Indian Institute of Information Technology, Pune

Kumar N., Rahman S.S., Dhakad N.

Intelligent Transportation System (ITS) has been emerged an important component and widely adopted for the smart city as it overcomes the limitations of the traditional transportation system. Existing fixed traffic light control systems split the traffic light signal into fixed duration and run in an inefficient way, therefore, it suffers from many weaknesses such as long waiting time, waste of fuel and increase in carbon emission. To tackle these issues and increase efficiency of the traffic light control system, in this work, a Dynamic and Intelligent Traffic Light Control System (DITLCS) is proposed which takes real-time traffic information as the input and dynamically adjusts the traffic light duration. Further, the proposed DITLCS runs in three modes namely Fair Mode (FM), Priority Mode (PM) and Emergency Mode (EM) where all the vehicles are considered with equal priority, vehicles of different categories are given different level of priority and emergency vehicles are given at most priority respectively. Furthermore, a deep reinforcement learning model is also proposed to switch the traffic lights in different phases (Red, Green and Yellow), and fuzzy inference system selects one mode among three modes i.e., FM, PM and EM according to the traffic information. We have evaluated DITLCS via realistic simulation on Gwalior city map of India using an open-source simulator i.e., Simulation of Urban MObility (SUMO). The simulation results prove the efficiency of DITLCS in comparison to other state of the art algorithms on various performance parameters.

Medhane D.V., Sangaiah A.K., Hossain M.S., Muhammad G., Wang J.

The Internet of Things (IoT) plays a vital role in the real world by providing autonomous support for communications and operations, thus enabling and promoting novel services that are commonly used in day-to-day life. It is important to do research on security frameworks for next-generation IoT and develop state-of-the-art confidentiality protection schemes to deal with various attacks on IoT networks. In order to offer prominent features like continuous confidentiality, authentication, and robustness, the blockchain technology comes out as a sustainable solution. A blockchain-enabled distributed security framework using edge cloud and software-defined networking (SDN) is presented in this article. The security attack detection is achieved at the cloud layer, and security attacks are consequently reduced at the edge layer of the IoT network. The SDN-enabled gateway offers dynamic network traffic flow management, which contributes to the security attack recognition through determining doubtful network traffic flows and diminishes security attacks through hindering doubtful flows. The results obtained show that the proposed security framework can efficiently and effectively meet the data confidentiality challenges introduced by the integration of blockchain, edge cloud, and SDN paradigm.

Tawade J.V., Guled C.N., Noeiaghdam S., Fernandez-Gamiz U., Govindan V., Balamuralitharan S.

The current research explores the problem of steady laminar flow of nanofluid on a two dimensional boundary layer using heat transfer of Cassona cross the linearly stretching sheet. The governing equations are partial differential equations which are transformed into non-linear ordinary differential equations by using some similarity transformation. The converted form of the combined non-linear higher-order ODEswith a set of boundary conditions are solved by means of Runge-Kutta 4th-order approach along with the shooting method. The nanoparticle concentration profiles, velocity, and temperature are examined by taking account of their influence of Prandtl number, “Brownian motion parameter”, Lewis number, thermophoresis, and Casson fluid parameter. It is reported that the temperature increase as Nt and Nb increases which causes thickening of the thermal boundary layer. Also it is observed that, there is increment in temperature profile for increasing values of Brownian motion parameter and the energy distribution grows with increment in the values of Thermophoresis parameter. The comparison for the local Nusselt & local Sherwood number has been tabulated with respect to variation of the Brownian Motion Parameter and Thermophoresis parameter. All the findings of the results are graphically represented and discussed. • The research explores the issue of steady laminar flow of nano fluid on a 2D boundary layer using heat transfer of Cassona cross the linearly permeable stretching sheet. • The governing boundary value problem with its typical boundary conditions is statistically resolved. • The 4-th order Runge-Kutta method is combined with the shooting method for finding the results. • The results are presented using some graphs and tables.

Sangaiah A.K., Medhane D.V., Bian G., Ghoneim A., Alrashoud M., Hossain M.S.

Adversary models have been fundamental to the various cryptographic protocols and methods. However, their use in most of the branches of research in computer science is comparatively restricted, primarily in case of the research in cyberphysical security (e.g., vulnerability studies, position confidentiality). In this article, we propose an energy-aware green adversary model for its use in smart industrial environment through achieving confidentiality. Even though, mutually the hardware and the software parts of cyberphysical systems can be improved to decrease its energy consumption, this article focuses on aspects of conserving position and information confidentiality. On the basis of our findings (assumptions, adversary goals, and capabilities) from the literature, we give some testimonials to help practitioners and researchers working in cyberphysical security. The proposed model that runs on real-time anticipatory position-based query scheduling in order to minimize the communication and computation cost for each query, thus, facilitating energy consumption minimization. Moreover, we calculate the transferring/acceptance slots required for each query to avoid deteriorating slots. The experimental results confirm that the proposed approach can diminish energy consumption up to five times in comparison to existing approaches.

Garg R., Maheshwari S., Shukla A.

