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System Built on the Berkeley Grounds that Permits Testing of Various Control Methodologies for Controlling an AC so as to Investigate Tradeoffs between Vitality Utilization and following a Temperature Set Point.
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Room Temperature prediction in Air Conditioners is highly challenged and ambiguous in today's life. To develop the system, some hardware like raspberry pi zero, thermal sensor, microcontroller, IR sensor and the room’s AC with existing remote are used. The proposed system is implemented through an embedded system by using the Python programming lan...
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It is common to use facial expressions to tell if a student is engaged in learning, but there are many situations where this is not practical or reliable. These expressions may simply be too subtle to see or difficult to interpret. In most cases, there are too many students to look around at, and quickly assess the entire room. The solution uses a...
In this study, the design and development of a sensor made of low‐cost parts to monitor inclination and acceleration are presented. Α micro electro‐mechanical systems, micro electro mechanical systems, sensor was housed in a robust enclosure and interfaced with a Raspberry Pi microcomputer with Internet connectivity into a proposed tilt and acceler...
Navigating through an environment can be challenging for visually impaired individuals, especially when they are outdoors or in unfamiliar surroundings. In this research, we propose a multi-robot system equipped with sensors and machine learning algorithms to assist the visually impaired in navigating their surroundings with greater ease and indepe...
An automated embedded system with overweight and location detection of a vessel has been planned, developed and executed. To eliminate overweight issues and able to locate the vessel for stopping accidents, an embedded system has been developed. Based on “Archimedes Principle Formula” an algorithm has been formed, it works on data that have been co...
Citations
... Modern technology is all about performance and speed [29][30] [39]. Today is the scientific and technical era. ...
One such complicated and exciting problem in computer vision and pattern recognition is identification using face biometrics. One such application of biometrics, used in video inspection, biometric authentica-tion, surveillance, and so on, is facial recognition. Many techniques for detecting facial biometrics have been studied in the past three years. However , considerations such as shifting lighting, landscape, nose being farther from the camera, background being farther from the camera creating blurring, and noise present render previous approaches bad. To solve these problems, numerous works with sufficient clarification on this research subject have been introduced in this paper. This paper analyzes the multiple methods researchers use in their numerous researches to solve different types of problems faced during facial recognition. A new technique is implemented to investigate the feature space to the abstract component subset. Principle Component Analysis (PCA) is used to analyze the features and use Speed up Robust Features (SURF) technique Eigenfaces, identification, and matching is done respectively. Thus, we get improved accuracy and almost similar recognition rate from the acquired research results based on the facial image dataset, which has been taken from the ORL database.
... It reviews the works previously on dynamic, Haar, Surf, Fea-Accu cascading methods for face detection [28][29][30]. Here, each of the techniques is described in brief to give an understanding of their working process [31][32][33][34][35][36]. Each method has a different performance rate which is portrayed in this paper as well. ...
This paper intends to evaluate previous works done on different cascading classifiers for human face detection of image data. The paper includes the working process, efficiency, and performance comparison of different cascading methods. These methods are Dynamic Cascade, Haar Cascade, SURF cascade, and Fea-Accu Cascade. Each Cascade classifier is described in the paper with their working procedure and mathematical induction as well. Each technique is backed with proper data and examples. The accuracy rate of the method is given with comparison to analyze the performance of the methods. In this literature, the human face detection process using cascading classifiers from image data is studied. From the study, the performance rate and comparison of different cascading techniques are highlighted. This study will also help to determine which methods are to be used for achieving an accurate accuracy depending on the data and circumstances.
... In this section,basically different papers are discussed with their method,pros and cons of detecting DOS attacks on wireless sensor [1][2][3][4][5] networks. In [6] and [7], Denial of Service Attacks are categorized . ...
... The well-known counter measures and security mechanisms of all the attacks are also mentioned in this article. In [2],this article contains custom dataset of intelligent underwater wireless sensor network which can be divided into four categories of DoS attacks (gray hole, black hole, scheduling attacks and flooding).Method used to train datasets is Artificial Neural Networks to classify them into different DoS attacks. The experimental work carried out here has a high classification rate and accuracy, which is worth mentioning attack with the suggested dataset.To create the structure of an intrusion detection system to resist DoS attacks at an affordable cost is the main goal of this paper.The results considered have been successfully classified as a DoS attack with higher detection rate. ...
