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Wireless Sensor Networks in Precision Monitoring of Crops
The sensor-based breadboard is rapidly covering almost every application from human health monitoring to prediction of diseases in accordance with the weather change. This paper presents a sensor based precision crop monitoring system for agriculture application and estimates the energy consumption of the sensor nodes. This high accuracy energy efficient system drastically reduces the damages to the crops and investment made to it. The main focus of the proposed research work is to reduce the energy consumption and minimize the traffic between the nodes of the sensor during the transmission of sensor information. The qualitative metrics has been carried to evaluate the performance of the proposed system which outperform the existing scenario. 2022 IEEE. -
Technologies Driving Digital Payments in India: Present and Future
The payments market in India has been witnessing a significant transformation in recent years. The Indian payments market has robustly and consistently been moving towards digitization due to enhanced digital infrastructure, favourable government policies, and initiatives, availability of new technologies, disruptive innovations, and changes in the mindset of the customers. India tops in the worlds real-time digital payments with 20.5billion transactions in the year 2020 despite the adverse effect of the COVID-19 pandemic. This article deals with the growth of the Indian digital payments market and the technologies that drive the digital payments space at present and in the future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Identification of Predominant Genes that Causes Autism Using MLP
Autism or autism spectrum disorder (ASD) is a developmental disorder comprising a group of psychiatric conditions originating in childhood that involve serious impairment in different areas. This paper aims to detect the principal genes which cause autism. Those genes are identified using a multi-layer perceptron network with sigmoid as an activation function. The multi-layer perceptron model selected sixteen genes through different feature selection techniques and also identified a combination of genes that caused the disease. From the background study, it is observed that CAPS2 and ANKUB1 are the major disease-causing genes but the accuracy of the model is less. The selected 16 genes along with CAPS2 and ANKUB1 produce more accuracy than the existing model which proved 95% prediction rate. The analysis of the proposed model shows that the combination of the predicted genes along with CAPS2 and ANKUB1 will help to identify autism at an early stage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An IoT-Based Model for Pothole Detection
Maintenance of the good roads plays a very important role in the growth of the country. Poorly maintained roads can lead to potholes which causes severe accidents. To overcome the damage caused by poor roads, the pothole detection model has been proposed in this paper. In recent days, the Internet of Things (IoT)-embedded models are developed in different applications. The main objective of the proposed work is to design the IoT prototype to collect data which can be used to detect potholes and humps. This prototype is embedded with three sensors, namely accelerometer, ultrasonic sensor, and GPS. The data from these sensors is collected by the controller and transmitted by Wi-Fi module to store in the cloud. The collected data can be downloaded as a spreadsheet from the cloud and can be used for different data analysis applications like pothole notifier application. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
DPETAs: Detection and Prevention of Evil Twin Attacks on Wi-Fi Networks
Numerous types of threats could become vulnerable to Wi-Fi networks. In terms of preventing and reducing their effect on the networks, it has become an imperative activity of any user to understand the threats. Even after thoroughly encrypting them, the route between the attackers device and the victims device may even be vulnerable to security attacks on Wi-Fi networks. It has also been noted that there are current shortcomings on Wi-Fi security protocols and hardware modules that are available in the market. Any device connected to the network could be a possible primary interface for attackers. Wi-Fi networks that are available in the transmission range are vulnerable to threats. For instance, if an Access Point(AP) has no encrypted traffic while it is attached to a Wi-Fi network, an intruder may run a background check to launch the attack.And then, attackers could launch more possible attacks in the targeted network, in which the Evil Twin attack have become the most prominent. This Evil Twin attack in a Wi-Fi network is a unique outbreak mostly used by attackers to make intrusion or to establish an infection where the users are exploited to connect with a victims network through a nearby access point. So, there are more chance to get users credentials by the perpetrators. An intruder wisely introduces a fake access point that is equivalent to something looks like an original access point near the network premises in this case. So, an attacker is capable of compromising the network when a user unconsciously enters by using this fake access point. Attackers could also intercept the traffic and even the login credentials used after breaching insecure networks. This could enable monitoring the users and perhaps even manipulating the behavior patterns of an authorized network user smoother for attackers. The key consideration of this research paper is the identification and avoidance of the Evil Twin attack over any Wi-Fi networks. It is named as DPETAs to address the strategies that intruders use to extract identities and what users need to do to keep them out of the networks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
District Level Analytical Study of Infant Malnutrition in Madhya Pradesh
