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Road boundary lanes are one of the serious causes of road accidents and it affects the driver and people’s safety. Detecting road boundary lanes is a challenging task for both computer vision and machine learning approaches. In recent years many machine learning algorithms have been deploying but they failed to produce high efficiency and accuracy. This paper presents a novel approach to alert the driver when the car leaps beyond the Road boundary lanes by employing machine learning techniques to avoid road mishaps and ensuring driving safety. Performance is assessed through the generation of experimental results on the dataset. When compared with state-of-the-art lane detection techniques, the proposed technique produced high precision and high efficiency.

This research aims to identify rice diseases, namely Leaf blast, Brown spot, Healthy and Hispa. The purpose of this research is to utilize deep convolutional neural network (DCNN) with support vector machine (SVM), DCNN with artificial neural network (ANN) and DCNN with long short-term memory (LSTM). To enhance the performance of LSTM further, the research includes particle swarm optimization, artificial fish swarm optimization (AFSO) and efficient artificial fish swarm optimization (EAFSO) to identify optimal weights. This research also compares the proposed technique results with a conventional feature extraction approaches like texture, discrete wavelet transforms and color histogram with SVM, ANN and LSTM. The results exhibit the superiority of proposed DCNN-LSTM (EAFSO) technique over other techniques. The proposed technique EAFSO associates DCNN-LSTM identifies the rice diseases with 97.5% accuracy, which is better than DCNN-SVM and DCNN-ANN.

Growth in consumption of Oryza sativa (rice) has led the farmers across Asian countries to cultivate Oryza sativa, with an impact of 2.5 percent increase in the cultivation of the crop every year. Along with the growth in Oryza sativa cultivation, there are new challenges that are faced by the farmers in terms of diseases. The absence of information to recognize what sort of infection the plant is influenced with during the harvest cycle drives the farmers over the globe to lose 37 percent of the production. Involving technology to identify these diseases during the harvest cycle will help the farmers to get benefitted by attaining better yields. Deep learning being a latest technology playing a vital role in helping human in many aspects. A thorough review of the research papers on the various classifiers used in the identification of Oryza sativa diseases was carried out and the survey was tabulated and presented.

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. The oil obtained from the groundnut is widely used for cooking and losing body weight, and its fats are widely used for making soaps. The groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of deep convolutional neural network (DCNN) because it automatically detects the important features without any human supervision. The DCNN procedure can deeply detect plant disease by using a deep learning process. Moreover, the DCNN training and testing process demonstrate an accurate groundnut disease determination and classification result. The number of groundnut leaf disease images is chosen from the plant village dataset, and it is used for the training and testing process. The stochastic gradient decent momentum method is used for dataset training, and it has shown the better performance of proposed DCNN. From the comparison analysis, the 6th combined layer of proposed DCNN delivers a 95.28% accuracy value. Ultimately, the groundnut disease classification with its overall performance of proposed DCNN provides 99.88% accuracy.

Cancer is one of the most dangerous disease in human life. Diagnosing the cancer cell in early stages plays an important (valuable) role in saving human life and for successful treatment. At an early stage the spreading of cancer cells of the body can be stopped by removing the benign cells (cancer cells in the early stage). Whereas, malignant tumor (cancer cells in later stages) which are having aggressive spreading capacity, affects different other parts of the body and cannot be controlled. This paper presents a study on five different types of cancer viz., breast, brain, lung, liver and skin; and different published techniques of detecting these cancers, which help the students (researcher) to understand the current ongoing techniques and aids to develop new structure that gives better and accurate result. It also focuses on different segmentation (ACM, PSO, UNet, watershed etc), cancer feature extraction, cancer features reduction (PCA, LDA, SVD). Also, it discusses different cancer classification using machine learning and clustering (SVM, KNN, Bayesian, Neuro fuzzy, k-mean algorithm, GANs etc.), deep learning (CNN, ResNet, VGG etc) technique, also discuss about different evaluating method.

The computer and the digital camera have given proven opportunities to improve the hematology research and education with patient service. Peripheral Blood Smear (PBS) images of high quality can be obtained quickly and smoothly from the Peripheral Blood Smear with the help of a modern, high resolution digital camera and a high quality microscope. A PBS or blood film is a thin layer of blood coated on a microscope slide. PBS are usually examined to analyze the blood related problems and occasionally, to find parasites within the blood. PBS image examination is a part of the daily work of every testing laboratory. The manual examination of these images is difficult, takes more time and faces human intervention and observation error. This has motivated researchers to develop different algorithms and methods to automate peripheral blood smear image analysis. The interest of computer aided decision making has been identified in many medical applications such as automatic detection, classification and analysis of objects in hematological cytology. Image analysis itself consists of a sequence of steps consisting of image segmentation, features extraction and selection and pattern classification. The aim of this review article is to summarize the qualitative abnormalities of the blood cells, viz., Red Blood Cell, White blood Cell and Platelets. This aids the researcher, for ease interpretation and common diagnosis of the peripheral blood smear images. Also, this helps the researchers to propose more relevant image processing and machine learning tools for developing a complete automated PBS analysis system that can reduce the time spent for slide examination.

