method is compared with state of art systems.

With increase in the data transmission and data exchange in daily life, along with the increasing data thefts and other adversary snooping, the security and privacy of data has become an important thing to be concerned about. Throughout the years many different approach, ideas, algorithms and protocols have been developed to protect the data from snoopers and adversaries and ensure secure communication. Every approach has its own pros and cons. This project is an attempt to combine two different approaches of data protection- (Steganography and Cryptography) to develop a powerful and secured method for data exchange.

Road extraction from satellite images has several Applications such as geographic information system (GIS). Having an accurate and up-to-date road network database will facilitate transportation, disaster management and GPS navigation. Most active field of research for automatic extraction of road network involves semantic segmentation using convolutional neural network (CNN). Although they can produce accurate results, typically the models give up performance for accuracy and vice-versa. In this paper, we are proposing architecture for semantic segmentation of road networks using Atrous Spatial Pyramid Pooling (ASPP). The network contains residual blocks for extracting low level features. Atrous convolutions with different dilation rates are taken and spatial pyramid pooling is performed on these features for extracting the spatial information. The low level features from residual blocks are added to the multi scale context information to produce the final segmentation image. Our proposed model significantly reduces the number of parameters that are required to train the model. The proposed model was trained on the Massachusetts roads dataset and the results have shown that our model produces superior results than that of popular state-of-the art models.

Lung Cancer is a disease of irregular cells multiplying and growing into a tumour. It’s hard to believe, but lung cancer is the primary cause of cancer deaths among both women and men. Every year more people die of lung cancer than of colon, prostate and Breast cancers. Some important facts of lung cancer are Excluding Skin cancer, lung cancer is the second most common cancer in both women and men. Statistics from Indian Council of Medical Research (ICMR) recommend that lung cancer is fast turning into a plague in India. It is a high mortality cancer due to poor access to affordable health care and diagnosis at late stage. At the time of diagnosis only 15 percent of the cases of lung cancer are curable. Due to the nature of the disease patients with lung cancer present themselves for diagnosis at a much later stage than other cancers. Globally lung cancer accounts for 8.4% percent of all cancers in women and 14.5% in men. For lung cancer Smoking is the single largest contributor. Other causes are exposure to carcinogenic toxins like radon, asbestos, radiation and air pollutants. Exposure to women to smoke from the burning of charcoal for cooking is also a cause of lung cancer. About 20 percent we can reduce the risk of lung cancer by doing physical activities and exercise is known to improve lung function. In recent times, image processing measures are frequently used in a number of medical areas for enlargement of the image in preceding recognition and managing periods, where the instant aspect is really important to determine the abnormality problems in objective figures, mainly in a variety of malignancy tumours such as lung cancer, breast cancer etc.

This paper introduces a new decision tree algorithm Diabetes Prediction Algorithm (DPA), for the early prediction of diabetes based on the datasets. The datasets are collected by using Internet of Things (IOT) Diabetes Sensors, comprises of 15000 records, out of which 11250 records are used for training purpose and 3750 are used for testing purpose. The proposed algorithm DPA yielded an accuracy of 90.02 %, specificity of 92.60 %, and precision of 89.17% and error rate of 9.98%. further, the proposed algorithm is compared with existing approaches. Currently there are numerous algorithms available which are not complete accurate and DPA helps.

In the internal combustion Diesel engines the most important subsystem is Intake manifold and Exhaust manifold. In the intake manifold which supplies fresh air –fuel mixture in to the cylinders where combustion takes place at high temperature and high pressure. After exhaust gases scavenged through valves from the cylinders, these gases past exhaust manifold an outlet, through which the gases flow into exhaust pipes from there to the emission control equipment of engine which consists of catalytic and thermal converters. The development of swirl can be enhanced by re-designing of inlet port of an Engine. There is further development in the swirl due to combustion process to another maximum part way in to the power stroke. Swirl can promotes the combustion process in a better way and causes efficiency increase. Better mixing of air – fuel there is a little bit changing the inlet and exhaust valve. Valve stem diameter is 9.5mm, Inlet valve diameter is 36mm, Exhaust Valve diameter is 28mm by varying the pitch 1.0mm to 2mm and thread depth of cut as 4mm and three thread per inch from this arrangement to investigate the performance by enhancing the swirl of air flow to get betterment in the performance and decrease in emissions in a (DI) direct injection diesel engine with single cylinder when compared with normal engine.

Raising rate and require of power has led a lot of organizations to discover elegant ways for monitoring, controlling and reduction energy. To create an innovative idea to reduce the rate of energy consumption smart EMS (Energy Management System) is proposed in this paper. To develop IoT technologies and Big Data is used to improved hold energy utilization in commercial, housing and industrial sectors. An EMS is used to build smart homes is proposed for he developed cities. In this system, every residence tool is interfaced with a data attainment module that is an IoT object with an exclusive IP address ensuing in a huge mesh wireless network of devices. The data gaining SoC (System on Chip) module collects energy utilization data from every device of every stylish residence and send data to a centralized server for supplementary handing out and study. This information from all housing areas accumulates in the utility’s server as Big Data. EMS consumes off-the-shelf BI (Business Intelligence) and Big Data surveys software packages which improves the energy usages also to assemble user order. While air conditioning gives to 60% of power use in American countries, HVAC (Ventilation, Air Conditioning and Heating) are in use as a research to approve the proposed system.

