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The objective of this paper is to present an effective and reliable method for the traffic surveillance using the concepts of digital image processing. The paper proposes a system that can detect, track, and estimate velocity of vehicles using an uncalibrated camera and also detect and recognize their registration number plate. This approach provides a cost-effective alternative for traffic flow monitoring and surveillance. This robust system finds its applications in urban traffic management systems, military installation, and research facility security systems. This approach is a computationally efficient approach for detecting and tracking moving cars on the road utilizing uncalibrated cameras mounted on the road. It is also helpful for military installation because all the security issues have been detected by using this approach., Author(s): Sandeep Gupta [1]; Ranju S Kartha [2]; Edwin Ramirez-Asis [3]; Judith Flores-Albornoz [3]; Rosa Vílchez-Vásquez [4]; Sitesh Kumar Singh (corresponding author) [5] 1. Introduction Effective monitoring of traffic has [...]

Now a day's the waste is produced in immense amounts during construction and coal burning, this study mainly focused on the utilization of sandstone slurry produced during sandstone cutting and Class-F fly ash produced by coal-burning. In this study a deep analysis is taken after 28 days' testing, six concrete mixes were prepared one is conventional as M20, and trial mix batches are a total of five, three specimens were taken from each concrete mix separately to determine the engineering properties of the prepared specimen. Fly ash partly replaced with the cement in % of 5%, 10%, 15%, 20%, 25% and Sandstone slurry partially replaced with sand in % of 10%, 20%, 30%, 40%, 50%. Water cement ration took. 4-7%, after 28 days specimens were tested of water curing at ± 2, 27°C. Sulfate bath is prepared for durability test with 5% Na2SO4 and prepared specimen left for 28 days curing in prepared sulfate bath after completion of 28 days' normal water curing, the specimen was tested after 56 day curing, acidic nature was maintained for prepared bath, on daily basis pH value has been noted if found more than 6.9 pH than sulphuric acid is added to keep the acidic nature of prepared bath. Only 5% FA and 10% SSS specimen show strength increment up to 14.477% and after sulfate bath, no change was found in specimen's volume. In compressive strength maximum strength increased up to 19.39%, from 5% FA and 10% SSS replacement. In splitting tensile strength maximum strength increased 11.37% with 10% FA and 20% SSS. In Flexural strength, no increment was noticed. [ABSTRACT FROM AUTHOR]

In the new global economy, getting natural aggregates (NA) has become a central issue for constructing flexible pavements due to the scarcity of aggregates and the ban on mining in various states in India. This research is an attempt to achieve sustainability by using a liquid antistripping additive for emulsion-treated base layer to improve the performance of Reclaimed Asphalt Pavement Material (RAPM) inclusive aggregates. RAPM was evaluated, with inclusion percentages of 50 and 70 percent, whereas, the control mix was prepared using 100 percent natural aggregate (NA). The effect of inclusion of liquid antistripping additive (ASA) with different RAPM percentages on various properties of ETB mixes, such as maximum dry density, indirect tensile strength, moisture resistance and resilient modulus, was studied. Furthermore, when compared to RAP-ETB mixes without ASA, RAP-ETB mixes with ASA were found to preserve many of their qualities. The present study aimed to propose the laboratory design of optimum bitumen emulsion content (OBEC) for ETB in a simpler manner. For 50 RAP, obtained OBEC was at 4.4%, whereas for 70 RAP, OBEC was obtained at 3.8%. However, for 100 % NA, calculated OBEC was 7.0% as there was 0% RAP in it, hence binder absorption was more. The strength parameter was assessed using the Indirect Tensile Strength (ITS) test. At the same time, the pavement response was measured in terms of Resilient Modulus (MR). MR of 70 RAP mixes was higher than that of 50 RAP mixes, and 100 NA mixes with antistripping additive. Test results were encouraging, and significant improvement in strength was caused by cement filler and antistripping additive. [ABSTRACT FROM AUTHOR]

Composites with improved electrical properties created new pathways in electrical engineering industries. In this work, electrical studies conducted on continuous unidirectional E-glass fibre-reinforced epoxy composites modified with three different graphite oxide fillers are discussed. The three different fillers are (i) graphite oxide (GO), (ii) exfoliated graphite oxide (EGO), and (iii) reduced exfoliated graphite oxide (rEGO). Incorporation of GO fillers exhibited significant improvement in the dielectric characteristics of the composites, where it showed 42% enhancement in breakdown strength values. Dielectric constant measurements of GO-filled composites have also demonstrated considerable enhancement in the values where the fillers promoted interfacial and dipolar polarization phenomena in the material. On the other hand, in the case of EGO and rEGO fillers, conducting nature induced from graphitic structure had significantly reduced the dielectric properties of their composites. [ABSTRACT FROM AUTHOR]

Most of the people all over the world pass away from complications related to lung cancer every single day. It is a deadly form of the disease. To improve a person's chances of survival, an early diagnosis is a necessary prerequisite. In this regard, the existing methods of tumour detection, such as CT scans, are most commonly used to recognize infected regions. Despite this, there are certain obstacles presented by CT imaging, so this paper proposes a novel model which is a correlation-based model designed for analysis of lung cancer. When registering pictures of thoracic and abdominal organs with slider motion, the total variation regularization term may correct the border discontinuous displacement field, but it cannot maintain the local characteristics of the image and loses the registration accuracy. The thin-plate spline energy operator and the total variation operator are spatially weighted via the spatial position weight of the pixel points to construct an adaptive thin-plate spline total variation regular term for lung image CT single-mode registration and CT/PET dual-mode registration. The regular term is then combined with the CRMI similarity measure and the L-BFGS optimization approach to create a nonrigid registration procedure. The proposed method assures the smoothness of interior of the picture while ensuring the discontinuous motion of the border and has greater registration accuracy, according to the experimental findings on the DIR-Lab 4D-CT public dataset and the CT/PET clinical dataset. [ABSTRACT FROM AUTHOR]

