IAES International Journal of Artificial Intelligence (IJ-AI)
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A novel energy efficient data gathering algorithm for wireless sensor networks using artificial intelligence
Get PDFEnergy efficiency is challenging task in wireless sensor network (WSN), it is the main barrier in extending network lifespan. In WSN, maximum energy is wasted during data gathering, hence energy efficient algorithms using artificial intelligence can be designed, that preserves energy while data gathering. Thus, our proposed methodology, A novel energy efficient data gathering algorithm using artificial intelligence for wireless sensor networks (NDGAI), uses novel artificial intelligence algorithms and addresses issue of energy consumption while gathering data. In our proposed work, mobile element is utilized to gather information from sensor nodes in the clusters, formed using amended-expectation-maximization. Each cluster should have a cluster leader and a virtual-point. These cluster leaders are formed utilizing fuzzy logic technique. Virtual-points are formed in the range of cluster leader, only when cluster leader has data. The mobile element reaches virtual point by taking the optimal path, that determined by the hybrid artificial intelligence algorithms, such as artificial-bee-colony (ABC) technique and particle swarm optimization (PSO) algorithms. Thus, by properly performing clustering, cluster leader selection, virtual-point selection and optimal path determination, lead to improved network lifetime and energy saving while gathering the data. Results are simulated and compared with scalable gridbased data gathering algorithm for environmental monitoring wireless sensor networks (SGBDN) and proposed algorithm performs better
Securing the internet of things frontier: a deep learning ensemble for cyber-attack detection in smart environments
Get PDFThis study presents a novel and innovative approach using deep learning (DL) ensemble technique to improve the security of internet of things (IoT) by identifying intricate cyber-attacks. By utilising advanced DL models like deep neural network (DNN) and long short-term memory (LSTM), our approach significantly enhances the accuracy of categorization compared to basic models. The initial binary classifier achieved an accuracy of 85.2%, while the multi-class classifier achieved an accuracy of 79.7%. Both classifiers continually enhanced, achieving accuracies of 99.34% and 98.26%, respectively, after 100 epochs. Real-time scenario evaluations showed that the average execution time per sample record was 0.9439 ms, confirming its efficiency. The DL ensemble exhibited improved performance in comparison to traditional models, indicating its potential for wider implementation in IoT security. The study not only emphasises significant improvements in accuracy, but also emphasises the method’s ability to perform well across many evaluation measures. This study presents a thorough and pragmatic method for identifying cyber-attacks in IoT settings. The stacked ensemble technique outperforms earlier models and fulfils real-time processing requirements, offering substantial advancements in IoT security. These findings enhance both the theoretical comprehension and practical application, establishing a novel benchmark for protecting intelligent IoT systems
Combining convolutional neural networks and spatial-channel “squeeze and excitation” block for multiple-label image classification
Get PDFIn emergency rooms and intensive care units, catheters and tubes are used to keep critically ill patients alive. Appropriate catheter or tube insertion is crucial to avoiding serious complications. Such issues can be rectified if they are identified early. Chest X-rays are commonly used to assess catheter placement. Convolutional neural networks (CNN) have recently been found to enhance multi-label classification tasks on chest X-rays images. Furthermore, attention modules have shown the effect of enhancing spatial encoding on network feature maps. This research analyzed the experiments of each CNN model with different attention blocks. Resnet200D with batch normalization and spatial-channel squeeze and excitation block (BN+scSE) is the best architecture for multiple-label image classification on a chest X-rays dataset from National Institutes of Health Clinical Center (NIH) with multiple catheters and tubes. Then came EfficientNetB5 with BN+scSE and Inception_v3 with spatial squeeze and channel excitation block, respectively
Deep ensemble architectures with heterogeneous approach for an efficient content-based image retrieval
Get PDFIn the field of digital image processing, content-based image retrieval (CBIR) has become essential for searching images based on visual content characteristics like color, shape, and texture, rather than relying on text-based annotations. To address the increasing demands for efficiency and precision in CBIR systems, we introduce the HybridEnsembleNet methodology. HybridEnsembleNet combines deep learning algorithms with an asymmetric retrieval framework to optimize feature extraction and comparison in extensive image databases. This novel approach, specifically custom-made for CBIR, employs a lightweight query structure skilled at handling large-scale data under resource-constrained environments. The experiments were performed on the ROxford and RParis datasets. The deep learning component of HybridEnsembleNet significantly refines the accuracy of image matching and retrieval. RParis The ROxford dataset, specifically in the medium and hard difficulty benchmarks, demonstrates an enhancement of 5.53% and 10.44%, respectively. Similarly, the RParis dataset, under medium and hard benchmarks, exhibits improvements of 3.01% and 5.83%, showcasing superior performance compared to existing models. By overcoming the traditional limitations of CBIR systems in mean average precision (mAP) metrics, HybridEnsembleNet provides a scalable, efficient, and more accurate solution for retrieving relevant images from vast digital libraries
Deep self-taught learning framework for intrusion detection in cloud computing environment