Speculation in commodity market is an important indicator that affects the prices of the commodities. Speculation is known as the purchase of a good for resale, or for temporary sale of a good with the purpose of repurchasing it later by hoping that earning profit from an interceding price change. Normally, excessive speculation in commodity market pushes up the prices and speculation create more fluctuations in prices. In this background, the studies focus on trend of speculation in gold future returns and also assess the short – run relationship between Gold futures returns and Speculative ratio. The data have been obtained from the MCX website. The statistical and econometric tools, such as, descriptive statistics, OLS Regression Model and Granger causality tests have been applied to analyze the data. The result of the study reveals that, time trend affects the speculation and there is no short run relationship between Gold futures returns and Gold Speculative ratio. Hence, it is proved that speculation is independent of futures price.

An integrated assessment of morphometric parameters has been conducted in this study with an objective of to develop the watershed and manage the available water resources in a hard rock terrain area at Karkala Taluk, Karnataka. According to the analysis of the Karkala micro-watershed, which is classified as closed basin with third-order dendritic drainage pattern, the watershed has a dense canopy of vegetation, a low relief, minute runoff, and a high infiltration rate. Drainage patterns are not greatly affected by geological structures. The basin's elongated shape is supported by the form factor, circulation ratio, and elongation ratio numbers. The Karkala micro-watershed's longitudinal profile exhibits a sharp gradient in the initial stages that has been progressively flattening as the river has weakened. Hypsometric integral found for the Karkala micro watershed is 0.5, showing the maturity stage of the basin. Low values for drainage density and texture, stream frequency, and infiltration number suggest that permeable rocks are underling and this controls the runoff in study area. Therefore, the integrated results of the morphometric assessment at the micro watershed level would be helpful for developing and managing water resources as well as for water harvesting.

In every organization, use of online services is increasing. With this the sensitive data is carried over internet on daily basis. Hence, there is a chance of misleading the data by unauthorized parties. So, there is need to provide security for that data and cryptography is the science that helps in providing security. By using cryptography different types of security algorithms have been developed. Three fish is a symmetric-key and tweakable block cipher algorithm designed as a part of the skein hash function. The strength of three fish encryption relies on 128-bit tweak value. The proposed work focuses on strengthening Encryption Process by implementing tweak buffer along with input. Whereas key scheduling is secured by applying SHA-256 algorithm. SHA-256 is a secured hash function which belongs to SHA-2 family. Three Fish is used in providing security on software and hardware. It is also implemented in electronic media such as transactions like banking.

It is one of the biggest challenges to land an unmanned aerial vehicle (UAV). Landing it by making its own decisions is almost impossible even if progress has been made in developing deep learning algorithms, which are doing a great job in the Artificial Intelligence sector. But these algorithms require a large amount of data to get optimum results. For a Type-I civilization collecting data while landing UAV on another planet is not feasible. But there is one hack all the required data can be collected by creating a simulation that is cost-effective, time-saving, and safe too. This is a small step toward making an Intelligent UAV that can make its own decisions while landing on a surface other than Earth's surface. Therefore, the simulation has been created inside gaming engine from which the required training data can be collected. And by using that training data, deep neural networks are trained. Also deployed those trained models into the simulation and checked their performance

This study was carried out to evaluate the effect of pre-treatment technique (blanching) and temperature on the drying characteristics of the red date variety. Thin layer drying experiments were conducted at varying drying temperatures of 50, 60 and 70 °C using electrical laboratory oven and cabinet dryer with a constant air velocity of 1.4 m/s. Other sets of samples were blanched at 50 °C for 2-, 5- and 10-minutes before being dried at varying drying temperatures of 50, 60 and 70 °C respectively under the same drying conditions. The drying process took place mainly in the falling rate period indicating that the mechanism that governed moisture movement in the samples was diffusion.The drying curve showed that blanched red date fruits had higher drying rate than the un-blanched samples. The result is useful for farmers, researchers, and the processing industry in designing a reliable machine for processing red date fruit which will boost the small and medium scale production of the fruit in Nigeria.

Water uses is increasing day by day. As development continues, the demand for water is increasing. Water is require for daily routine, for irrigation, for fish and wildlife and for industrial use, not only water but pure water is require. This is a helpful approach to make people or authorities aware and alert about water quality in real-time situation. In this paper, the proposed technology helps to monitor the water quality in real time situation or environment. The technology such as Internet of Things, Wireless Sensor Networkand Cloud Computingare used in this approach for water quality parameters (pH, minerals and Temperature) measuring in real-time environment. For water quality prediction and analysis, a training data set has been prepared and these training data sets use for categorize utility of water in different field. The sensor sensed the water parameters and send this sensed value to the cloud server for processing. These data compared with training data set. In this paper monitor data classify by using Naive Bayes and the utility of water can be predicted by Recurrent Neural Network. The resultant of this proposed approach are: it gives high accuracy and the response time of this approach is very less comparatively.

The main goal of the current article was to review challenges of online purchasing. Theoretically, this research has few benefits and has importance which the findings of this study will listing the crucial challenges that need to be addressed and tackled to achieve the success of E-commerce (and precisely electronic-shopping) in companies. It encourages additional utilization of technologies of Electronic-Commerce, styles the e-transaction practice at ease, boosts the amount of acknowledgement and adoption of this online technology, results in advancement of online customer responsibility, presents the loyalty concept, raise customer satisfaction, sustains longer-term relationship with clienteles and contributions to obtaining of competitive advantages.

Present manuscript deals with the sliding wear behaviour of SS- 304 and SS-316 with and without oxidation prior to wear test. The dry sliding wear performance of the steel samples was noted in as received conditions and then after oxidizing the sample at 600°C for 6 hours. The variations in hardness of both the steels before and after oxidation were estimated. The dry sliding wear tests were carried out using pin on disk technique at different sliding velocity and different loads, by using hardened steels counter surface. The worn surfaces were characterised by using FE-SEM/EDS analysis to ascertain the mechanism of wear in the samples. From various characterisation results and observations of volume loss, the effect of prior oxidation was analysed for the both stainless steels. It has been found that the oxidation leads to the formation of some protective oxides which leads to increase in the hardness of the samples due to which the wear performance was improved compared to the as received alloys.

The article explains evolution of transportation sector and concept of public transport. It links with the transportation scenarios in India and Mumbai as a case. The importance of public transportation in India and Mumbai is explained by quoting various numerical datasets published by authorities and research agencies. The development of public transport sector will mostly depend upon the total number of private vehicles and registrations. In Mumbai, currently the trend of using public transport is decreasing while on other hand private vehicles registration is increasing day by day. Proposed transportation projects of MMRDA will give relief to the internal traffic if implemented on time. But still they need to be supported by the additional planning interventions such as planning for industrial areas, business centers and growth centers on periphery of MMR. New transportation modes are required to be experimented since existing modes are insufficient to handle the public transport demand of MMR. The proposed waterway project, need for new airports and integration of last mile connectivity through public transport is emphasized relating to the proposed projects of MMRDA.

Since the current EGR has a problem, it makes a local decision during the route discovery process to choose the next neighbour node that has the highest residual energy. This could be the worst case scenario if there is no way to stop the process and the intermediate node has lower energy than the alternate path. To solve this issue, we suggested the Enhanced Energy Aware Geographic Routing (EEGR) protocol. In EEGR, we use a modified transmission energy strategy along with numerous route requests at the destination and replies that have better node residual energy. Our simulation findings demonstrate the enhancement over the current EGR routing mechanism.

This research conducted on the Catur sub-watershed-Kare District with the area of 190.85 km2. The study location is oriented on the Kare District-Madiun Regency which included four villages such as the Kepel, Kuwiran, Kare, and the Bodag. This research intended to study the value of erosion and sedimentation for analyzing the Erosion Danger Level (TBE). Result of the TBE analysis can be used as the base on determining the Village Soil Conservation Plan (RKTD) in the Kare District. The pparameters of the Catur sub-watershed condition during the 10 years will be processed by using the AVSWAT 2000 for producing the value of erosian and sedimentation on the Catur sub-watershed. Result shows that the Erosion Danger Level (TBE) in the Bodag village is in the level of light to heavy (21.89%), however, in the Kuwiran village is in the level of light to heavy (14.46%), in the Kepel village is in the level of light to moderate (22.23%), and in the Kare village is in the level of light to moderate (41.31%). The Village Land Conservation Plan that is applied in the Kare sub-district are conservation by the vegetative and mechanical method, however, in the Bodag village and Kuwiran by the vegetative conservation method with the regulation of land use, in the Kare village by the vegetative and mechanical conservation method with the regulation of land use and by building the two check dams, and in the Kepel village by the mechanical conservation method with building a check dam. The result is hoped as the reference for the recovery and management of the sustainable soil conservation.

