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Remote sensing image (RSI) scene classification has become a hot research topic due to its applicability in different domains such as object recognition, land use classification, image retrieval, and surveillance. During RSI classification process, a class label will be allocated to every scene class based on the semantic details, which is signific...
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Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rainfall forecasting now becomes more complex than be...
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... The Figs. 2, 3, 4, 5, 6, 7, [25] illustrate visual comparison of EABC, ABC algorithm and SVM procedure for 2018 year data, Red and white circles are used for highlighting the classification results in the RS data. In Figs. 2, 3, 4, 5, 6, 7, SVM and ABC algorithm, Fallow land and Shrub land were misclassified due to the influence of illumination condition. ...
This study aims to develop an experienced artificial bee colony algorithm which is proposed for classification of crops using remote sensed data. The study was conducted in Devnur village located in Nanajangudu taluk of Mysore district, where a highly differed irrigated agriculture system is present. The study is designed to overcome the issues of misclassification in crop patterns due to similar crop Phenology. In Artificial Bee Colony algorithm a single parameter like waggle dance is used for training and validating the classes particularly in high resolution images, this may lead to misclassification of classes like crops which has a same spectral features. In order to avoid this misclassification, a group of experienced employed bee is been trained and updated by each employed bee through a waggle dance where each employed bee depends on its own experience and the current terrain in each space. Here these features help in identifying the classes and updating of the weights in order to map the agents and identifying the classes and robust the spatial and spectral features. This resultant algorithm is called Experienced Artificial bee colony (EABC). The proposed strategy gives a structure within which a pixel based EABC gives commonly integral data to one another, so characterization process is refined through waggle dance. The findings of the experiment indicate that the suggested approach exhibits a 7% and 6% enhancement in Level 2, and an 8% and 5% improvement in Level 3 categories, respectively, compared to the Artificial Bee Colony (ABC) algorithm and Support Vector Machine (SVM).
... With an exponential production of vehicles across the globe, vehicle classification models serve a crucial role in different areas such as the advancement of ITS in traffic flow control systems, automatic highway toll collection, and perception in self-driving vehicles [6,7]. In early research works, loop and laser induction sensors-related methodologies were suggested for classification of the vehicle types [8]. ...
There is a drastic increase experienced in the production of vehicles in recent years across the globe. In this scenario, vehicle classification system plays a vital part in designing Intelligent Transportation Systems (ITS) for automatic highway toll collection, autonomous driving, and traffic management. Recently, computer vision and pattern recognition models are useful in designing effective vehicle classification systems. But these models are trained using a small number of hand-engineered features derived from small datasets. So, such models cannot be applied for real-time road traffic conditions. Recent developments in Deep Learning (DL)-enabled vehicle classification models are highly helpful in resolving the issues that exist in traditional models. In this background, the current study develops a Lightning Search Algorithm with Deep Transfer Learning-based Vehicle Classification Model for ITS, named LSADTL-VCITS model. The key objective of the presented LSADTL-VCITS model is to automatically detect and classify the types of vehicles. To accomplish this, the presented LSADTL-VCITS model initially employs You Only Look Once (YOLO)-v5 object detector with Capsule Network (CapsNet) as baseline model. In addition, the proposed LSADTL-VCITS model applies LSA with Multilayer Perceptron (MLP) for detection and classification of the vehicles. The performance of the proposed LSADTL-VCITS model was experimentally validated using benchmark dataset and the outcomes were examined under several measures. The experimental outcomes established the superiority of the proposed LSADTL-VCITS model compared to existing approaches.
... For instance, Convolutional Neural Networks (CNNs) [9,10] have been broadly utilized as a classifier whereas feature learning method, namely, Softmax is utilized for categorization. CNN is generally tracked by Recurrent Neural Networks (RNNs) to produce the captions [11,12]. ...