The deep learning-based compressive Sensing (CS) has shown substantial improved performance and in run-time reduction with signal sampling and reconstruction. In most cases, moreover, these techniques suffer from disrupting artefacts or high-frequency contents at low sampling ratios. Similarly, this occurs in the multi-resolution sampling method, which further collects more components with lower frequencies. A promising innovation combining CS with convolutionary neural network has eliminated the sparsity constraint yet recovery persists slow. We propose a Deep wavelet based compressive sensing with multi-resolution framework provides better improvement in reconstruction as well as run time. The proposed model demonstrates outstanding quality on test functions over previous approaches.

In the present day scenario, where huge volumes of data are being generated from various sources, as such storing and processing these data using traditional systems is a big challenge. The majority of data is of unstructured; hence necessary architectures should be designed to meet the continuous challenges. Among the possible solutions for the big data problem, one of the best solutions to address the huge volumes of unstructured data was Hadoop. In the medical field, huge volumes of clinical image data are resulting from the respective hardware tools. The necessary methods that are required to store, analyze, process and classification of these medical images can be done with map-reduce architecture using the Hadoop framework thereby reduces the computational time for the overall processing as the mapper will perform parallel processing. This paper includes a detailed review of Hadoop and its components. The main motive of this work is to deal with the medical image data using an efficient architecture such that automatic clustering or classification of images will be done within the architecture itself. The clustering of these medical images for future predictions and diagnosis for the disease is essential. In the map-reduce architecture, along with the map and reduce phases, the usage of combiners and partitioners will improve the efficiency of medical image processing for clustering the image data. The other responsibilities of this paper are to review the recent works in the image data clustering along with the state of art techniques for image classification. The clustered medical images will be used for automatic predictions and diagnosis of various patient diseases by applying Convolution Neural Network (CNN) techniques on top of the clustered or classified images.

The proposed work solely focuses on transforming the manual task of pathologists in classifying a test mutation to a task automatically done by a machine. We collected the dataset from a Kaggle competition which distributes the three features, Gene, Variation and Text into nine different classes. These classes are provided by genomic researchers which state whether a particular mutation is a driver (cancer causing mutation) or a passenger (neutral mutation). Our model was able to perform the labour intensive work of classification, thus saving time, diminishing possibility of human error and risks of wrong analysis. As the score was not too high, we have given a probabilistic output and hence our model is interpretable. Researchers need to analyse only around two classes with highest probability for classification ignoring all other classes. We have used different encoding and embedding techniques to convert text entry to numerical form, after which they are fed as input to different classifiers. Logistic Regression classifier with Term Frequency—Inverse Document Frequency encoding technique fetched the maximum accuracy, i.e. 67.27%. Attention had been given to enhance this accuracy with Word2Vec and Doc2Vec embedding, but it only decreased due to some issues with our dataset as discussed later. However, with a better dataset, our model can ensure better accuracy.

Data sampling is critical process for energy constrained Wireless Sensor Networks. In this article, we proposed a Predictive Data Recovery Compressive Sensing (PDR-CS) procedure for data sampling. PDR-CS samples data measurements from the monitoring field on the basis of spatial and temporal correlation and sparse measurements recovered at the Sink. Our proposed algorithm, PDR-CS extends the iterative re-weighted $-\ell_{1}(IRW-\ell_{1})$ minimization and regularization on the top of Spatio-temporal compressibility for enhancing accuracy of signal recovery and reducing the energy consumption. The simulation study shows that from the less number of samples are enough to recover the signal. And also compared with the other compressive sensing procedures, PDR-CS works with less time.

We are well aware of the importance of traffic signs and road rules in our day-to-day lives. Ignorance of traffic signs and rules may lead to various road mishaps. Technology has been trying to develop “smarter cars” that are able to automatically recognize the traffic signs and understand roadways leading to a safe drive. Within these smart cars “driver alert” system is an important feature for knowing the roadway (especially the traffic signals) to alert drivers. Recognizing the traffic signs at night is still a challenging task for researchers. This can be tried to overcome by various Deep Learning models by employing Computer Vision. In this work, the data set is trained with a Convolutional Neural Network and analyzed using the Deep Learning technique on the Keras framework. Our proposed model achieved a state-of-the-art performance of 99.66% and 96.86% accuracy on the training and testing datasets respectively ...