An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model's ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and F 1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68-A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68-A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a new approach that provides exceptional speed in healthcare activities with greater accuracy. In this regard, "convolutional neural network" or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. This research is going to analyze the extension of blockchain with the help of CNN for lung cancer prediction and making food safer. CNN algorithm has been trained with a huge number of images by altering the filters, features, epoch values, padding value, kernel size, and resolution. Subsequently, the CNN accuracy has been measured to understand how these factors affect the accuracy. A linear regression analysis has been carried out in IBM SPSS where the independent variables selected are image dataset augmentation, epochs, features, pixel size (90 × 90 to 512 × 512), kernel size (0–7), filters (10–40), and padding. The dependent variable is the accuracy of CNN. Findings suggested that a larger number of epochs improve the CNN accuracy; however, when more than 12 epochs are considered, the accuracy may decrease. A greater pixel/resolution also improves the accuracy of cancer and food image detection. When images are provided with excellent features and filters, the CNN accuracy improves. The main objective of this research is to comprehend how the independent variables affect the accuracy (dependent), but the reading may not be fully exact, and thus, the researcher has conceded out a minor task, which delivered evidence supportive of the analysis and against the analysis. As a result, it can be determined that image augmentation and a large number of images develop the CNN accuracy in lung cancer prediction and food safety determination when features and filters are applied correctly. A total of 10–12 epochs are desirable for CNN to receive 99% accuracy with 1 padding. [ABSTRACT FROM AUTHOR]

In the present scheme of the world, the problem of shortage of power is seen across the world which can be a vulnerability to various communication securities. The scope of proposed research is that it is a step towards completing green communication technology concepts. In order to improve energy efficiency in communication networks, we designed UART using different nanometers of FPGA, which consumes the least amount of energy. This shortage is happening because of expanding of industries across the world and the rapid growth of the population. Therefore, to save the power for our upcoming generation, the globe is moving towards the concept and ideas of green communication and power-/energy-efficient gadget. In this work, a power-efficient universal asynchronous receiver transmitter (UART) is implemented on 28 nm Artix-7 field-programmable gate array (FPGA). The objective of this work is to reduce the power utilization of UART with the FPGA device in industries. To do this, the same authors have used voltage scaling techniques and compared the results with the existing FPGA works. [ABSTRACT FROM AUTHOR]

Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making. [ABSTRACT FROM AUTHOR]

A widely used genetic algorithm (GA) is endorsed to improve the design of a multilayer microwave radar absorbing material (MMRAM) which shows good absorption of radar waves over a broad frequency range. In this research, the authors have used genetic algorithm based on MMRAM which plays an important role in defense and civil applications. The scope of multilayer microwave radar absorbing material (MMRAM) is that it can absorb radar signals and reduce or eliminate their reflection. Its primary use is in defense and certain commercial enterprises. The multilayer RAM design demands the superiority of suitable materials to be used in different layers, a decision about multiple layers, and the optimum breadth of an individual layer. The permeability and permittivity of the materials varying with frequency in a fictitious material are used. The effect of change in thickness and the number of layers of RAM on reflectivity is studied. Since the material characteristics are frequency-dependent, different restrained conditions are used for frequency bands to identify the RAM that has good electromagnetic absorption in the frequency range of 1 to 8 GHz. [ABSTRACT FROM AUTHOR]

Machine learning has become an essential tool in daily life, or we can say it is a powerful tool in the majority of areas that we wish to optimize. Machine learning is being used to create techniques that can learn from labelled or unlabeled information, as well as learn from their surroundings. Machine learning is utilized in various areas, but mainly in the healthcare industry, where it provides significant advantages via appropriate decision and prediction methods. The proposed work introduces a remote system that can continuously monitor the patient and can produce an alert whenever necessary. The proposed methodology makes use of different machine learning algorithms along with cloud computing for continuous data storage. Over the years, these technologies have resulted in significant advancements in the healthcare industry. Medical professionals utilize machine learning tools and methods to analyse medical data in order to detect hazards and offer appropriate diagnosis and treatment. The scope of remote healthcare includes anything from tracking chronically sick patients, elderly people, preterm children, and accident victims. The current study explores the machine learning technologies' capability of monitoring remote patients and alerts their current condition through the remote system. New advances in contactless observation demonstrate that it is only necessary for the patient to be present within a few meters of the sensors for them to work. Sensors connected to the body and environmental sensors connected to the surroundings are examples of the technology available. [ABSTRACT FROM AUTHOR]

The purpose of this research is to assess the traffic situations of the existing system by evaluating the Level of service as a key component. Traffic survey have been carried out for the analysis of AADT, Design service volume, capacity and Leve of Service of the city road networks. By using AADT, the LOS have been estimated for the analysis of future traffic condition. Peak hour traffic survey data have been collected for the analysis of AADT and Level of Service. LOS has been converted into percentile form for the analysis of the future LOS. It has been observed that the LOS of the roads of the major traffic operations have mostly same LOS in the morning & evening peak hours which signifies that the traffic movement and the pattern of movement are same in both the traffic session of peak hours. The rapid decrease in LOS has been presented in results, which means, LOS will reduce from time to time and the unimproved existing traffic system will create a problem for motorists and pedestrians. Hence, the existing traffic systems must be evaluated in order to cope with future traffic demands and problems. [ABSTRACT FROM AUTHOR]