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The development of the behaviour cloning technique allows robots to mimic human experts’ behaviour by observation. The technique is mainly based on model architecture’s design and associated training mechanisms. İt is believed that such an approach will impact the importance of robotics applications in the coming future. The ongoing research presented in this paper has investigated the use of behaviour cloning with image and video data streaming to improve robot learning using imitation of human experts’ behaviour. The investigation has focused on the methodology, algorithms, and challenges associated with training robots to imitate human actions solely based on visual data inputs. An overview of the process of collecting diverse and annotated image and video datasets depicting various human actions and behaviours is presented. To provide efficient and consistent data representation, the preprocessing process includes feature extraction using convolutional neural networks (CNN) and normalization techniques. The CNN model for learning action mappings from visual inputs is described. These models’ training focuses on optimization algorithms and loss functions. A thorough examination of data quality, overfitting, and model generalization issues is addressed and presented. The research’s initial results showed the effectiveness of image and video-based behaviour cloning and how it is leading to more sophisticated and adaptive robotic systems. The limitations of the research are also discussed and presented in this paper.

Through the growth of the fifth-generation networks and artificial intelligence technologies, new threats and challenges have appeared to wireless communication system, especially in cybersecurity. And IoT networks are gradually attractive stages for introduction of DDoS attacks due to integral frailer security and resource-constrained nature of IoT devices. This paper emphases on detecting DDoS attack in wireless networks by categorizing inward network packets on the transport layer as either “abnormal” or “normal” using the integration of machine learning algorithms knowledge-based system. In this paper, deep learning algorithms and CNN were autonomously trained for mitigating DDoS attacks. This paper lays importance on misuse based DDOS attacks which comprise TCP SYN-Flood and ICMP flood. The researcher uses CICIDS2017 and NSL-KDD dataset in training and testing the algorithms (model) while the experimentation phase. accuracy score is used to measure the classification performance of the four algorithms. the results display that the 99.93 performance is recorded.

With high rates of morbidity and mortality, lung cancer continues to be a major problem for world health. A precise prognosis is essential for clinical judgment and patient care. This study provides a unique deep learning method for enhancing the prognosis of lung cancer through multimodal data enhancement and standardized pre-processing. The proposed methodology begins with the comprehensive pre-processing of diverse patient data sources, including medical images, clinical records, and genomic information. Standardization techniques are applied to ensure data consistency and reliability, reducing noise and enhancing the quality of the input data. Furthermore, feature selection and extraction methods are employed to identify the most informative variables for prognostic prediction. To harness the full potential of the integrated data, a deep learning architecture is developed. This architecture combines convolutional neural networks (CNNs) for image analysis, recurrent neural networks (RNNs) for sequential clinical data, and fully connected layers for genomic information. By fusing these diverse data modalities, the model captures intricate patterns and relationships, enabling more accurate prognosis. This research paper introduces a cutting-edge deep learning approach for lung cancer prognosis that leverages standardized pre-processing and multifaceted data enhancement. By integrating medical images, clinical records, and genomic information, our model provides clinicians with a powerful tool for improving patient outcomes through more precise prognostic predictions. This research contributes to the advancement of personalized medicine in lung cancer management, offering new avenues for early intervention and tailored treatment strategies.

Cracks in structures pose significant risks to safety, integrity, and functionality, necessitating efficient and accurate analysis methods. This paper presents an innovative approach to crack analysis by integrating Artificial Intelligence (AI) techniques with Natural Language Processing (NLP) methodologies. Initially, NLP techniques are employed to extract key concepts, methodologies, and findings related to crack analysis from textual sources. Subsequently, features relevant to crack detection, classification, and severity assessment are extracted from the processed text data. AI models, including supervised learning algorithms such as Convolutional Neural Networks (CNNs) and unsupervised clustering methods, are trained using labelled data to detect cracks, classify their types, and assess their severity. Reinforcement learning techniques may also be explored for optimizing inspection strategies and maintenance schedules based on crack analysis results. Furthermore, the integration of sensor data, such as images from cameras and measurements from ultrasonic devices, enriches the analysis process, providing more accurate and comprehensive insights into crack formation and propagation. Real-time monitoring systems, coupled with AI models, enable continuous assessment of structural health, with alert mechanisms in place to notify relevant stakeholders of potential issues. This approach not only streamlines crack analysis processes but also enhances the efficiency and effectiveness of structural health monitoring systems, ultimately contributing to safer and more resilient infrastructure.

PID controller is widely used in many fields. The input sequence and output sequence of a well-parameterized PID controller are transformed into a matrix presented in images in this paper, and a Every-Time step data augmentation algorithm is operated. This paper propose a image to image Convolutional Neural Networks(CNN) structure to learn the PID controller, which incorporates the successful methods in computer vision and deep learning to control field. The simulation is performed using Matlab/Simulink and Keras[1]. The simulation demonstrates that the CNN controller is capable in performance.

In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters. In the stage of characteristics extraction, we are representing the input data into the vector of primitives. These characteristics are linked to pixels’ densities and they are extracted on binary pictures. In the classification stage, we examine four classification methods with two different classifiers types namely the convolutional neural network (CNN) and the Random Forest method. We carried out the experiments with a database containing 3300 samples collected from different writers. The experimental results show that our proposed OCR system is very efficient and provides good recognition accuracy rate of handwriting characters images acquired via Video camera phone.

In this paper we proposed a prostate segmentation and also tumour detection using deep neural networks. The cutting-edge deep learning techniques are useful compared to the challenges of machine learning based feature extraction techniques. Here we proposed a strategy that contains an FCN model that incorporates data from several MRI images, allowing for faster convergence and more accurate segmentation. T1 and DWI volumes may be used together to delineate the prostate boundary, according to this study. Second, we investigated whether this method might be utilized to provide voxel-level prostate tumor forecasts. The cascaded learning method and performed tests to demonstrate its effectiveness.

In order to get a successful and appropriate treatment for the disorder regarding health, précised and identifying it early is much important in the scenario of brain tumor treatment. Prior knowledge and detection of the tumor helps to cope up with good medication, and also helps in saving a life in due time. Bio-medical informatics(BI) and Computer aided diagnosis(CAD) are benefiting neurooncologists in many ways. Machine learning algorithms are now used to do Image processing on medical images and contrast with the information due to manual diagnosis of Brain tumor which is always a tedious task because of human error is indulged. When compared with manual traditional practices, Computer aided mechanisms are compared to obtain better results. In this paper we are presenting the existing models or architectures overview of various researchers who dedicatedly addressed and worked on this tedious task.

In this paper, we propose a lightweight CNN model. Firstly, we standardize the existing CNN model structure based on the minimum computing unit, and second we apply a parameter control solution to solve the problem of parameter redundancy in the model. At last we build a lightweight nonaligned CNN model. The experimental results show that the model parameters can be reduced by more than 50% when the test error is almost the same. Through deep learning, the proposed model is applied to the practical teaching system to achieve the intelligent evaluation effect of the practical teaching process, while improve the quality and efficiency of teaching.

Epilepsy is one of the most common neurological disorders, which is characterized by unpredictable brain seizure. About 30% of the patients are not even aware that they have epilepsy and many have to undergo surgeries to relieve the pain. Therefore, developing a robust brain-computer interface for seizure prediction can help epileptic patients significantly. In this paper, we propose a hybrid CNN-SVM model for better epileptic seizure prediction. A convolutional neural network (CNN) consists of a multilayer structure, which can be adapted and modified according to the requirement of different applications. A support vector machine is a discriminative classifier which can be described by a separating optimal hyperplane used for categorizing new samples. The combination of CNN and SVM is found to provide an effective way for epileptic prediction. Furthermore, the resulting model is made autonomous using edge computing services and is shown to be a viable seizure prediction method. The results can be beneficial in real-life support of epilepsy patients.

Exploration in a known or unknown environment for a mobile robot is an essential application. In the paper, we study the mobile robot obstacle avoidance problem in an indoor environment. We present an end-to-end learning model based Convolutional Neural Network (CNN), which takes the raw image obtained from camera as only input. And the method converts directly the raw pixels to steering commands including turn left, turn right and go straight. Training data was collected by a human remotely controlled mobile robot which was manipulated to explore in a structure environment without colliding into obstacles. Our neural network was trained under caffe framework and specific instructions are executed by the Robot Operating System (ROS). We analysis the effect of the datasets from different environments with some marks on training process and several real-time detect experiments were designed. The final test result shows that the accuracy can be improved by increase the marks in a structured environment and our model can get high accuracy on obstacle avoidance for mobile robots.