One of the main causes for Indias high infant mortality rate is malnutrition. It can be addressed using three broad groups of conditions: stunting, wasting, and underweight. Other factors such as sanitation, poverty, breastfeeding also contribute to the prevalence of malnutrition. Understanding the contribution of these factors and thus, eliminating them, to reduce malnutrition, is the purpose of this study. In this chapter, the district-level data obtained through NFHS-4 is used for analytical study for infant malnutrition, in Madhya Pradesh. Hierarchical Agglomerative clustering is used to group the districts based on the factors such as exclusively breastfeeding, inoculation, breastfeeding within one hour, no inoculation. The proposed model presents the effect of each factor, on infant malnutrition. It will help decision-makers and the government to shortlist the most appropriate districts contributing to malnutrition and to take curative action to reduce the rate of infant malnutrition. It is a generic model which can be utilized by other states to study infant malnutrition. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Kubernetes for Fog Computing - Limitations and Research Scope
With the advances in communications, Internet of Everything has become the order of the day. Every application and its services are connected to the internet and the latency aware applications are greatly dependent on Fog Infrastructure with the cloud as a backbone. With these technologies, orchestration plays an important role in coordinating the services of an application. With multiple services contributing to a single application, the services may be deployed distributed in multiple server. Proper coordination with effective communication between the modules can improve the performance of the application. This paper deals with the need for orchestration, challenges, and tools with respect to edge/fog computing. Our proposed research solution in the area of intelligent pod scheduling is highlighted with the possible areas of research in Microservices for Fog infrastructure. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Grading of Apples Using Multiple Features
Apple is the most demanding food product that has the utmost importance when it comes to drupes. Food is the very basic necessity for our survival. Every new day brings a change, and the demand for a better quality is no greed. Quality food benefits the health of the living beings, and thus, it increases the economic growth of our country. There is a huge possibility that identifying the different varieties of apples is quite a tedious job for these traders and time consuming. Generally, identification is done manually by the very three basic senses: sight, hearing and smell. In the proposed work, an image processing technique is used to differentiate between the varieties of apples such that the manual process can be eliminated. Commercially available seven varieties of apple with various size, shape and color are considered to create database. Apples are purchased from different places across Karnataka, India to create the database. Various spatial and frequency domain based features are extracted from the images of apple. Naive Bayes, Random Forest and Multilayer perceptron (MLP) classifiers are used and got motivating results. An average accuracy of 78.47% is obtained using methods like Fourier Transform and Discrete Cosine Transform. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Computational Methods to Predict Suicide Ideation among Adolescents
Suicide has been a prominent cause of death worldwide, regardless of age, sex, geography, and so on, and predominantly suicide among teens, increased as the years have passed. Suicide ideation, suicide risk, suicide attempts have been studied extensively, and the most common cause has been identified as depression, followed by familial concerns, hereditary factors, stress, avoidance fear, and a variety of other variables. When visited by a doctor, most adolescents are unaware of their mental state and hence do not take action on their own or are not assisted by family or peer members to overcome their fear of social stigma or the treatment they must undergo. According to popular belief, early treatment and detection are the most effective ways to reduce the risk of suicide. As a result, the focus of this study is to illustrate some of the computational strategies utilized in deep learning and machine learning fields to detect kids at risk of suicide 2022 IEEE. -
AROSTEV: A Unified Framework to Enhance Secure Routing in IoT Environment
The Internet of Things (IoT) is a global network which collects, process, and analyzes the data. IoT sensors and devices are limited to low memory, power, and processing capabilities. The RPL is a proactive routing protocol which is mainly intended for the IoT. There is a possibility of routing vulnerabilities, which masquerade the data in IoT environment. In order to overcome this problem, a framework called AROSTEV is proposed which comprises of three techniques such as RDAID, RIAIDRPL, and E2V. The primary objective of AROSTEV framework is to detect and mitigate the routing attacks such as rank decreased attack (RDA), rank increased attack (RIA), and rank inconsistency attack (RInA), respectively. Each technique takes the responsibility to progress its activity against the internal routing attacks. This framework can be used to implement the smart city environment. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Machine Learning Entrenched Brain Tumor Recognition Framework