Wireless sensor networks are the new emerging technologies that are the combination of wireless devices, small, effective sensors and special embedded system design with them. Basically WSN gathers data from very sensitive and harsh environments. Then after processing,they transmit all the information to base station or user application for their further use. But in their design,there is some design constraints like less memory,power or less secured system. For this they have faced lots of attacks. Denial of service (DOS) is one of the most crucial of them which attacks the whole network system on each layer separately and makes the whole network paralysed and jeopardized. In this review paper, all the attacks of DOS are discussed and their countermeasures are also discussed here attack wise. Introduction: Wireless sensor networks are getting much attention and popularity day by day because of its vast application on different parts of human life. It is basically making life easier by getting the updated information from its combination of wireless technology, tiny sensors and embedded systems and devices. WSN can work in any environment like rain , sunlight, cold breeze and also in harsh environment. So it also has to face some attack on it. Denial of service (DOS) is one of those attacks. Because of its design constraints , it is much weaker against those attacks. So in order to get the proper feedback from the sensor nodes of WSN proper counter measures should be taken against those attacks. Wireless sensor networks are basically a sensory system which sense the different parts of environment and gather needed information. It is used in different sectors like monitoring of traffics, to diagnosis of healthcare problems, nuclear plantation, military network communication,weather update and information collection, ensuring security of a system etc. Wireless sensor networks must deliver security, integrity and correct output. But because of low power consumption, their tiny body structure and limitations of memory, DOS attack easily takes place and security vulnerability increases. Wireless sensor networks are much easier to implement in any situation and environment ,it is also very cost effective super fast than any other sensory device. tacks .
... The consumers can monitor the data about the crops through the web (output monitor). The proposed system also sends the alert notification message to the consumers if any lacking is found by the sensors data [18]. The Arduino outputs are often attached to other display modules. ...
... An algorithm based on detection and tracking was utilized to minimize false fire alarms, often employed using conventional electrical methods. In recent years IoT [18][19][20][21][22][23][24][25][26][27], machine learning [28][29][30][31] and artificial intelligence [32][33] did an excellent job of solving such types of problems. Machine learning [34][35][36][37] can assist in demystifying the hidden patterns in IoT data by evaluating large quantities of data using powerful algorithms. ...
... In order to assess performance with a wider dataset, we can attempt to add further models to compare with Mobilenetv2. In the future, we will integrate this model with IoT [23][24][25][26][27] to detect rotten fruits automatically by AI and IoT. ...
Mostly in the agriculture sector, identifying rotten
fruits has been critical. The classification of fresh and rotting fruits
is typically carried out by humans, which is ineffective for fruit
growers. Humans wear out by doing the same role many days, but
robots do not. As a result, the study proposed a method for
reducing human effort, lowering production costs, and shortening
production time by detecting defects in agricultural fruits. If the
defects are not detected, the contaminated fruits can contaminate
the good fruits. As a result, we proposed a model to prevent the
propagation of rottenness. From the input fruit images, the
proposed model classifies the fresh and rotting fruits. We utilized
three different varieties of fruits in this project: apple, banana, and
oranges. The features from input fruit images are collected using
a Convolutional Neural Network, and the images are categorized
using Max pooling, Average pooling, and MobileNetV2
architecture. The proposed model's performance is tested on a
Kaggle dataset, and it achieves the highest accuracy in training
data is 99.46% and in the validation set is 99.61% by applying
MobileNetV2.The Max pooling achieved 94.49% training
accuracy and validation accuracy is 94.97%. Besides, the Average
pooling achieved 93.06% training accuracy and validation
accuracy is 93.72%. The findings revealed that the proposed CNN
model is capable of distinguishing between fresh and rotting fruits.
... For measuring accuracy with a larger dataset, we would attempt to apply further models to compare with our proposed model. Our model can be applied to any automated system [29] or IoT [30][31][32][33][34] embedded device for face recognition more accurately. ...
... The number of nodes and leaves in a decision tree, as well as the PCC, which represents the percent of correctly classified [33,34] items in the testing collection, are important metrics for judging a pruning method's results. Therefore, the following Table I and II show the result of size and PCC before and after implementing pre-pruning and post-pruning. ...
... Our primary objective was to propose a compatible model with high accuracy such that mask identification will be simple throughout the pandemic. In order to assess performance with a wider dataset, we can attempt to add further models to compare with Mobilenetv2 and tried to integrate this model with IoT [32][33][34][35] to detect humans without masks automatically. ...
... In order to assess performance with a wider dataset, we can attempt to add further models to compare with Mobilenetv2. In the future, we will integrate this model with IoT [23][24][25][26][27] to detect rotten fruits automatically by AI and IoT. ...
