Iraqi Journal for Computers and Informatics
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IMPROVING MOVEMENT OF WHEELED GROUND ROBOTS ON SLOPES USING LIDAR TECHNOLOGY: MAPPING, PLANNING, AND OPTIMIZATION
Get PDFThe development of wheeled ground robots has enabled them to be used for a variety of tasks. These robots must be able to move with accuracy and precision, especially when faced with obstacles or inclines. To improve the movement of these robots on a slope, lidar data can be used to detect the location and shape of obstacles. In recent years, Lidar technology has become an essential tool for various robotic applications. It has proven to be a game-changer in the field of autonomous navigation, especially in situations where robots have to operate in unknown environments. Lidar technology provides a high-resolution 3D map of the environment around the robot, enabling it to navigate autonomously while avoiding obstacles. In this paper, we discuss the use of Lidar technology in improving the movement of a wheeled ground robot on a slope. We describe the steps involved in obtaining Lidar data, processing the data to create a 3D map of the environment, and using the map to plan more efficient movement of the robot. We present an applied example of how Lidar data improves the movement of a ground robot with wheels on a slope, calculating the inclination of the ground, calculating the force required for the movement of the robot, creating three-dimensional models of the terrain to be navigated, creating plans for more efficient movement, and reducing damage and wear of the robot
ENHANCING HUMAN-ROBOT INTERACTION THROUGH GROUP EMOTION RECOGNITION
Get PDFAbstract - This article explores within the field of Human-Robot Interaction (HRI), focusing on the complicated relationship between emotions, decision-making, and robot behaviors. Emotions are essential to effective communication and interaction, requiring the development of emotion recognition systems in robots. The article explores both individual and group emotion recognition, including microexpressions and macroexpressions. Group emotion dynamics, encompassing phenomena like emotional contagion, convergence, and social influence, are separated to understand how emotions combine within collective settings. A concept, Group Emotion Recognition (GER), is introduced, providing a framework for recognizing emotions within groups. GER involves proximity metrics, emotion classification, and entropy-based analysis to quantify emotion diversity. The article also outlines how GER can enhance user engagement, personalize interactions, improve group dynamics, and foster social acceptance in various human-robot interaction scenarios. Decision-making based on GER, driven by positive or negative emotion labels, is discussed, highlighting the adaptability and sensitivity required for effective human-robot interactions. Ethical considerations regarding the use of emotion recognition technology are addressed throughout the article, emphasizing responsible implementation. Overall, this work lays a solid foundation for advancing the field of HRI by integrating emotion recognition and decision-making to create emotionally intelligent and socially aware robots
EDIBLE FISH IDENTIFICATION BASED ON MACHINE LEARNING
Get PDFAutomated fish identification system has a beneficial role in various fields. Fish species can usually be identified based on visual observation and human experiences. False appreciation can cause food poisoning. The proposed system aims to efficiently and effectively identify edible fish from poisonous ones based on three machine learning (ML) techniques. A total of 300 fish images are used, collected from 20 species with differences in shapes, sizes, and colors. Hybrid features were extracted and then fed to three types of ML techniques: k-nearest neighbor (K-NN), support vector machine (SVM), and neural networks (NN). The 300 fish images are divided into two: 70% for training and 30% for testing. The accuracy rates for the presented system were 91.1%, 92.2%, and 94.4% for KNN, SVM, and NNs, respectively. The proposed system is evaluated using four terms: precision, sensitivity, F1-score, and accuracy. Results show that the proposed approach achieved higher accuracy compared with other recent pertinent studies
THE USE OF ROUGH CLASSIFICATION AND TWO THRESHOLD TWO DIVISORS FOR DEDUPLICATION
Get PDFThe data deduplication technique efficiently reduces and removes redundant data in big data storage systems. The main issue is that the data deduplication requires expensive computational effort to remove duplicate data due to the vast size of big data. The paper attempts to reduce the time and computation required for data deduplication stages. The chunking and hashing stage often requires a lot of calculations and time. This paper initially proposes an efficient new method to exploit the parallel processing of deduplication systems with the best performance. The proposed system is designed to use multicore computing efficiently. First, The proposed method removes redundant data by making a rough classification for the input into several classes using the histogram similarity and k-mean algorithm. Next, a new method for calculating the divisor list for each class was introduced to improve the chunking method and increase the data deduplication ratio. Finally, the performance of the proposed method was evaluated using three datasets as test examples. The proposed method proves that data deduplication based on classes and a multicore processor is much faster than a single-core processor. Moreover, the experimental results showed that the proposed method significantly improved the performance of Two Threshold Two Divisors (TTTD) and Basic Sliding Window BSW algorithms
Evaluation of Image Cryptography by Using Secret Session Key and SF Algorithm
Get PDFIn the unreliable domain of data communication, safeguarding information from unauthorized access is imperative. Given the widespread application of images across various fields, ensuring the confidentiality of image data holds paramount importance. This study centers on the session keys concept, addressing the challenge of key exchange between communicating parties through the development of a random-number generator based on the Linear Feedback Shift Register. Both encryption and decryption hinge on the Secure Force algorithm, supported by a generator. The proposed system outlined in this paper focuses on three key aspects. First, it addresses the generation of secure and randomly generated symmetric encryption keys. Second, it involves the ciphering of the secret image using the SF algorithm. Last, it deals with the extraction of the image by deciphering its encrypted version. The system’s performance is evaluated using image quality metrics, including histograms, peak signal-to-noise ratio, mean square error, normalized correlation, and normalized absolute error (NAE). These metrics provide insights into both encrypted and decrypted images, analyzing the extent to which the system preserves image quality. This assessment underscores the system’s capability to safeguard and maintain the confidentiality of images during data transmission