Get PDFCloud has become a target-rich environment for malicious attacks by cyber intruders. Security is a major concern and remains an obstacle to the adoption of cloud computing. The intrusion detection system (IDS) is regarded as defense-in-depth. Unfortunately, most machine learning approaches designed for cloud intrusion detection require large amounts of labeled attack samples, but in real practice, they are limited. Therefore, the key impetus of this work is to introduce self-taught learning (STL) combining stacked sparse autoencoder (SSAE) with long short-term memory (LSTM) as a candidate solution to learn the robust feature representation and efficiently improve the performance of IDS with respect to false alarm rate (FAR) and detection rate (DR). Accordingly, the proposed approach as a first step employs SSAE to achieve dimensional reduction by learning the discriminative features from network traffic. The approach adopts LSTM to recognize the intrusion with the features encoded by SSAE. To evaluate the detective performance of our model, a comprehensive set of experiments are conducted on NSL-KDD. Also, ablation experiments are conducted to show the contribution of each component of our approach. Further, the comparative analysis shows the efficacy of our approach against the existing approaches with an accuracy of 86.31%
Enhanced scene text recognition using deep learning based hybrid attention recognition network
Get PDFThe technique of automatically recognizing and transforming text that is present in pictures or scenes into machine-readable text is known as scene text recognition. It facilitates applications like content extraction, translation, and text analysis in real-world visual data by enabling computers to comprehend and extract textual information from images, videos, or documents. Scene text recognition is essential for many applications, such as language translation and content extraction from photographs. The hybrid attention recognition network (HARN), unique technology presented in this research, is intended to greatly improve efficiency and accuracy of text recognition in complicated scene situations. HARN makes use of cutting-edge elements including alignment-free sequence-to-sequence (AFS) module, creative attention mechanisms, and hybrid architecture that blends attention models with convolutional neural networks (CNNs). Thanks to its novel attention processes, HARN is capable of comprehending wide range of scene text components by capturing both local and global context information. Through faster network convergence, shorter training times, and better utilization of computing resources, the suggested technique raises bar for state-of-the-art. HARN’s versatility makes it a good choice for range of scene text recognition applications, including multilingual text analysis and data extraction. Extensive tests are conducted to assess the effectiveness of HARN approach and demonstrate it is ability to greatly influence real-world applications where accurate and efficient text recognition is essential
Enhancing internet of things security and efficiency through advanced elliptic curve cryptography-based strategies in fog computing
Get PDFFog computing (FC) has evolved as a significant paradigm within the internet of things (IoT) ecosystem, serving as a crucial link between edge devices and centralised cloud computing resources. This research paper investigates advanced methodologies for improving the security and efficiency of FC in the IoT domain. The primary emphasis is placed on the utilisation of elliptic curve cryptography (ECC) to accomplish these goals. This study examines the difficulties encountered in ensuring the security of IoT deployments based on FC. It also presents novel solutions based on ECC to mitigate these obstacles. Moreover, this study investigates techniques for enhancing the efficiency and allocation of resources in IoT applications within a FC environment. This study seeks to offer significant insights into the application of ECC-based techniques for enhancing the security and efficiency of FC in the context of the IoTs. These insights are derived through a combination of theoretical analysis and practical implementations. To evaluate the effectiveness of the proposed system, an analysis is conducted to examine the encryption time, decryption time, and correlation coefficients. These metrics are then compared to those of existing state-of-the-art approaches
Unsupervised hindi word sense disambiguation using graph based centrality measures
Get PDFThe task of word sense disambiguation (WSD) plays a key role in multiple applications of natural language processing. In this paper, we propose a novel unsupervised method for targeted Hindi WSD task. First, we create a weighted graph where the nodes correspond to various synsets of the target word and the neighboring context words. The edges in the graph represent the semantic relations between these synsets in the Hindi WordNet hierarchy. A path-based similarity measure, namely Leacock-Chodorow similarity measure, is used to assign weights to edges. An unsupervised weighted graph-based centrality algorithm is used to identify the correct sense of a target word in a given context. The performance of the proposed algorithm is measured on 20 ambiguous Hindi nouns using four different graph-based centrality measures. We observed a maximum accuracy of 66.92% using PageRank centrality measure which is significantly better than earlier reported graph-based Hindi WSD algorithmsevaluated on the same dataset
Methodology for eliminating plain regions from captured images
Get PDFFinding relevant content and extracting information from images is highly significant. Still, it may be challenging to do so because of changes within the textual contents, such as typefaces, size, line orientation, sophisticated backgrounds in images, and non-uniform illuminations. Despite these challenges, extracting content from captured images is still very important. Proficient textual content image recognition abilities extract text from the images to get over these issues. Despite the availability of several optical character recognition (OCR) techniques, this issue has yet to be resolved. Captured images with text are a rich source of information that should be presented so that viewers may make informed decisions. Because of this, it has become a complicated process to extract the text from an image because the text might be of poor quality, has a variety of fonts and styles, and occasionally have a complicated backdrop, among other things. Several approaches have been tried. However, finding a solution remains challenging. The maximally stable external regions (MSER) approach is developed to identify the text region in a picture. MSER is utilized to elevate the plain regions outside the text and non-text areas using geometric features and stroke width variation qualities
Machine learning-based intrusion detection system for detecting web attacks
Get PDFThe increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve