Image captioning is the description generated from images. Generating the caption of an image is one part of computer vision or image processing from artificial intelligence (AI). Image captioning is also the bridge between the vision process and natural language process. In image captioning, there are two parts: sentence based generation and single word generation. Deep Learning has become the main driver of many new applications and is also much more accessible in terms of the learning curve. Image captioning by applying deep learning model can enhance the description accuracy. Attention mechanisms are the upward trend in the model of deep learning for image caption generation. This paper proposes the comparative study for attention-based deep learning model for image captioning. This presents the basic analyzing techniques for performance, advantages, and weakness. This also discusses the datasets for image captioning and the evaluation metrics to test the accuracy. 

The introduction of remote sensing techniques had lead  us into a new race of advanced data processing applications. The analysis ready data is also a part of it which is generated at the producer end to facilitate its user to directly go on to the application part. This paper highlights the generation, processing and cloud applications of the Analysis Ready Data (ARD) using ISRO's Satellites Resourcesat-2 and Resourcesat-2A's LISS-3 sensor data. The proposed work includes use of terrain corrected data for generating Radiance, Top of Atmosphere (ToA) Reflectance, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Time series analysis with pixel level Quality Assessment (QA) for all the generated data products. A graphical user interface has been developed for online ordering of data by the user. This paper also highlights the implementation of the developed application in cloud platform  using the cloud computing model,  Platform as a Service (PaaS).This facilitates the users to generate the ARD products from any device, facilitating a quick and all time available transmission rate for the customers.

The wastage of electrical power cannot be over-emphasized in FCT College of Education, Zuba-Abuja as many lighting bulbs, street-lights, electrical fans are always ON even when not in use. The college community is characterized by electrical power wastage. However, the motivation for this research was to curtail the electrical power wastage and reduce the high cost of electricity. This research was designed to control ON and OFF time of any electrical appliance connected to its output and could as well be used as a digital clock. The objective of this research was to control the ON and OFF time of air conditioning. The design included a microcontroller (ATMEGA328) that was programmed to achieve the timing operation. The Light Emitting Diode (LED) displayed the ton (Time ON) and the t0ff (Time OFF); four keys set the hour and the minutes; and the relay was activated whenever the time set elapsed, causing the air conditioning to be energized/dis-energized automatically. A time of 3:19 was set to test the ON switching. An air conditioning connected to the developed system was activated at exactly 3:19. Also, a time of 5:57 was set to de-activate the already ON electric bulb. The electric bulb was switched OFF at exactly 5:57. The developed switching system was tested and satisfactorily switched ON and OFF air conditioning as desired and pre-set by the user.

Carbon Nanotube Field Effect Transistors (CNTFETs) are being proposed as candidates for next-generation integrated circuit technology replacing conventional MOSFET devices. It is a suitable nanoelectronic device which is used for high speed and low power design applications which include analog and digital circuits. In this paper, a single wall carbon nanotube field effect transistor (SW-CNTFET) with a coaxial structure in the ballistic regime has been studied and its performance parameters discussed. Numerical simulations were performed based on Natori approach. The various device metrics in consideration are drive current (Ion), Ion/Ioff ratio, output conductance (gd), trans-conductance (gm), gain, carrier injection velocity, sub-threshold swing and drain induced barrier lowering (DIBL). In particular, the influences of gate oxide thickness on the short-channel effects are presented in detail. Also, the dependence of sub-threshold swing and DIBL on the gate control parameter has been discussed.

Face recognition is one of the most commonly used biometric features in the identification of people. In this article, a novel facial image recognition architecture is proposed with a novel image descriptor which is called as fully center symmetric dual cross pattern (FCSDCP) The proposed architecture consists of preprocessing, feature extraction and classification phases. In the preprocessing phase, discrete wavelet transform (DWT) and Neutrosophy are used together to calculate coefficients of the face images. The proposed FCSDCP extracts features. LDA, QDA, SVM and KNN are utilized as classifiers. 4 datasets were chosen to obtain experiments and the results of the proposed method were compared to other state of art image descriptor based methods and the results clearly shows that the proposed method is a successful method for face classification.

The mathematical notation is well known and used throughout the world. Humanity has evolved from simple methods to represent accounts to the current formal notation capable of modeling complex problems. In addition, mathematical equations are a universal language in the scientific world, and many resources such as science and engineering technology, medical field also not an exception containing mathematics have been created during the last decades. However, to efficiently access all that information, scientific documents must be digitized or produced directly in electronic formats.
Although most people are able to understand and produce mathematical information, introducing mathematical equations into electronic devices requires learning special notations or using editors. The proposed methodology is focused on developing a method to recognize intricate handwritten mathematical equations. For pre-processing, Gray conversion and Weiner filtering are used. Segmentation is performed using the morphological operations, which increase the efficiency of the subsequent image of equation. Finally Neural Network based template matching technique is used to recognize the image of handwritten mathematical equation.