This paper proposes a project that involves an automated solar tracking system which will make use of LDR’s to track the position of sun. The output of LDR’s will be compared and analyzed to provide correct alignment of the solar panel. Also another tracking technique is being implemented along, which uses the relation of sun earth position at a given location. This telemetric data is given to microcontroller which will drive the motors to align the solar panel. This is useful during cloudy weather and rainy days when it is difficult to check the position of sun. Solar panels give output efficiency of around 15% to 20% based on the type of panel. The use of solar tracking system increases it to a range of about 30% to 35%. This project further involves use of reflective sheets on the sides of solar panel which will concentrate the reflected rays on the panel. Due to this the efficiency is further increased around 40%. This project is a cost effective solution for stationary solar systems to increase efficiency.

Charging has always been an issue in electrical vehicles. In this project, the kinetic energy is transmitted in the brakes through drive train and is directed by a mechanical system to the potential store during deceleration. That energy is held until required to the vehicle, wherein it is transformed back into energy and stored in the battery of the vehicle. The amount of the power available for conservation varies depending on the type of storage, drivetrain efficiency, and drive cycle and inertia weight. When a normal vehicle applies its brake, its kinetic energy is transformed to heat because of friction between wheels and brake pad. This heat passes through the air and the energy is wasted. The total energy lost in this way depends on how often, long and hard the brake is being applied. An energy conversion action in which a part of the energy of the vehicle is stored by a battery or storage device is known as regenerative braking. Driving within a city involves more braking representing a high loss of energy with the opportunity for savings in energy. In the case of public transport vehicles such as local trains, buses, taxis, delivery vehicles there is even more potential for energy to be regenerated

The interaction of Ni-doped graphene sheet with CO molecule is investigated using density functional theory simulation to analyze the reactivity of doped graphene towards CO molecule. The adsorption energy is calculated for energetically favorable adsorption of CO on doped graphene sheet. Our result indicates that the structural properties of NI-doped graphene sheet are influenced by the adsorption of CO. The electronic band structure result for CO adsorbed on the doped graphene sheet shows the significant changes in the electronic properties of Ni-doped graphene.

Writing long sentences is bit boring, but with text prediction in the keyboard technology has made this simple. Learning technology behind the keyboard is developing fast and has become more accurate. Learning technologies such as machine learning, deep learning here play an important role in predicting the text. Current trending techniques in deep learning has opened door for data analysis. Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis. Many techniques have been used for text sequence prediction such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Recurrent Convolution Neural Networks (RCNN). This paper aims to provide a comparative study of different techniques used for text prediction.

There is an exponential increase in the use of conversational bots. Conversational bots can be described as a platform that can chat with people using artificial intelligence. The recent advancement has made A.I capable of learning from data and produce an output. This learning of data can be performed by using various machine learning algorithm. Machine learning techniques involves construction of algorithms that can learn for data and can predict the outcome. This paper reviews the efficiency of different machine learning algorithm that are used in conversational bot.

Bankruptcy is a legal status of a person or other organization that cannot repay their debts to creditors. Bankruptcy prediction is the task of predicting bankruptcy and by doing various surveys we can avoid financial distress of firms. It is a huge area of accounting and finance research. The significance of this area is an important part of financial specialists and creditors in assessing the probability that a firm may go bankrupt or not. Estimating the risk of corporate bankruptcies is very important as the effect of bankruptcy is on a global level. The aim of predicting financial distress is to develop a predictive model that combines various economic factors which allow foreseeing the financial status of a firm. In this domain, various methods were proposed that were based on neural networks, Support Vector Machines, Decision Trees, Random Forests, Naïve Bayes, Balanced Bagging and Logistic Regression. In this paper, we document our observations as we explore and build a Restricted Boltzmann Machine to Bankruptcy Prediction. We started by carrying out data pre-processing where we impute the missing data values using Mean Imputation. To solve the data imbalance issue, we apply the Synthetic Minority Oversampling Technique (SMOTE) to oversample the minority class labels. Finally, we analyze and evaluate the performance of the model.

Deep Learning has achieved significant improvement in various machine learning tasks. Nowadays, Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM) have been increasing its popularity on Text Sequence i.e. word prediction. The ability to abstract information from image or text is being widely adopted by organizations around the world. A basic task in deep learning is classification be it image or text. Current trending techniques such as RNN, CNN has proven that such techniques open the door for data analysis. Emerging technologies such has Region CNN, Recurrent CNN have been under consideration for the analysis. Recurrent CNN is being under development with the current world. The proposed system uses Recurrent Neural Network for review prediction. Also LSTM is used along with RNN so as to predict long sentences. This system focuses on context based review prediction and will provide full length sentence. This will help to write a proper reviews by understanding the context of user.

In current academic environment, assignments and home works are very necessary so that students can increase their final grades. This assignments and home works are checked manually by teachers, due to this it consumes lots of time and efforts. Due to manual checking sometimes human error may occur which may affect to student’s grades. Students may misplace their hard copies of assignments because of this they have to rewrite it again. In order to overcome these problems, the proposed system will convert the manual work to digital, in which student will submit their assignments to the system and the system will generate and assign appropriate grades. In the proposed system, by using K Nearest Neighbor Algorithm, it will collect keywords check for the similarity and will generate similarity score. It will also check the relation of the keyword with respect to sentence. To comparing the keywords with synonyms and similar meaning words Semantic Similarity Measure algorithm will be used. After getting the similarity score the grades are assigned accordingly.

The waste management system in the country is not proper, in order to provide a proper waste management system we need to manufacture a machine that can sort the various waste constituents from garbage which can be further processed and recycled to reduce the overall waste. So to achieve this, we are manufacturing a waste segregator machine which is capable of separating wastes such as metals, non-metals, plastic, etc. The design is the basic step for manufacturing of the machine if the design is proper and safe the manufacturing goes smooth. We are manufacturing the machine to obtain the goal of separating the waste which can be used by small scale as well as large scale industry effectively, which would directly add into benefit for the society. The manufacturing the machine which would be easy to operate and simple in construction. In short it would be affordable if produced in numbers.

As education is growing day by day, the competition has prompted a need for the student to understand more about the educational field. Many times the counselor isn’t available all the time and sometimes due to the lack of proper knowledge about some educational field. Due to this, it creates an issue of misconception of that field. This creates a problem for the student to decide a proper educational trajectory and guidance is not always useful. The proposed paper will overcome all these problem using machine learning algorithm. Various algorithms are being considered and amongst them the best suitable for our project are used here. There are 3 major problems that come across our path and they are solved using Random forest, Linear regression and Searching algorithm using Google API. At first Searching algorithm solves the problem of location by segregating the college’s location vice, then Random Forest provides the list of colleges by using stream and range of percentage and finally Linear Regression predicts the current cutoff using previous years’ data. Rather than this, the proposed system also provides information regarding all fields of education helping students to understand and know about their field of interest better. The following idea is a total fresh idea with no existing projects of similar kind. This project will help students guide them throughout.

Global Warming has an enormous impact on melting glaciers and ice sheets. Rising global temperatures melt glaciers increasing the amount of seawater. A large in rise sea level across the world poses many threats. With continuous increase of rise in water level, the area occupied by land decreases. This paper represents the study concerning floating construction to counter the ill effects of global warming in terms of utilisation of offshore renewable energy resources and improving an awareness to construct them.

Various applications of image processing has given it a wider scope when it comes to data analysis. Various Machine Learning Algorithms provide a powerful environment for training modules effectively to identify various entities of images and segment the same accordingly. Rather one can observe that though the image classifiers like the Support Vector Machines (SVM) or Random Forest Algorithms do justice to the task, deep learning algorithms like the Artificial Neural Networks (ANN) and its subordinates, the very well-known and extremely powerful Algorithm Convolution Neural Networks (CNN) can provide a new dimension to the image processing domain. It has way higher accuracy and computational power for classifying images further and segregating their various entities as individual components of the image working region. Major focus will be on the Region Convolution Neural Networks (R-CNN) algorithm and how well it provides the pixel-level segmentation further using its better successors like the Fast-Faster and Mask R-CNN versions.