Traffic monitoring and management is one of the most crucial tasks of governing bodies in modern big cities. With each passing day the traffic problem grows in complexity due to the continuous increase of participating vehicles and the hard expansion of the road network and parking places. In this article we introduce a new method for vehicle detection and localization in parking lots using high resolution UAV images. In order to end up with practical and yet effective approach, which could be implemented on low computing hardware resources and integrated with the camera in the UAV, we considered simple steps in the proposed algorithm for optimization. It follows the machine learning pipeline such as preprocessing, sensing, feature extraction, training and classification. In preprocessing the images are thresholded iteratively in multiple color spaces to extract the candidate regions of interest (ROI). The algorithm relies on point and shape features using fast techniques in the feature extraction. The features are then clustered by the K-means algorithm and represented by the resulted clusters’ centers. Region based linear classification is finally applied using SVM to classify if the object is a vehicle or else. The proposed approach proved high detection and classification accuracy more than 86% and still running under the low complexity constraint..

DNA Sequencing plays a vital role in the modern research. It allows a large number of multiple areas to progress, as well as genetics, meta-genetics, and phylogenetics. DNA Sequencing involves extracting and reading the strands of DNA. This research paper aims at comparing DNA Sequencing using “Machine Learning algorithms (Decision Trees, Random Forest, and Naive Bayes) and Deep Learning algorithms (Transform Learning and CNN)”. The aim of our proposed system is to implement a better prediction model for DNA research and get the most accurate results out of it. The “machine learning and deep learning models” which are being considered are the most used and reputed. A prediction accuracy of the higher range in deep learning is also being used which is also the better performer in different medical domains. The proposed models include “Decision Tree, Random Forest, Naive Bayes, CNN, and Transform Learning”. The Naive Bayes method gave greater accuracy of 98.00 percent in machine learning and the transform learning algorithm produced better accuracy of 94.57 percent in deep learning, respectively.

In India about 760 millions of farmers are engaged in agriculture. According to an estimate more than 60 per cent of the farmers have no access to any source of modern agriculture information. Reaching to these unreached farmers is a great big challenge before the extension system. Now times 21st century declared to be the age of information and communication technology. This is the time when more people everywhere are involved in acquiring new knowledge and skills. The need of ICT interventions are felt need of the hour in agriculture. Thus, it can be said that the twenty first century, it is necessary to use advanced communication media for rural and agricultural development in its proper perspective of message dissemination and feedback along with people’s participation and contribution so as to meet the felt needs of people living under diverse socio-economic conditions and here an extension agent should act as a catalyst for transfer of farm technologies. Hence, a present study was taken to assess the utilization pattern of ICT tools among extension personnel. The present study was conducted in Cuddalore District of Tamil Nadu. The 120 extension personnel working in the state department of agriculture were selected as sample for the study by simple random sampling method. The Data were collected through well structured questionnaire. Careful analysis, tabulation and classification of data were done. Mean, frequency, percentage were employed for the interpretation of results. The results showed that majority (70.83 per cent) of the extension personnel were utilized the internet / web services for gaining knowledge followed by (41.67 per cent) of the extension personnel were utilized agriportals for transfer of technology.

This study aimed to check the temporal variations in Air Pollution levels using Air Quality Index in major cities spanning geographically across India and understand the correlation between the severity of Covid-19 and the concentrations of the six key air pollutants. The study considered a tenure between May-June 2020 when there was a drastic shift in human behavior due to Unlock period in India. It employed Spearman’s Rank Correlation Coefficient, which better understands monotonic function association between two variables for correlation. The study successfully established statistical evidence of improvement in the air quality of 29% (average) in May and June of 2020 compared to 2019 in the cities under study. A significant correlation was also established between air pollution and covid-19 across various cities in India, with Delhi and Mumbai exhibiting a weak negative correlation. At the same time, Hyderabad and Bengaluru showed a moderate negative correlation. On the other hand, the city of Chennai implied a strong positive correlation between air pollution and Covid-19.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a causative agent for global pandemic disease nCOVID’19, has directed the attention of the scientific community towards the development of effective vaccines and drugs. Attempts are being made for repurposing existing drugs known for their antiviral activities, and test the traditional herbal medicines, natural compounds, nutraceuticals known for their health benefiting and immune-boosting activity against SARSCoV-2. Multidimensional approach is employed to find effective drugs against SARS-CoV2. In this study, 108 natural compounds were examined in inhibiting human protein targets which are responsible for worsening the condition of COVID-19 through a virtual screening approach. This study has screened the natural compounds 3-Hydroxydecanedioic acid, Cucurbitacin S, Diosgenin which may exhibit inhibitory activity against IL-1 receptor,IL-6 receptor respectively. It has also revealed that Dihydro-beta-ergocryptine and cucurbitacin S may exhibit inhibitory activity against TNF-alpha receptor. Further it concluded that exfoliazone shows better inhibitory activity against P38-MAPK and NF-κB pathway inhibitory proteins. These natural compounds could lead the way for future drug discovery, design and development. This will help researchers to scout new drugs in drug discovery(in-vitro and in-vivo studies).