The recent developments in Multimedia Internet of Things (MIoT) devices, empowered with Natural Language Processing (NLP) model, seem to be a promising future of smart devices. It plays an important role in industrial models such as speech understanding, emotion detection, home automation, and so on. If an image needs to be captioned, then the objects in that image, its actions and connections, and any silent feature that remains under-projected or missing from the images should be identified. The aim of the image captioning process is to generate a caption for image. In next step, the image should be provided with one of the most significant and detailed descriptions that is syntactically as well as semantically correct. In this scenario, computer vision model is used to identify the objects and NLP approaches are followed to describe the image. The current study develops a Natural Language Processing with Optimal Deep Learning Enabled Intelligent Image Captioning System (NLPODL-IICS). The aim of the presented NLPODL-IICS model is to produce a proper description for input image. To attain this, the proposed NLPODL-IICS follows two stages such as encoding and decoding processes. Initially, at the encoding side, the proposed NLPODL-IICS model makes use of Hunger Games Search (HGS) with Neural Search Architecture Network (NASNet) model. This model represents the input data appropriately by inserting it into a predefined length vector. Besides, during decoding phase, Chimp Optimization Algorithm (COA) with deeper Long Short Term Memory (LSTM) approach is followed to concatenate the description sentences produced by the method. The application of HGS and COA algorithms helps in accomplishing proper parameter tuning for NASNet and LSTM models respectively. The proposed NLPODL-IICS model was experimentally validated with the help of two benchmark datasets. A widespread comparative analysis confirmed the superior performance of NLPODL-IICS model over other models.
... In particular, hyperspectral information plays a significant role in agricultural surveys and is utilized for agricultural yield estimation, pest monitoring, crop condition monitoring, etc. In agriculture surveys, the fine classifying of HIS provide the data on crop distribution [7]. Fine classifying of crop needs image with higher spectral and spatial resolution. ...
... , N i . The parameter r 1 are an arbitrary variable amongst zero and one that ensures the equity of commuting among the sine and cosine trigonometric functions referred to in Eq. (7). φ 0 i implies the primary angular co-ordinate of wind parcels i that gets arbitrary values amongst [0, 2π]. ...
Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.
... There is a bigger stream of information exists amidst the connected devices and security is one of the key challenges in IoT [4]. To guarantee the security of IoT network and devices associated with it, appropriate privacy requirements should be met at early stages of design, development and deployment of IoT devices [5,6]. Since IoT model is an emerging phenomenon, it still lacks a strong security mechanism or infrastructure which remains a threat to beneficial data. ...
... (4). 6. Check whether the searching agent exceeds the boundary limits of searching region and then regulate it. ...
Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.
... In this big data era, GPU computing power is increasing, and deep learning is gradually proving to be advantageous in many computer vision applications, notably target recognition jobs. Beginning with two-stage networks like R-CNN, Fast R-CNN [9], Mask-R-CNN [10], and Faster R-CNN [11], as well as some excellent algorithms for improving the CNN network model [12][13][14][15], which have the advantages of high detection accuracy and low leakage rate, there has been an explosion of deep learning-based target detection networks since 2014. However, the detection speed is slow and the computation is relatively complicated, making it difficult to be used for the detection and prevention of cybercrime. ...
It is quite simple for foreign objects to attach themselves to transmission line corridors because of the wide variety of laying and the complex, changing environment. If these foreign objects are not found and removed in a timely manner, they can have a significant impact on the transmission lines’ ability to operate safely. Due to the problem of poor accuracy of foreign object identification in transmission line image inspection, we provide an improved YOLOX technique for detection of foreign objects in transmission lines. The method improves the YOLOX target detection network by first using Atrous Spatial Pyramid Pooling to increase sensitivity to foreign objects of different scales, then by embedding Convolutional Block Attention Module to increase model recognition accuracy, and finally by using GIoU loss to further optimize. The testing findings show that the enhanced YOLOX network has a mAP improvement of around 4.24% over the baseline YOLOX network. The target detection SSD, Faster R-CNN, YOLOv5, and YOLOV7 networks have improved less than this. The effectiveness and superiority of the algorithm are proven.
... Singh et al. [49] proposed a multistage particle swarm optimization (MPSO) algorithm to explore the CNN architecture and its hyperparameters (MPSO-CNN), which achieved better performance on 5 benchmark datasets. Hilal et al. [50] studied a remote sensing image classification model (FCMBS-RSIC) based on fuzzy logic and bird swarm algorithm and performed 2 Computational Intelligence and Neuroscience performance verification on benchmark open-access datasets. e FCMBS-RSIC model has enhanced results compared to other state-of-the-art methods. ...
This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.
... The complete image captioning method not only gives information, but it also further reveals the connection among the substances [5]. Image captioning consists of numerous applications-for example, as an aid advanced for guiding the person having visual disabilities while traveling alone [6]. This is made possible by changing the scenario into text and transforming the text into voice messages. ...
Image captioning is a popular topic in the domains of computer vision and natural language processing (NLP). Recent advancements in deep learning (DL) models have enabled the improvement of the overall performance of the image captioning approach. This study develops a metaheuristic optimization with a deep learning-enabled automated image captioning technique (MODLE-AICT). The proposed MODLE-AICT model focuses on the generation of effective captions to the input images by using two processes involving encoding unit and decoding unit. Initially, at the encoding part, the salp swarm algorithm (SSA), with a HybridNet model, is utilized to generate effectual input image representation using fixed-length vectors, showing the novelty of the work. Moreover, the decoding part includes a bidirectional gated recurrent unit (BiGRU) model used to generate descriptive sentences. The inclusion of an SSA-based hyperparameter optimizer helps in attaining effectual performance. For inspecting the enhanced performance of the MODLE-AICT model, a series of simulations were carried out, and the results are examined under several aspects. The experimental values suggested the betterment of the MODLE-AICT model over recent approaches.
... With the advent of ML technology in HCI, machines have become very intelligent. Amongst authors in both the industry and academia whose aim was the development of ubiquitous computing, the most broadly discussed research concept regarding HCI has been human activity recognition (HAR) [5,6]. Recently, the amount of research on HAR has increased rapidly due to the extensive availability of sensors, improvements in power utilization, and reduction in costs, and resulting in technological advances in ML approaches. ...
Human action and activity recognition are clues that alleviate human behavior analysis. Human action recognition (HAR) becomes a significant challenge in various applications involving human computer interaction (HCI) and intellectual video surveillance for enhancing security in distinct fields. Precise action recognition is highly challenging because of the variations in clutter, backgrounds, and viewpoint. The evaluation method depends on the proper extraction and learning of data. The achievement of deep learning (DL) models results in effectual performance in several image-related tasks. In this view, this paper presents a new quantum water strider algorithm with hybrid-deep-learning-based activity recognition (QWSA-HDLAR) model for HCI. The proposed QWSA-HDLAR technique mainly aims to recognize the different types of activities. To recognize activities, the QWSA-HDLAR model employs a deep-transfer-learning-based, neural-architectural-search-network (NASNet)-based feature extractor to generate feature vectors. In addition, the presented QWSA-HDLAR model exploits a QWSA-based hyperparameter tuning process to choose the hyperparameter values of the NASNet model optimally. Finally, the classification of human activities is carried out by the use of a hybrid convolutional neural network with a bidirectional recurrent neural network (HCNN-BiRNN) model. The experimental validation of the QWSA-HDLAR model is tested using two datasets, namely KTH and UCF Sports datasets. The experimental values reported the supremacy of the QWSA-HDLAR model over recent DL approaches.
... The solution of β is obtained from (6) on the basis of KKT condition into (7) if N is smaller and (8) if N is larger. For multi-class classification with m class, all the labels are mapped into a vector of [−l, 1] through length of m; for example, a dataset that is categorized into the 2nd classes from three classes are ...
Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.
