Proposed algorithm for image classification using regression-based pre-processing and recognition models

Image classification algorithms can categorise pixels regarding to image attributes with the pre-processing of learner’s trained samples. The precision and classification accuracy are complex to compute due to the variable size of pixels (different image width and height) and numerous characteristics of image per se. This research proposes an image classification algorithm based on regression-based pre-processing and the recognition models. The proposed algorithm focuses on an optimization of pre-processing results such as accuracy and precision. To evaluate and validate, recognition model is mapped in order to cluster the digital images which are developing the problem of a multidimensional state space. Simulation results show that compared to existing algorithms, the proposed method outperforms with the optimal number of precision and accuracy in classification as well as results higher matching percentage based upon image analytics

Peer-to-peer Approach for Distributed Privacy-preserving Deep Learning

The revolutionary advances in machine learning and Artificial Intelligence have enables people to rethink how we integrate information, analyze data, and use the resulting insights to improve decision making. Deep learning is the most effective, supervised, time and cost efficient machine learning approach which is becoming popular in building today’s applications such as self-driving cars, medical diagnosis systems, automatic speech recognition, machine translation, text-to-speech conversion and many others. On the other hand the success of deep learning among others depends on large volume of data available for training the model. Depending on the domain of application, the data needed for training the model may contain sensitive and private information whose privacy needs to be preserved. One of the challenges that need to be address in deep learning is how to ensure that the privacy of training data is preserved without sacrificing the accuracy of the model. In this work, we propose, design and implement a decentralized deep learning system using peer-to-peer architecture that enables multiple data owners to jointly train deep learning models without disclosing their training data to one another and at the same time benefit from each other’s dataset through exchanging model parameters during the training. We implemented our approach using two popular deep learning frameworks namely Keras and TensorFlow. We evaluated our approach on two popular datasets in deep learning community namely MNIST and Fashion-MNIST datasets. Using our approach, we were able to train models whose accuracy is relatively close to models trained under privacy-violating setting, while at the same time preserving the privacy of the training data

Deep Learning Classification of Building Types in Northern Cyprus

Among the areas where AI studies centered on developing models that provide real-time solutions for the real estate industry are real estate price forecasting, building age, and types and design of the building (villa, apartment, floor number). Nevertheless, within the ML sector, DL is an emerging region with an Interest increases every year. As a result, a growing number of DL research are in conferences and papers, models for real estate have begun to emerge. In this study, we present a deep learning method for classification of houses in Northern Cyprus using Convolutional neural network. This work proposes the use of Convolutional neural networks in the classification of houses images. The classification will be based on the house age, house price, number of floors in the house, house type i.e. Villa and Apartment. The first category is Villa versus Apartments class; based on the training dataset of 362 images the class result shows the overall accuracy of 96.40%. The second category is split into two classes according to age of the buildings, namely 0 to 5 years Apartments 6 to 10 years Apartments. This class is to classify the building based on their age and the result shows the accuracy of 87.42%. The third category is villa with roof versus Villa without roof apartments class which also shows the overall accuracy of 87.60%. The fourth category is Villa Price from 10,000 euro to 200,000 Versus Villa Price from 200,000 Euro to above and the result shows the accuracy of 81.84%. The last category consists of three classes namely 2 floor Apartment versus 3 floor Apartment, 2 floor Apartment versus 4 floor Apartment and 2 floor Apartment versus 5 floor Apartment which all shows the accuracy of 83.54%, 82.48% and 84.77% respectively. From the experiments carried out in this thesis and the results obtained we conclude that the main aims and objectives of this thesis which is to used Deep learning in Classification and detection of houses in Northern Cyprus and to test the performance of AlexNet for houses classification was successful. This study will be very significant in creation of smart cities and digitization of real estate sector as the world embrace the used of the vast power of Artificial Intelligence, machine learning and machine vision

Vision Based Vehicles Detection for Intelligence Transportation Systems

Research in advanced driver help machine (ADAS) is a vital step towards accomplishing the intention of the autonomous smart automobile. ADAS is the machine to help the driver inside the using technique due to the fact maximum road injuries took place due to human blunders. Vehicle detection and distance estimation is a crucial solution for ADAS. This paper aims to reduce traffic accidents on the road using computer vision technologies and to implement the driver assistance system. In this paper, firstly, this system inputs the video and segments the videos as the frames. After segmenting the images, vehicle detection results are represented. In the experiments, own datasets are created by capturing videos in Nay Pyi Taw, Myanmar and detection results are described

Survey on Detection Methods for Self-Driving Cars

Accurate vehicle detection or classification plays an important role for self-driving cars. Objects classification and detection can be used in various such as Robotics, Medical Diagnosis, Safety, Industrial Inspection and Automation, Human Computer Interface, Advanced Driver Assistance System and Information Retrieval. In this article, we investigated the methods of detection and classification in context images and videos. SIFT, HOG, SVM, CNN, faster RCNN and YOLO methods are reviewed to detect and recognize the objects. The paper aims to know the methods that detect the obstacles on the way to reduce the traffic accidents. We summarize the results, faster-RCNN is better than the other methods for real-time citing the advantages and disadvantages of existing methods

Building Smart System by Applied Deep Learning and Spatial Indoor Agent Based Model for a New Adaptation University Learning Process Post Covid-19

The impact of COVID-19 implied various restrictions on people’s mobility, especially for the higher education communities, by implementing the Learning from Home approach. This approach has altered the behavior of a human on a daily basis for a year long. Subsequently, the global vaccination program has been the advent of a “New Normal” approach as it reenables the direct human interactions by following health protocols to abide such as social distancing. This study investigated the pedestrian flow in the Department of Urban and Regional Planning (DURP) lecture building, Brawijaya University, and predicted the potential crowd spots using the Integrated Agent-Based Model (ABM), Computer Vision, and the Geographical Information System on an Indoor scale. Additionally, alternative designs of pedestrian flow were proposed to prevent crowds from occurring. The results showed the East and West entrance paths of the DURP building have high traffic, so the proper response is to organize the Southside door as an alternative entrance for pedestrian access. Moreover, the opening of the south gate could reduce the crowd spots on the 2nd Floor of the DURP lecture building

Hardware development of autonomous mobile robot based on actuating lidar

Object detection using a LiDAR sensor provides high accuracy of depth estimation and distance measurement. It is reliable and would not be affected by light intensity. However, high-end LiDAR sensors are high in cost and require high computational costs. In some applications such as navigation for blind people, sparse LiDAR point cloud are more applicable as they can be quickly generated and processed. As opposed to a point cloud generated from high-end LiDAR sensors where many algorithms have been developed for object detection, sparse LiDAR point clouds still possess large room for improvement. In this research, we present the construction of an autonomous mobile robot based on a single actuating LiDAR sensor, with human subjects as the main element to be detected. From here, the extracted values are implied on k-NN, Decision Tree and CNN training algorithm. The final result shows promising potential with 91% prediction when implemented on the Decision Tree algorithm based on our proposed system of a single actuating LiDAR sensor

Veicoli Autonomi: viaggio tra algoritmi ed etica

La tesi ha il primario obiettivo di far luce sui bias negli algoritmi alla base dei veicoli a guida autonoma(VA). Dai vari casi di studio visionati sono stati rilevati chiaramente dei bias in termini di performance nei metodi di riconoscimento di un ostacolo che può essere un pedone, un ciclista o un altro veicolo. Nella seconda parte si discute circa la sorgente di tali bias, le implicazioni etiche che essi hanno, tra cui il concetto di etica delle conseguenze e dei doveri. Si riportano possibili metodi per la correzione ed eventuali ostacoli alla soluzione di questi problemi