Carbon Nanotubes reinforced composite materials are widely used in aerospace engineering due to high strength-to-weight ratios, which can be tailored per requirements. The behaviour of the composite structures depends on various factors like, Lamination scheme, Ply orientation and loading conditions. In the present work, Static deflection and stresses analysis of a Carbon Nano Tube reinforced laminated hybrid composite plate extends the First Order Shear Deformation Theory with variable ply angles and volume fractions of CNT. A micromechanics model based on the Mori-Tanaka method is used to calculate the properties of CNT reinforced laminate. In this analysis, hybrid composite plates mainly consist of Graphite/epoxy, Kevlar/epoxy and CNT/ polystyrene sub-laminates. The lamination scheme and CNT volume fraction play a vital role in the non-dimensional deflections and stresses. The main intention of this work is to enhance the suitability of CNT reinforced Hybrid composite plates under static loading for structural applications.

Privacy is an important feature in transmission of data. Due to continuous flow of data over network, there may be a chance of attacks on sensitive data, either passive or active. To provide security, Cryptography plays a vital role by scrambling data into unreadable form. Several techniques have been developed to provide security for digital data transmission. AES is one of the efficient encryption engaged for the past decade. There is every need to improve its security mechanism periodically. In this paper we implement Transpose mix column and S-BOX Rotation to enhance efficiency. This proposed model provides more Avalanche Effect than traditional AES. Moreover, Diffusion and Confusion are crucial in Cryptography. The more the diffusion rate the more secure the data is and is shown in this proposed model. However, S-Box rotation provides more confusion. AES is most efficiently used in Wi-Fi, particularly in WPA2-PSK(AES), so enhancing security mechanism is needed.

In order to fulfil the demands of modern IT, it is necessary to address the many limitations of current networking infrastructures. Software Defined Network (SDN) is the new networking technique to get around these constraints. Existing Network Infrastructures use static switches, which results in a substandard utilization of the network resources, which is one of the main problems. The main issues with the present networking trend are the slow response and delays. In this research different load-balancing algorithms for Software Defined Network based Data Center network is implemented to overcome mentioned issues. The POX controller is used in the implementation and the Mininet emulator is used to simulate the network functionality. The load balancing algorithms program is written in the Python language, which is also used for creation of the network topology. Finally, Siege HTTP load testing tool is used to test network performance. The testing is focused on Quality of Service pa- rameters transaction rate, average response time and throughput. The results show that the Weighted algorithm outperforms all other considered algorithms.

Volatility is a standard measure of financial vulnerability and it plays a vital role in analyzing the risk of the securities market. It is traditionally measured using the standard deviation, which indicates how the price of a stock is clustered around the mean or moving average. The intent of the study is to analyse the volatility clustering of six select sectoral indices such as S&P BSE AUTO (Automobile), S&P BSE BANKEX (Bank) , S&P BSE FMCG (Fast Moving Consumer Goods), S&P BSE IT (Information Technology), S&P BSE METAL ( Metals), and S&P BSE OIL & GAS (Oil & Gas Industries). A sample of 2726 days of observations for 11 years period from 03.01.2011 to 31.12.2021 has been taken for the study. The econometric model namely ARCH and GARCH have been applied to analyse the data. The result of the study reveals the presence of volatility clustering in the select six sectoral indices. Metal Sector has shown the higher phase of volatility.

Plant diseases are very impactful towards the overall effectiveness and quality management of the agricultural sector. In recent years, deep learning methods have been used as a way to identify these diseases, based on neural networks. The study presents GLCM and LSTM Recurrent Neural Networks Integrated with Machine Learning towards the identification of plant diseases. It has been found that the process is very accurate and can assess diverse plants disease characteristics dataset as well.

Mobile Ad Hoc Network (MANET) routing is highly dynamic network & challenged by power and bandwidth constrained as well as frequent topology changes to which it must adapt to and converge quickly. As we know that it is not possible to give a continuous significant amount of power to mobile devices of MANET so that it could be active for long time. As MANET nodes are powered by batteries with limited lifetime. One of the most existing challenge for MANET is in this Energy constrained to increase the time between recharging. It is possible if and only if energy consumption in communication is less without increasing interruption or packet loss.

Agriculture is a subdivision that provides considerably to the business-related incident of our country. As the world's population grows, Agricultural production has overtaken all other industries as its most significant source of growth. Because the need for food was lower in the 1970’s due to a smaller population compared to today's population statistics, that think that the world's public will extend to 9.9 billion by 2050, humanity must plan for agricultural production to feed these exponentially growing numbers. Therefore, we need huge agricultural lands to produce the grains and crops necessary for the current population. But much of the formerly arable land has been abandoned during periods of drought, and due to poor planning and poor water management during the dry season, saving arable land from drought is inevitable. Making a structured plan for water management can be one solution to mitigating large amounts of farmland disappearing. Only 12% of the land can be used for agriculture, but we need to feed 9.9 billion people 70% more food by 2050. That's enough to feed 10 billion people (we're at 7.6 billion right now). Despite this excess, hunger persisted. So we have come up with the Differential Evolution Algorithm, which predicts the future population's food needs based on past and present agricultural data. This helps to solve hunger in the near future and also helps limit drought on land. This study aims to highlight the importance of agricultural information systems for agricultural development, identify the strengths and weaknesses of current systems, and provide recommendations for improving their performance. We will return to the results of previous studies on this issue. Finally, general conclusions about farm information systems are highlighted, suggesting implications for better farm information systems..