With the advancement in digital video technology, video surveillance has been playing its vital role for ensuring safety and security. The surveillance systems are deployed in wide range of applications to invigilate stuffs and to analyse the activities in the environment. From the single or multi surveillance camera, a huge amount of data is generated, stored and processed for security purpose. Due to time constraints, it is a very tedious process for an analyst to go through the full content. This limitation has been overcome by the use of video summarization. The video summarization is intended to afford comprehensible analysis of video by removing duplications and extracting key frames from the video. To make an easily interpreted outline, the various available video summarization methods will try to shot the summary of the main occurrences, scenes, or objects in a frame. Depending on the applications, it is required to summarize the happenings in the scene and detect the objects (static/dynamic) which is recorded in the video. Hence this paper provides the various methods used for video summarization and a comparative study of different techniques. It also presents different object detection, object classification and object tracking algorithms available in the literature.

This paper investigates the flexural behavior of high-performance concrete beams strengthened with polyolefin fibers. In the recent development in concrete technology, high-performance concrete is designed to give materials, exposure conditions, and applicable requirements of cost, better service life, and durability. The replacement of cement by adding the mineral admixture and chemical admixture to the concrete increases its strength and durability properties. To improve the strength and ductility of High-performance concrete, fibers are added to the concrete. This study focused on the effect of polyolefin fibers on the mechanical and flexural performance of high-performance concrete. Two types of polyolefin fibers at various percentages of 0.1%, 0.2%, 0.3% volume fraction of high-performance concrete. The flexural beams were cast and tested under two-point loading. The addition of fibers increases the first crack load, ultimate load capacity, and ductility. Polyolefin micro and macro fiber performance reinforced high-performance concrete beams compared with experimental and analytical investigation carried out using ANSYS software.

Reinforced Concrete (RC) beams are essential structural elements that transfer loads from the slabs to the columns through flexure and shear. Openings in Reinforced Concrete (RC) beams are required for a variety of architectural and mechanical reasons. The purpose of this article is to investigate the flexural behaviour of an RC beam with circular openings running the length of it. The three-dimensional nonlinear finite element method was used to investigate a supported RC beam with circular web openings of varying diameters using ANSYS, a finite element software package. The study’s primary characteristic is the variation in diameter and location of reinforcement around the openings. Five RC beams with simple supports were constructed, and tested were conducted under two-point loads. Beams were constructed with one conventional specimen without any openings in beams and two beams with unstrengthen circular openings located in the flexure zone with varying diameters such as 80mm and 100mm considered as a control beam. In contrast, the remaining two beams provided effective diagonal reinforcement around the openings. The load-bearing capacity and load-deflection behaviour of such openings in beams were studied and analysed in detail. To increase the ultimate strength of the beam, effective diagonal reinforcement and short stirrups at the top and bottom of openings are used to prevent the beam from collapsing prematurely.

This paper provides the analytical and experimental work of reinforced concrete beams strengthened with FRP. The main objective of this investigation is to study the flexural behaviour of reinforced concrete beams of different methods of strengthening methods using GFRP to find the flexural strength, failure modes, and ductility of the reinforced concrete beam. All beams were strengthened for flexure with external bonding to prevent flexural failure. The analytical and experimental results indicated that the externally bonded GFRP used for flexural strengthening of reinforced concrete beams increased the cracking load, increased the ultimate load-carrying capacity, and exhibited decreased ductility corresponding to the unstrengthen control specimen. The analytical work was carried out using ANSYS 17.0 Software and found the parameters of total deformation, stress strain curves, and Load deflection graph plotted.

This article discusses the behavior of a reinforced concrete deep beam that has been strengthened with GFRP sheets. The GFRP shows many essential and necessary properties such as low weight to strength ratio, non-corrosive, high fatigue strength, high tensile strength, etc. In this paper, four deep beams were cast, in which one beam is a control beam, and another one is with special confinement reinforcement. The rest of the two beams are strengthened with the GFRP sheets in standard control beam and special confinement reinforcement beam under static or monotonic loading under three points, and the corresponding behavior is determined. The behavior of the RC Deep beams, such as load-carrying capacity, first crack load, load-deflection behavior, crack patterns, and stress vs. strains, are determined. The analysis of the reinforced concrete deep beams was done using the finite element software called ANSYS. Additionally, this article provides preliminary support for proposing a novel shear strengthening technique during the member’s design. In the finite element simulations, the proposed deep beams have been modeled and analyzed using the finite element software, ANSYS, and the results are compared with experimental work.