Skin cancer is one of the most deathful of all the cancers. It is bound to spread to different parts of the body on the off chance that it is not analyzed and treated at the beginning time. It is mostly because of the abnormal growth of skin cells, often develops when the body is exposed to sunlight. Furthermore, the characterization of skin malignant growth in the beginning time is a costly and challenging procedure. It is classified where it develops and its cell type. High Precision and recall are required for the classification of lesions. The paper aims to use MNIST HAM-10,000 dataset containing dermoscopy images. The objective is to propose a system that detects skin cancer and classifies it in different classes by using the convolution neural network. The diagnosing methodology uses image processing and deep learning model. The dermoscopy image of skin cancer undergone various techniques to remove the noise and picture resolution. The image count is also increased by using various image augmentation techniques. In the end, the transfer learning method is used to increase the classification accuracy of the images further. Our CNN model gave a weighted average precision of 0.88, a weighted recall average of 0.74, and a weighted F1 score of 0.77. The transfer learning approach applied using ResNet model yielded an accuracy of 90.51%

Hazra T., Anjaria K.

This paper provides a comprehensive overview of the applications of game theory in deep learning. Today, deep learning is a fast-evolving area for research in the domain of artificial intelligence. Alternatively, game theory has been showing its multi-dimensional applications in the last few decades. The application of game theory to deep learning includes another dimension in research. Game theory helps to model or solve various deep learning-based problems. Existing research contributions demonstrate that game theory is a potential approach to improve results in deep learning models. The design of deep learning models often involves a game-theoretic approach. Most of the classification problems which popularly employ a deep learning approach can be seen as a Stackelberg game. Generative Adversarial Network (GAN) is a deep learning architecture that has gained popularity in solving complex computer vision problems. GANs have their roots in game theory. The training of the generators and discriminators in GANs is essentially a two-player zero-sum game that allows the model to learn complex functions. This paper will give researchers an extensive account of significant contributions which have taken place in deep learning using game-theoretic concepts thus, giving a clear insight, challenges, and future directions. The current study also details various real-time applications of existing literature, valuable datasets in the field, and the popularity of this research area in recent years of publications and citations.

Guled C.N., Tawade J.V., Kumam P., Noeiaghdam S., Maharudrappa I., Chithra S.M., Govindan V.

The current study investigates the laminar flow of boundary layer using a porous surface shrinking exponentially under varying magnetic field, suction/injection, radiation on velocity and thermal slips. The dimensionless nonlinear differential equations were also defined in terms of governing partial differential equations and applying similarity transformations. The equations were analytically solved following the homotopy analysis method (HAM). An excellent agreement is observed in comparison with theavailable literature in special case (Skin friction coefficient). In the current study, an analytic solution is obtained for the laminar incompressible MHD boundary layer viscous fluid flow problem on the exponentially electrically conducting porous shrinking surface. The impact of different parameters like injection/suction, Prandtl number, radiation, velocity slip, thermal slip, as well as the magnetic field on different velocity and temperature values have been presented graphically, and have been discussed in detail. The f″(0) skin friction rises with higher suction parameter s(>0) values, magnetic parameter M but it decreases when the slip parameter value is increased. Whereas heat transfer rate −θ′(0) rises with an increasing M, s and λ..

Venkata Mahendra T., Wasmir Hussain S., Mishra S., Dandapat A.

Hardware search engines (HSEs) have been drawing significant attention in replacing software search algorithms in order to speed up location access and data association in modern systems. Content addressable memory (CAM) is one of the promising HSEs due to its parallel search accessibility. However, it is subjected to considerable dissipation which becomes severe while accessing many components including cells and associated matchlines (MLs) during every search. Ternary CAM (TCAM) based routing tables, especially employed in network systems for packet classification, has put a challenge to design energy-efficient architectures with high-performance and reliable look-up operation. Precharge-free CAM schemes are preferred solutions over precharge types to accomplish high-speed as well as low-power goals of associative memory design. In order to overcome the drawbacks of precharge based designs and also to improve performance during the search, we introduce a precharge-free ternary content addressable memory (PF-TCAM). The proposed searching approach enhances the rate of search by reducing half of the ML evaluation time as it eliminates precharge phase prior to every search by performing search in HALF clock cycle. A 32 × 16-bit proposed macro is designed using 45-nm CMOS technology and post-layout simulations at 1 V supply shows 56% and 63% energy efficiency improvements compared to conventional TCAM and compact TCAM respectively over 25 different search keys despite increasing evaluation speed by 50% with an area overhead of 1 transistor/cell over compact TCAM.

Binnar V., Sharma S.

A key difficulty in agriculture are plant leaf diseases and destructive insects. The development of early treatment options for leaf diseases should be aided by faster and more exact prediction of leaf illnesses, while reducing economic losses. Researchers have been able to considerably improve the overall performance and accuracy of object detection and identification systems because to recent advances in deep learning. Using a deep learning methods, conventional neural network (CNN) models were constructed to identify and diagnose plant leaf dis- ease in basic images of damaged and healthy plants. This paper uses four deep learning models like AlexNet, simple sequential model, MobileNet, and Inception-v3 to detect disease in leaf. Here, new plant diseases dataset has been used for the training and testing. There are 38 distinct classes in all, including basic leaf images of healthy and diseased plants are used. Plant leaf images from the Internet are also tested using this trained model. The models successful outcome makes it an effective early warning tool, as well as a strategy that may be extended to allow a real-world integrated plant disease detection system. After evaluating all four models, we discovered that the MobileNet model is a good fit for the new plant diseases dataset, with training and validation accuracy are 99.07 and 97.52%.

Hasija S., Akash P., Bhargav Hemanth M., Kumar A., Sharma S.

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.