Server Room is the main part when it comes to any organization since any malicious activity in the server room may bring down the whole server room bringing the work of organization to halt. Hence, we need a server room monitoring system which works on real time images and monitors using compressed circuit television (CCTV). The framework used for server room monitoring is YOLO (You Only look Once). YOLO employs Convolutional neural networks (CNN) for image processing. In CNN image must pass through different layers like Convolutional layer, pooling layer, ReLu layer and fully connected layer.

The growth in Artificial Intelligence (AI), Big-data,and Internet-of-Things (IOT) technologies has increased chabot’s application in many areas. Some of the applications of chatbot can be seen in areas such as social media, e-commerce, healthcare, stock market, education, banking sector etc. Most of the high-end chatbots are deployed inside e-commerce, banking and health websites. There is a need to deploy the chatbots in educational website to improve interactivity of the educational platforms. The main target users of this website is rural students. In rural areas, probability of students dropping school after some age is common because, there won’t be proper monitoring of students and also sometimes facilities will be less. With e- learning, anyone can learn everything with limited cost. The key insight of developing this e-learning website is to provide a chatbot which can motivate rural students towards education. Thus a single platform where users can learn different courses, take quizzes, and chat with the bot is developed. It also provides an additional facility of tracking the scores of the quizzes and giving personalized recommendation systems to improve the scores. The chatbot will also help users to find details aboutfaculties and help users to set an appointment with distant faculties in online mode for doubts clarification. Flask micro- framework is used for developing the website. Firebase is used to store the data. RASA framework is used in developing the chatbot. Finally a content based filtering is used to givepersonalized recommendation systems.

The popularity of deep features based image retrieval and classification task has grown a lot in the recent years. Feature representation based on Convolutional Neural Networks (CNNs) found to be very effective in terms of accuracy by various researchers in the field of visual content based image retrieval. The features which are neutral to their domain knowledge with automatic learning capability from their images are in demand in various image applications. For improving accuracy and expressive power, pre-trained CNN models with the use of transfer learning can be utilized by training them on huge volume of datasets. In this paper, a hybrid model for image retrieval is being proposed by using pre-trained values of hyper parameters as input learning parameters. The performance of the model is being compared with existing pre-trained models showing higher performance on precision and recall parameters

E-learning has changed how students grow over the past ten years by allowing them access to high-quality education whenever and wherever they need it. In any case, understudies frequently get occupied in light of different reasons, which influence the learning ability by and large. Numerous experts have been striving to address the nature of online education, but we really need a comprehensive solution to this problem. This essay aims to present a method for monitoring students' continuing attention during online classes using the surveillance camera and oral input. We investigate different picture handling strategies and AI calculations all through this review. We suggest a framework that makes use of five specific non-verbal cues to calculate an understudy's consideration score during computer-based tasks and generate continual feedback for both the association and the understudy. The output can be used as a heuristic to investigate both the speakers' and understudy' general methods of exhibiting themselves

Predicting students’ overall performance turns into greater difficult because of the massive quantity of records in academic databases. Currently in India, the shortage of current system to examine and display the student development and overall performance isn't always being addressed. Hence on this paper, supplied an in-depth literature assessment on predicting student overall performance through the use of data mining strategies is proposed to enhance students’ achievements. The main goal of this paper is to offer an outline at the data mining strategies which have been used to predict students’ overall performance. We may want to really enhance students’ achievement and success greater efficaciously in an efficient manner the use of academic records mining strategies. It could convey the benefits and affects to students, educators and educational institutions.

With the growing demand of Social media and its various networks and with the emergence of virtual nature of Online Shopping users are not easily able to determine whether the quality of the product or service is good or worse. For this work there is no such specific model existed that can find similar or dissimilar products from the people posting tweets in the form of post, messages or hashtags using similar sort of textual content. Also the companies are giving their Product reviews, which contain the direct / Indirect feedback from the customers. The companies are also getting feedback from the social media in the form of tweets / post / blogs from the twitter in the form of text for the same product. So, to find the sentiments of an individual based on the product recommendations the work had been proposed. The reviews from various intelligent algorithm for sentimental classification based on Adaptive Neuro-Fuzzy Inference System were studied. The research work is based on the comparison of the other traditional based Sentiment Analysis methodology with the ANFIS. Therefore, we are able to identify the best method to be used for the reviews identification in the best possible method.