Brain tumor detection plays a significant role in medical image processing. Treatment for patients with brain tumors is primarily dependent on faster detection of these tumors. More rapid detection of brain tumors will help in the improvement of the patient's life chances. Diagnosis of brain tumors by doctors most commonly follow manual segmentation, which is difficult and time-consuming; instead, automatic detection is necessary. Nowadays, automatic detection plays a vital role and can be a solution to detecting brain tumors with better performance. Brain tumor detection using the MRI images method is an essential diagnostic tool for predicting brain tumors; the implementation for these kinds of detection can be done using various machine learning algorithms and methodologies. It helps the doctors understand the actual progression of the evolving tumor, allowing the doctors to decide how the treatment has to be given for that particular patient and measures required to follow up. Therefore, the intention is to create a framework to detect brain tumors in MRI images using a machine learning algorithm and analyze the performance of the brain tumor detection using sensitivity and specificity, which helps us to analyze how well the algorithm has performed in detecting the brain tumors accurately and develop a mobile application framework in which the MRI images can be directly scanned to know whether the cancer is present in a scanned MRI image or not. 2022 IEEE. -
Negative Domination inNetworks
We introduce s-domination in signed graphs which is based on the number of negative edges between the dominating set and its complement. The s-domination in both the positive and negative homogeneous signed graph will be studied for each value of s. As a special case, the properties of s-domination in sum signed graphs will be analyzed. The maximum value of s for a graph for which the s-domination exists is identified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Mechanical strength and impact resistance of hybrid fiber reinforced concrete with coconut and polypropylene fibers
This experimental study investigates the mechanical properties and resistance to impact of concrete reinforced with coconut fibers (CF) and polypropylene fibers (PPF). The fiber proportions were decided based on the results obtained from the tests on coconut fiber reinforced concrete (CFRC) and polypropylene fiber reinforced concrete (PPFRC), tested individually. PP fibers of 12 mm and 24 mm of 0.1%, 0.2%, and 0.3% of the volume of concrete were used in PPFRC. Coconut fibers having 50 mm and 75 mm of 0.2%, 0.3%, and 0.4% of the volume of concrete were used in CFRC. Based on test results, PPF (12 mm) and CF (50 mm) were selected for hybrid fiber reinforced concrete (HyFRC). By varying both PPF and CF content, three different proportions with a total fiber content of 0.2% and 0.3% of the volume of concrete were selected. The improvement in strength was observed to be maximum when the total fiber content in the hybrid fiber reinforced concrete was 0.3%. The increase in impact resistance of HyFRC was almost double that of individual FRC and three times that of plain concrete. 2022 -
Synthesis and characterization of Poly-Vinyl Alcohol-Alumina composite film: An efficient adsorbent for the removal of Chromium (VI) from water
Composite poly vinyl alcohol-alumina films were synthesized by a novel eco-friendly route in the absence of template. The physico-chemical nature of the synthesized film was studied using different characterization techniques. The poly vinyl alcohol-alumina composite film was found to be an efficient adsorbent for the removal of Chromium (VI) at higher concentrations from water. The preparation conditions were optimized to synthesize an efficient adsorbent film for the removal of chromium. The surface properties, chemical composition and amorphous nature of the film confirmed by different characterisation techniques attributes to the chromium removal efficiency of the film. Poly vinyl alcohol-alumina films are economically cheap, easy to prepare, efficient adsorbent for removal of chromium (VI) eco-friendly in nature and reusable with effortless regeneration methods. 2022 -
Detection of Various Security Threats in IoT and Cloud Computing using Machine Learning
Due to the growth of internet technology, there is a sharp rise in the growth of IoT enabled devices. IoT (Internet of Things) refers to the connection of various embedded devices with limited processing and memory. With the heavy adoption of IoT applications, cloud computing is gaining traction with the ever-increasing demand to process and compute a massive amount of data coming from various devices. Hence, cloud computing and IoT are often related to each other. However, there are two challenges in deploying the IoT and cloud computing frameworks: security and Privacy. This article discusses various types of security threats affecting IoT and cloud computing, and threats are classified using machine learning (ML). ML has gained much momentum in recent years and is applied in various domains. One of the main subdomains of machine learning is used in IoT and cloud security. A machine learning model can be trained with data based on which the model can predict the impending security threats. Popular security techniques to protect IoT devices from hackers are IoT authentication, access control, malware detection, and secure overloading. Supervised learning algorithms can be used to detect malware in the runtime behavior of applications. The malware is detected from network traffic and is labeled based on its suspicious behavior. Post identification of malware, the application data is stored in a database trained via an ML classifier algorithm (KNN or Random Forest). With increased training, the model can identify malware applications with higher accuracy. 2022 IEEE. -
Effect of fiber types, shape, aspect ratio and volume fraction on properties of geopolymer concrete A review
Researchers have emphasized on sustainable construction with utilization of industrial wastes or byproducts in production of concrete. Geopolymer concrete is one of the popular construction materials which has shown promising results and potential to substitute conventional energy intensive materials such as Portland cement concrete. Further, the use of fibers has shown potential to overcome various deficiencies of geopolymer concrete. However, there are limited studies which explore the benefits of fiber reinforced geopolymer concrete and its applications. The development of fiber reinforced geopolymer concrete is relatively new construction material and has to be experimentally validated in order to increase its usage in the construction industry. As a result, this review paper is an attempt to discuss the effect of shape, type, aspect ratio and volume fraction of fibers on strength and durability properties of geopolymer concrete. From this detailed review it can be concluded that fiber reinforced geopolymer concrete enhances ductile behavior, tensile strength, toughness & energy absorption capacities. 2022 -