Coronavirus Classification using Deep Convolutional Neural Network, Models. and Chest ,X-ray images
Get PDFThe COVID-2019 virus, which was discovered for the first time in December 2019 in the city of Wuhan, China, went on to become a pandemic after rapidly spreading around the globe. As there are currently no reliable automated toolkits on the market, there has been an increase in the demand for supplementary diagnostic tools for COVID19 patients. It may be possible to improve the accuracy of the diagnosis of covid19 disease by making use of more recent developments in artificial intelligence (AI) approaches and radiological imaging. In this research, three different convolution neural networks were applied to raw chest x-rays before the histogram filter was used for the basic pre-processing. The goal was to automatically detect COVID-19. The results that we obtained using the three suggested models indicate that the ResNet50 model provides the greatest classification performance with 96% accuracy , while the InceptionV3 model only achieves 95% accuracy, and the Inception-ResNetV2 model only achieves 82% accuracy
The Detection of Students\u27 Abnormal Behavior in Online Exams Using Facial Landmarks in Conjunction with the YOLOv5 Models
Get PDFThe popularity of massive open online courses (MOOCs) and other forms of distance learning has increased recently. Schools and institutions are going online to serve their students better. Exam integrity depends on the effectiveness of proctoring remote online exams. Proctoring services powered by computer vision and artificial intelligence have also gained popularity. Such systems should employ methods to guarantee an impartial examination. This research demonstrates how to create a multi-model computer vision system to identify and prevent abnormal student behaviour during exams. The system uses You only look once (YOLO) models and Dlib facial landmarks to recognize faces, objects, eye, hand, and mouth opening movement, gaze sideways, and use a mobile phone. Our approach offered a model that analyzes student behaviour using a deep neural network model learned from our newly produced dataset" StudentBehavioralDS." On the generated dataset, the "Behavioral Detection Model" had a mean Average Precision (mAP) of 0.87, while the "Mouth Opening Detection Model" and "Person and Objects Detection Model" had accuracies of 0.95 and 0.96, respectively. This work demonstrates good detection accuracy. We conclude that using computer vision and deep learning models trained on a private dataset, our idea provides a range of techniques to spot odd student behaviour during online tests
PREDICTING MEDICINE DEMAND USING DEEP LEARNING TECHNIQUES
Get PDFMedication supply and storage are essential components of the medical industry and distribution. Most medications have a predetermined expiration date. When the demand is met in large quantities that exceed the actual need, this leads to the accumulation of medicines in the stores, and this leads to the expiration of the materials. If demand is too low, this will have an impact on consumer happiness and drug marketing.Therefore, it is necessary to find a way to predict the actual quantity required for the organization\u27s needs to avoid material spoilage and storage problems. A mathematical prediction model is required to assist any management in achieving the required availability of medicines for customers and safe storage of medicines. The research question is to design a system based on deep learning that can predict the amount of drugs required with high efficiency and accuracy based on the chronology of previous years.Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) are used to build prediction models. Those models allow for the optimization of inventory levels, thus reducing costs and potentially increasing sales. Various measures such as mean squared error (MSE), mean absolute squared error (MASE), root mean squared error (RMSE), and others are used to evaluate the prediction models. RNN model achieved the best result with MSE: 0.019 MAE: 0.102, RMSE: 0.0
USING SPECIAL LETTERS AND DIACRITICS IN STEGANOGRAPHY IN HOLY QURAN
Get PDFBecause of the great development that took place in information transfer and communication technologies, the issue of information transfer security has become a very sensitive and resonant issue, great importance must be given to protecting this confidential information. Steganography is one of the important and effective ways to protect the security of this information while it is being transmitted through the Internet, steganography is a technology to hide information inside an unnoticeable envelope object that can be an image, video, text or sound. The Arabic language has some special features that make it excellent covers to hide information from Through the diversity of the Arabic letters from dotted letters in several forms or vowels or special letters, the Holy Qur’an is considered a cover rich in movements and Arabic grammar, which makes it a wide cover for the purpose of concealing information. The Holy Qur’an is a sacred book where it is not permissible to modify, add or move any of the letters or any diacritical mark to it. The algorithm hides the two bits by uses six special letters of Arabic language. Moreover, it checks for the presence of specific Arabic linguistic features referred Arabic diacritics. The proposed system achieved a high ability to hide as in Surat Al-Baqarah (4524 bits) and also (2576 bits) in Surat Al-Imran and in Surat Al-An’am (2318 bits)
UNDERGROUND CRUDE OIL PIPELINE LEAKAGE DETECTION USING DEXINED DEEP LEARNING TECHNIQUES AND LAB COLOR SPACE
Get PDFComputer vision plays a big role in pipeline leakage detection systems and is one of the latest techniques. Still, it requires a powerful image-processing algorithm to detect objects. The purpose of this work is to develop and implement spill detection in oil pipes caused by leakage using images taken by a drone equipped with a Raspberry Pi 4. The acquired images are sent to the base station along with the global positioning system (GPS) location of the captured images via the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, images are processed to identify contours by dense extreme inception networks for edge detection(DexiNed) deep learning techniques based on holistically-nested edge detection(HED) and extreme inception (Xception) networks. This algorithm is capable of finding many contours in images. To find a contour with black color, the CIELAB color space (LAB) has been used. The proposed algorithm removes small contours and computes the area of the remaining contours. If the contour is above the threshold value, it is considered a spill; otherwise, it will be saved in a database for further inspection. For testing purposes, three different spill areas were implemented with spill sizes of (1 m^2,2 m^2 ,and 3 m^2). Images have been captured at three different heights (5 m, 10 m, and 15 m) by the drone used to capture the images. The result shows that effective detection has been obtained at 10 meters high. To monitor the entire system, a web application has been integrated into the base station