Nowadays food is one of the major concerns of people. During this COVID-19 a pandemic situation, everyone wants to take the right food with its nutritional values. “Superfoods” are the expanded availability of products which has been a major demand for marketing. “Superfoods” has tremendously and recently gained attention along with the digital marketing strategy to motivate the super health benefits for the human body and also to prevent diseases and to provide the health benefits. Fruits and vegetables are the main sources of it. In many situations these foods play a very important role in decreasing the risk of critical conditions and diseases. Superfood represents a natural source of vitamins, minerals, and other nutrients required for a healthy lifestyle. These superfoods provided focusses on hygiene and balanced nutrition with the objective for promoting the health of the people in a better way [9]. This article explores how “Superfoods” awareness and its requirements can be known to common people using the blog. Therefore, we have created a blog on superfood and made a survey of it to know about the people's views.

Digital technology and Internet of things is acting as turning force on aviation industry. It is also noted that airport IoT solutions are leading in efficient airport operations and smooth passenger flow. The proposed architecture is to gear smart passenger processing through sensor, connected devices, artificial intelligence and analytical decision making. It also focuses on great potential of IoT integration in end to end passenger processing at airport terminal. This proposition helps in ‘always stays a connected passenger ‘concept. The proposed concept gives new dimension to check-in to boarding in smart way. The use of IoT will help to enhance security efficiency, shorter and better traveller experience with selecting capabilities in IoT. The Internet of Things (IoT), a network of connected smart devices through sensors which are used to communicate and collect the smart data which are analysed with augmented intelligence, such analysed data used to perform act across the applied area.

The process of lifting a ship’s block is one of the important process in shipbuilding. The purpose of lifting or transfering of the blocks is to relocate the block to a new position before joining with other blocks. Prior to the lifting operation of ship’s sub-block, some stress and deflection analysis shall be done, in order to assess the magnitude of the deformation and the stresses acting on the structural members of the block. Based on the assessment, some changes in lifting operation can be intiated for example changing the lifting eye pad location to minimize plate deformation and bucking of structural members. In this study, the structural behaviour of a block was investigated using basic finite element method in order to simulate the deflections and stresses during the lifting operation. The initial dimension of the sub-block that was being investigated are with its dimensions: 21 meters in length, 16 meters in width, 4 meters in height. The type of material of all the structural components used in the sub-block is High Tensile Steel DH36. The investigation used Multiframe software. There are four case studies conducted with varying padeye location and number of lifting eye pads used in a single lifting. The sub block is approximately at 160 tonnes. These results were assessed with the DNV-GL acceptance criteria which can be found in DNV-GL Rules for finite element analysis. The highest yield ulitilization factor was found in case study 3 at 0.864, where it did not comply with the permissible coarse mesh yield utilization factor which is limited to 0.80. The lowest yield utilization factor was found in case study 2 at 0.278. Using the DNV-GL acceptance criteria it is concluded that case study 2 is the best configuration in lifting the sub-block.

Crime detection, investigation and prosecution are usually carried out by the various law enforcement agencies saddled with such responsibilities. In this study, an integrated web based unified system was developed and implemented for the five (5) agencies (Nigerian Police Force, National Drug Law Enforcement Agency, Economic Financial Crime Commission, Independent Corrupt Practices Commission, Department of State Services) to enhance domiciliation of crime data into one system for effective information sharing among the five agencies. The methodology adopted for the system design is Object Oriented Analysis and Design Methodology (OOADM) and the tools used are HTML, CSS, JavaScript, MySQL. The result obtained shows that with the integration of the five agencies, accurate records of suspects and victims were timely shared by the various agencies. There is also an effective collaboration among the various agencies in crime detection, investigation and prosecution of suspects.

In this research paper, the simulation based modelling of heat transfer in tissue under periodic boundary condition has been considered. The dual phase lag bioheat transfer (DPLBHT) model is implemented for computation of the temperature based thermal therapy treatment. The elements of volumetric heat source such as perfusion of blood, metabolism heat source and external heat source are considered in nonlinear DPL model. In this paper we have taken three cases for metabolic heat source namely, constant, linear and exponential. The combined two numerical methods which are based on finite difference scheme and Runge Kutta (4,5) scheme are exerted to solve the non-linear problem. We compute the exact solution for particular case. Numerical conclusions which are computed by numerical hybrid method are equated with exact result. It has been found that non linear DPL model with exponential metabolic heat source is closed to exact solution. We also expressed the effect of different parameters such as relaxation time, perfusion rate, metabolic heat source parameter, associated blood perfusion heat, heat source because of heat flux and temperature gradient etc.

In all technologies, including traditional computing and cloud computing, security has always been the primary concern. In recent years, cloud computing has become widely accepted on a global scale. Cyber attacks aimed at it have increased along with its widespread acceptance. Although ample research is done in the security domain and cloud computing is based on rigid security fundamentals, the advancing network security attacks create the need for an advanced security mechanism. Also, the multiclass classification strategy has received very little attention, and classification accuracy can yet be improved. Hence, this work proposes an Ensemble Filter-based Intrusion Detection System (EFIDS) to address the limitations of previous research work. It not only identifies malicious traffic but also categorizes the attempted attacks (multiclass classification). The famous intrusion detection benchmark dataset, NSL KDD, is used to evaluate the model. Using the model, it was possible to enhance the classification accuracy of both binary and multiclass approaches up to 99.85 percent and 99.63 percent, respectively. Additionally, both forms of classification have shown a 65–70% improvement in training time.

The network of devices known as the Internet of Things (IoT) consists of hardware with sensors and software. These devices communicate and exchange data through the internet. IoT device-based data exchanges are often processed at cloud servers. Since the number of edge devices and quantity of data exchanged is increasing, massive latency-related concerns are observed. The answer to these issues is fog computing technology. Fog computing layer is introduced between the edge devices and cloud servers. Edge devices can conveniently access data from the fog servers. Security of fog layer devices is a major concern. As it provides easy access to different resources, it is more vulnerable to different attacks. In this paper, a deep learning-based intrusion detection approach called Multi-LSTM Aggregate Classifier is proposed to identify malicious traffic for the fog-based IoT network. The MLAC approach contains a set of long short-term memory (LSTM) modules. The final outcomes of these modules are aggregated using a Random Forest to produce the final outcome. Network intrusion dataset UNSW-NB15 is used to evaluate performance of the MLAC technique. For binary classification accuracy of 89.40% has been achieved using the proposed deep learning-based MLAC model.

Nowadays food is one of the major concerns of people. During this COVID-19 a pandemic situation, everyone wants to take the right food with its nutritional values. “Superfoods” are the expanded availability of products which has been a major demand for marketing. “Superfoods” has tremendously and recently gained attention along with the digital marketing strategy to motivate the super health benefits for the human body and also to prevent diseases and to provide the health benefits. Fruits and vegetables are the main sources of it. In many situations these foods play a very important role in decreasing the risk of critical conditions and diseases. Superfood represents a natural source of vitamins, minerals, and other nutrients required for a healthy lifestyle. These superfoods provided focusses on hygiene and balanced nutrition with the objective for promoting the health of the people in a better way [9]. This article explores how “Superfoods” awareness and its requirements can be known to common people using the blog. Therefore, we have created a blog on superfood and made a survey of it to know about the people's views.

The use of electronic devices and the digital globalization has brought a lot of technical revolution in the world. People use many devices and several applications available on the cloud. This leads to generation of ample amounts of data which is termed as huge data, technically a big data. This big data is heterogeneous in nature which is a combination of structured and unstructured data. This review paper aims to show the general facts of big data and then narrowing it down we are showing types of heterogeneous big data. This paper will help the novel researchers to gain facts about big data and its heterogeneity.

Stress plays an important role in late-life depression. It is a natural reaction to various factors which can lead to physiological and behavioural changes in human behaviour and performance. Dependence, ill health, loss of social role and recognition, and a lack of opportunity for creative use of leisure are some of the most common medical and psychological difficulties among the elderly. Automatic stress monitoring reduces the likelihood of health problems and promotes society's well-being. Our project's major goal is to use machine learning and image processing techniques to detect stress in the elderly population. Our system is an improved version of previous stress detection systems that did not include live detection, personal counselling, or stress level notification via a mobile device application. Instead, this system includes live detection and periodic analysis of the person in question, as well as detecting mental stress levels and notifying the person's emergency contact. Our system is primarily targeted at the elderly, but it may also be used by students and IT professionals to manage stress and create a healthy, spontaneous work environment for employees, allowing them to give their best during working hours.

A surprising mass of tissue wherein some cells duplicates and develops wildly is calledcerebrum growth. It should be identified at a beginning phase utilizing X-ray or CT checked pictures when it is just about as little as conceivable in light of the fact that the growth might perhaps result to malignant growth. This paper, for the most part centers around identifying and limiting the growth locale existing in the cerebrum by proposed philosophy utilizing patient’s X-ray pictures. The proposed procedure comprises of different stages which assume an alternate parts to deliver the examination with at most precision. division is applied to obviously show the growth impacted locale in the X-ray pictures. There are two main objectives of our project: 1) To analyze brain tumor 2) If there is, specify the location of the tumor.

Heart is one most important organ in our body. The prediction of heart disease is most complicated task in today world. There are number of instruments available in today’s worlds. These instruments are so expensive some of them can afford that instrumentals some of them cannot afford the instruments. Early prediction of heart disease will reduce the death rate. we can tell the patients before the hand. In todays world we all have the good amount of data using that good amount of data we can predict the heart disease using various machine learning techniques. The proposed method will tell to patients probabilities of heart diseases. In this paper using the UCI dataset performed various machine learning techniques like Logistic Regression, Decision tree, KNN, Naïve Bayes, Random Forest, XGBoost, Support vector machine . In this paper we used proposed methodology from PHASE I to PHASE VII Using the evaluation metrics we can check the performance of the machine learning which gives more accuracy from the above seven machine learning algorithm.