Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM’s training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU)

Improved Reptile Search Optimization Algorithm using Chaotic map and Simulated Annealing for Feature Selection in Medical Filed

The increased volume of medical datasets has produced high dimensional features, negatively affecting machine learning (ML) classifiers. In ML, the feature selection process is fundamental for selecting the most relevant features and reducing redundant and irrelevant ones. The optimization algorithms demonstrate its capability to solve feature selection problems. Reptile Search Algorithm (RSA) is a new nature-inspired optimization algorithm that stimulates Crocodiles’ encircling and hunting behavior. The unique search of the RSA algorithm obtains promising results compared to other optimization algorithms. However, when applied to high-dimensional feature selection problems, RSA suffers from population diversity and local optima limitations. An improved metaheuristic optimizer, namely the Improved Reptile Search Algorithm (IRSA), is proposed to overcome these limitations and adapt the RSA to solve the feature selection problem. Two main improvements adding value to the standard RSA; the first improvement is to apply the chaos theory at the initialization phase of RSA to enhance its exploration capabilities in the search space. The second improvement is to combine the Simulated Annealing (SA) algorithm with the exploitation search to avoid the local optima problem. The IRSA performance was evaluated over 20 medical benchmark datasets from the UCI machine learning repository. Also, IRSA is compared with the standard RSA and state-of-the-art optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grasshopper Optimization algorithm (GOA) and Slime Mould Optimization (SMO). The evaluation metrics include the number of selected features, classification accuracy, fitness value, Wilcoxon statistical test (p-value), and convergence curve. Based on the results obtained, IRSA confirmed its superiority over the original RSA algorithm and other optimized algorithms on the majority of the medical datasets

Stacking and chaining of normalization methods in deep learning-based classification of colorectal cancer using gut microbiome data

Machine learning (ML)-based detection of diseases using sequence-based gut microbiome data has been of great interest within the artificial intelligence in medicine (AIM) community. The approach offers a non-invasive alternative for colorectal cancer detection, which is based on stool samples. Considering limitations of existing methods in CRC detection, medical research has shown interest in the use of high throughput data to identify the disease. Owing to several limitations of conventional ML algorithms, deep learning (DL) methods are becoming more popular due to their outstanding performance in related fields. However, the performance of DL methods is affected by limitations such as dimensionality, sparsity, and feature dominance inherent in microbiome data. This research proposes stacking and chaining of normalization methods to address the limitations. While the stacking technique offers a robust, easy to use, and interpretable alternative for augmenting microbiome and other tabular data, the chaining technique is an alternative to data normalization that dynamically adjusts the underlying properties of data towards the normal distribution. The proposed techniques are combined with rank transformation and feature selection to further improve the performance of the model, with area under the curve (AUC) values between 0.857 to 0.987 using publicly available datasets

Correction to “Stacking and Chaining of Normalization Methods in Deep Learning-Based Classification of Colorectal Cancer Using Gut Microbiome Data”

In the above article [1], the acknowledgment for the research grant was referencing an incorrect grant reference number

Review of Vision-Based Deep Learning Parking Slot Detection on Surround View Images

Autonomous vehicles are gaining popularity, and the development of automatic parking systems is a fundamental requirement. Detecting the parking slots accurately is the first step towards achieving an automatic parking system. However, modern parking slots present various challenges for detection task due to their different shapes, colors, functionalities, and the influence of factors like lighting and obstacles. In this comprehensive review paper, we explore the realm of vision-based deep learning methods for parking slot detection. We categorize these methods into four main categories: object detection, image segmentation, regression, and graph neural network, and provide detailed explanations and insights into the unique features and strengths of each category. Additionally, we analyze the performance of these methods using three widely used datasets: the Tongji Parking-slot Dataset 2.0 (ps 2.0), Sejong National University (SNU) dataset, and panoramic surround view (PSV) dataset, which have played a crucial role in assessing advancements in parking slot detection. Finally, we summarize the findings of each method and outline future research directions in this field

Infrastructure based spectrum sensing scheme in VANET using reinforcement learning

Spectrum sensing is one of the fundamental functionality performed by a cognitive radio to identify vacant radio spectrum for dynamic spectrum access (DSA). However, there are many challenges still existing before the benefits of DSA can be realized. The challenges include multipath fading, shadowing and hidden primary user (PU) problem. The challenges are more severe in vehicular communication due to unique characteristics such as dynamic topology caused by vehicle mobility. Furthermore, spectrum sensing is dependent on the activities of the PU traffic pattern which are not known in advance. In a typical cognitive radio network, the PU plays a passive role. Therefore, a sensing technique should account for traffic pattern of the PU autonomously. However, most of the proposed spectrum sensing schemes in vehicular communication assumes a static ON/OFF PU model which does not realistically model the PU traffic pattern. In this paper, we propose reinforcement learning (RL) to model the traffic pattern of the PU and use the model to predict channels likely to be free in future. The RL is implemented on road side unit (RSU) which send predicted vacant PU channels to vehicles on the road. Before the channels can be used, vehicles perform spectrum sensing. To account for multipath fading and shadowing, adaptive spectrum sensing is proposed. The results from spectrum sensing, sensing time and PU channel capacity are calculated into a scalar value and used as reward for RL at RSU. The RSU continuously update the reward for channels of interest using sensing history from passing vehicles as reward. Compared to history based schemes from literature, the RL technique proposed in this paper performs better