Covid-19 Classification and Detection Model using Deep Learning
One of the deadly diseases in recent years is covid19 which is affecting the lives of peoples. Also leading to severe adverse problems and death. Prevention is done using early diagnosis and medication which in turn helps in early detection of the disease. The basic aim of the paper is to identify and further classify the patients using the chest x-rays. From scratch the Convolutional Neural Network is diagnosed producing a very high accurate and optimum results. In recent years, researchers found out that in the radiological images such as like x-rays, the traces of covid-19 can be found. In few areas, a good accuracy of the covid-19 detection cannot be achieved due to lack of the people who test so the artificial intelligence is combined with the radiological image. In machine learning the models used are deep learning by automatizing the actions and making it certain by swift, skillful and proficient outcome produced by the chest images provided by the patients. There are several layers like convolutional layer, max pooling layer etc. which are initiated and are used with aid of ReLU activation function. These images given as inputs are also classified accordingly. There is a sequence of neurons being given as input to the active dense layer and there is a result to the input by a sigmoidal function. There is a rise in efficiency because the models are trained and there is a decline of loss at the same time. If there is a model where fitting is done earlier to the overfitting and is restricted from implementing in the data augmentation. There is a better and efficient involvement of suggestions to models of deep learning. Further there is a classification of chest images for identifying and analyzing covid19. So, to check the Covid detection, the images are used as raw. In this paper a model is proposed to have good accuracy in the classification between Covid and normal and further it can be classified into three categories like Covid, pneumonia, normal. There is a 98.08% for the first one and 87.02% for the second one. By introducing 17 convolutional layers and using the Darknet model used for classifying you only look once (YOLO) for the live identification of the objects and multiple layers of filters are used. In the model there is an initial screening. 2022 IEEE. -
An Optimized Algorithm for Selecting Stable Multipath Routing in MANET Using Proficient Multipath Routing and Glowworm Detection Techniques
Mobile Ad Hoc Networks (MANETs) depend on the selected and constant path with an extended period and the flexibility of the battery power condensed in searching end nodes, leading to numerous link failures. This kind of link damages occurs, and it also affects the packet success rate. We presented a Proficient Multipath Routing and Glowworm detection (PMGWD) technique to overcome such a Manets failure. Initially, a proposed Proficient Multipath Routing (PMR) technique identifies the damaged or failure routes and continues communication inefficiently. Secondly, the Glowworm detection node technique is implemented for both fault node identification and for extending the nodes network lifetime. Another reason to select the glowworm optimization is to update the node based on the glow to improve its neighbor its search space. Lastly, the PMGWD technique is utilized for identifying an optimal route and fault nodes in the manet. It is achieved to correct the identification of fault nodes using the glowworm detection node technique, and it helps to explore more paths for the optimal route by using proficient multipath routing. Hence, this proposed PMGWD technique is used to perform a problem-free communication process in a network system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Rendering View of Kitchen Design Using Autodesk 3Ds Max
The method of creating a 3D kitchen design model is clarified, including setting up the sources, working with editable poly, information in the inside of the kitchen design, and applying turbo-smooth and symmetry modifier. The way materials are introduced to the model which is defined in addition to lighting the environment and setting up the renderer. Rendering methods and procedures are also defined. Multiple images were drawn to create the final rendering. The goal of our research is to produce a kitchen design that uses materials to enhance models. Cylinder, sphere, box, plane, and splines were the shapes employed. Editable poly, editable spline, and UVW map are the modifiers. Finally, we enhanced the model using a material editor and target lighting. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effective View of Swimming Pool Using Autodesk 3ds Max: 3D Modelling and Rendering
As well as setting up the sources, working with editable poly, information in the interior of the swimming pool, using turbo-smooth and symmetry modifier, this procedure of making a 3D swimming pool model is clarified. The lighting the scene and setting up the rendering, the method in which substances are added to the replica is defined. The methods and techniques of rendering are defined, too. The final rendering is the result of multiple images being drawn. The aim of our research work is to create a swimming pool design with enhancing models with materials affect. The shapes used for that are cylinder, sphere, box, plane and splines. The modifiers are editable poly, editable spline and UVW map. Finally, we used a material editor and target lights for enhancing the model. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.