HMM-based Arabic handwritten word recognition via zone segmentation

This paper presents a novel approach towards Arabic handwritten word recognition using the zone-wise material. Due to complex nature of the Arabic characters involving issues of overlapping and related issues like touching, the segmentation and recognition is a monotonous main occupation of in Arabic cursive (e.g. Naskha, Riqaa and other comparable scripts written for Holy Quran). To solve the issues of this character segmentation in such cursive, HMM founded on sequence modelling relying on the holistic way. This paper proposes an efficient framework word recognition by segmenting the handwritten word features horizontally into three zones (upper, middle and lower) and then recognise the corresponding zones. The aim of this zone is to minimise the quantity of distinct component classes associated to the total a number of classes in Arabic cursive. As an outcome of this proposed approach is to enhance the recognition performance of the system. The elements of segmentation zone especially in middle zone (baseline), where characters are frequently tender, are recognised using HMM. After the recognition of middle zone, HMM Based in Viterbi forced Alignment is performed to mark the right and left characters in conjoint zones. Next, the residue components, if any, in upper and lower zones are highlighted in a character boundary then the Components are joint with the morphology of the character to achieve the whole word level recognition. Water reservoir- created the main properties that had integrated into the framework to increase the performance of the zone segmentation especially for the upper zone for the character to determine the boundary detection imperfections in segmentation stage. A new sliding window-based feature, named hierarchical Histogram OF-Oriented Gradient (PHOG) is suggested for lower and upper zone recognition. The comparison study with other similar PHOG features and found robust for Arabic handwriting script recognition. An exhaustive experiment is performed of other handwriting using different dataset such IFN / IFNT to evaluate the rate and the recognition performance. The outcome of this experiment, it has been renowned that proposed zone-wise recognition increases accuracy

EEG-based neural networks approaches for fatigue and drowsiness detection: A survey

Drowsiness is a state of fatigue or sleepiness characterized by a strong urge to sleep. It is correlated with a progressive decline in response time, compromised processing of available information, more errors in short-term memory, and reduced vigilance behaviors. The electroencephalogram (EEG), a recording of the brain’s electrical activities, has demonstrated the most robust association with drowsiness. As a result, EEG is widely recognized as a dependable indicator for evaluating drowsiness, fatigue, and performance levels. In this survey paper, we thoroughly investigate the application of shallow and deep neural network approaches utilizing EEG signals for the detection of fatigue and drowsiness. As far as our knowledge extends, this is the pioneering survey paper dedicated to exploring this specific research domain. The paper presents a comprehensive overview of the diverse EEG features utilized in the detection of fatigue and drowsiness, the different types of neural networks, and the reported performance of these methods in the literature. Additionally, the paper thoroughly examines the challenges and limitations associated with EEG-based fatigue and drowsiness detection and highlights directions for future research. The survey aims to offer a comprehensive overview of the existing methods in EEG-based fatigue and drowsiness detection, serving as a valuable resource for researchers and practitioners working in the respective field

Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets

The rapid growth in biomedical datasets has generated high dimensionality features that negatively impact machine learning classifiers. In machine learning, feature selection (FS) is an essential process for selecting the most significant features and reducing redundant and irrelevant features. In this study, an equilibrium optimization algorithm (EOA) is used to minimize the selected features from high-dimensional medical datasets. EOA is a novel metaheuristic physics-based algorithm and newly proposed to deal with unimodal, multi-modal, and engineering problems. EOA is considered as one of the most powerful, fast, and best performing population-based optimization algorithms. However, EOA suffers from local optima and population diversity when dealing with high dimensionality features, such as in biomedical datasets. In order to overcome these limitations and adapt EOA to solve feature selection problems, a novel metaheuristic optimizer, the so-called improved equilibrium optimization algorithm (IEOA), is proposed. Two main improvements are included in the IEOA: The first improvement is applying elite opposite-based learning (EOBL) to improve population diversity. The second improvement is integrating three novel local search strategies to prevent it from becoming stuck in local optima. The local search strategies applied to enhance local search capabilities depend on three approaches: mutation search, mutation–neighborhood search, and a backup strategy. The IEOA has enhanced the population diversity, classification accuracy, and selected features, and increased the convergence speed rate. To evaluate the performance of IEOA, we conducted experiments on 21 biomedical benchmark datasets gathered from the UCI repository. Four standard metrics were used to test and evaluate IEOA’s performance: the number of selected features, classification accuracy, fitness value, and p-value statistical test. Moreover, the proposed IEOA was compared with the original EOA and other well-known optimization algorithms. Based on the experimental results, IEOA confirmed its better performance in comparison to the original EOA and the other optimization algorithms, for the majority of the used datasets

A Joint Evaluation of Energy-Efficient Downlink Scheduling and Partial CQI Feedback for LTE Video Transmission

A green cellular technology is proposed to optimize the energy and spectrum resources. Such optimization will require perfect channel state information at the transmitting base station. However, reporting the channel status of the entire bandwidth requires huge undesirable feedback overhead. Therefore, the aim of this paper is to optimize the energy and bandwidth resources while maintaining quality-of-service at the downlink when a partial feedback is considered. In this paper, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB to optimize the energy and spectrum resources. On the user equipment side, a partial channel feedback scheme based on an adaptive feedback threshold is developed. A primary concern of this feedback scheme is to reduce the uplink signaling overhead without a substantial loss in downlink performances. Finally, the downlink packet scheduling and the partial feedback are jointly evaluated to further enhance the system performance. Based on a system-level simulation results, the proposed energy-efficient scheduling with partial feedback has achieved an improvement in EE of up to 79% compared to the PPM scheduler. Besides, it minimizes the degradation caused by the partial channel quality indicator feedback. Thus, the proposed two-sided algorithm gives the best tradeoff between uplink and downlink performances