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2024 International Conference on Information and Communication Technologies for Disaster Management

作者： Saadouni, Rafika Khacha, Amina Gherbi, Chirihane Harbi, Yasmine Aliouat, Zibouda Harous, Saad Ferhat Abbas Univ Setif 1 LRSD Lab Setif Algeria Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates

ISBN: (纸本)9798350367935;9798350367928

In recent years, Wireless Sensor Networks (WSNs) have gained significant attention for their role in forest fire detection, offering early detection and real-time monitoring capabilities. WSNs are tailored to continuously monitor environmental parameters such as temperature, humidity, smoke, and gas concentrations, utilizing various sensors, communication modules, and a central monitoring system. However, their heterogeneous nature and unattended environments render WSNs vulnerable to diverse cyberthreats. Adversaries can exploit these vulnerabilities to manipulate smart devices remotely, posing serious risks to the integrity and resilience of forest ecosystems. Addressing these security concerns is crucial to ensuring uninterrupted monitoring of environmental parameters and effective forest fire detection. To mitigate these risks, we present a robust Intrusion Detection System (IDS) based on a Convolutional Neural Network (CNN) and Genetic algorithm (GA), specifically designed to enhance the security of WSNs deployed for forest fire detection. Our proposed model involves hyperparameters optimization of the CNN model using GA. The experiments conducted on three public datasets, namely NSL-KDD, Edge-IoTset, and CICIoT-2023, demonstrate the effectiveness of our methodology in enhancing security, achieving a high accuracy of 99.26% and a low FPR of 0.00735% using NSL-KDD while achieving 100% accuracy and zero FPR using the two new realistic datasets Edge-IIoTset and CICIoT-2023.

关键词： Wireless Sensor Networks (WSNs) Intrusion Detection System (IDS) Deep Learning bio-inspired algorithm Genetic algorithm (GA) NSL-KDD Edge-IIoTset CICIoT-2023 Hyperparameters Optimization (HPO)

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Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

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COMPUTERS IN bioLOGY AND MEDICINE 2020年 124卷 103940-103940页

作者： Isaac, Anisha Nehemiah, H. Khanna Isaac, Anubha Kannan, A. Anna Univ Ramanujan Comp Ctr Chennai 600025 Tamil Nadu India Anna Univ Dept Comp Sci & Engn Chennai 600025 Tamil Nadu India Anna Univ Dept Informat Sci & Technol Chennai 600025 Tamil Nadu India

Pulmonary emphysema is a condition characterized by the destruction and permanent enlargement of the alveoli of the lungs. The destruction of gas-exchanging alveoli causes shortness of breath followed by a chronic cough and sputum production. A Computer-Aided Diagnosis (CAD) framework for diagnosing pulmonary emphysema from chest Computed Tomography (CT) slices has been designed and implemented in this study. The process of implementing the CAD framework includes segmenting the lung tissues and extracting the regions of interest (ROIs) using the Spatial Intuitionistic Fuzzy C-Means clustering algorithm. The ROIs that were considered in this work were emphysematous lesions - namely, centrilobular, paraseptal, and bullae that were labelled by an expert radiologist. The shape, texture, and run-length features were extracted from each ROI. A wrapper approach that employed four bio-inspired algorithms - namely, Moth-Flame Optimization (MFO), Firefly Optimization (FFO), Artificial Bee Colony Optimization, and Ant Colony Optimization - with the accuracy of the support vector machine classifier as the fitness function was used to select the optimal feature subset. The selected features of each bio-inspired algorithm were trained independently using the Extreme Learning Machine classifier based on the tenfold cross-validation technique. The framework was tested on real-time and public emphysema datasets to perform binary classification of lung CT slices of patients with and without the presence of emphysema. The framework that used MFO and FFO for feature selection produced superior results regarding accuracy, precision, recall, and specificity for the real-time dataset and the public dataset, respectively, when compared to the other bio-inspired algorithms.

关键词： Emphysema Segmentation Spatial Intuitionistic Fuzzy C-Means (SIFCM) bio-inspired algorithm Moth-Flame Optimization (MFO) Feature selection

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Improved salp swarm algorithm combined with chaos

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年 202卷 113-148页

作者： Tawhid, Mohamed A. Ibrahim, Abdelmonem M. Thompson Rivers Univ Fac Sci Dept Math & Stat Kamloops BC V2C 0C8 Canada Al Azhar Univ Fac Sci Dept Math Assiut Branch Assiut Egypt

A recently developed metaheuristic optimization algorithm, Salp Swarm algorithm (SSA), has manifested its capability in solving various optimization problems and many real-life applications. SSA is based on salps' swarming behaviour when finding their way and searching for food in the oceans. Nonetheless, like most metaheuristic algorithms, SSA experiences low convergence and stagnation in local optima and rate. There is a need to enhance SSA to speed its convergence and effectiveness to solve complex problems. In the present study, we will introduce chaos into SSA (CSSA) to increase its global search mobility for robust global optimization. Detailed studies are carried out on real-world nonlinear benchmark systems and CEC 2013 benchmark functions with chaotic map (Tent). Here, the algorithm utilizes a Tent map to tune the salp leaders' attractive movement around food sources. The experimental results, considering both convergence and accuracy simultaneously, demonstrate the effectiveness of CSSA for 12 nonlinear systems and 28 unconstrained optimization problems CEC 2013. Two nonparametric statistical tests, the Friedman test and Wilcoxon Signed-Rank Test, are conducted to show the superiority of CSCA over other states of the art algorithms and our results' significance. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： bio-inspired algorithm Salp Swarm algorithm Metaheuristics Chaos theory Nonlinear system of equations

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FOA: fireworks optimization algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第23期81卷 33151-33170页

作者： Ehsaeyan, Ehsan Zolghadrasli, Alireza Sirjan Univ Technol Elect Engn Dept Sirjan Iran Shiraz Univ Dept Commun & Elect Engn Shiraz Iran

In this paper, a novel meta-heuristic algorithm called Fireworks Optimization algorithm (FOA) is introduced with few control parameters for discrete and continuous optimization problems. This algorithm is inspired from explosion pyrotechnic devices producing colorful spikes like red, blue and silver. By modelling the explosion behavior of the Fireworks in the sky, the search space can be swept efficiently to find the global optima. To improve the balance between the exploration and exploitation of individuals, three categories are defined to avoid local optimal traps and applied to the search agents. Each category has a different task and predefined updating position rules. A grouping strategy is considered to prevent the algorithm from premature convergence. The performance of FOA is demonstrated over 15 standard benchmarks in the continuous version and 30 images thresholding problems in the discrete version. The obtained results reveal the superiority of the proposed algorithm with fewer input parameters over other state-of-the-art optimization methods in most cases.

关键词： Fireworks optimization algorithm bio-inspired algorithm Benchmarked functions meta-heuristic technique Image segmentation Engineering applications

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Integrated Deep Auto-Encoder and Q-Learning-Based Scheme to Detect Anomalies and Supporting Forensics in Cloud Computing Environments

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期127卷 2247-2265页

作者： Savaridassan, P. Maragatham, G. SRM Inst Sci & Technol Sch Comp Dept Informat Technol Chennai Tamil Nadu India

The cloud computing environment when deployed correctly is responsible for delivering scalability, cost efficiency, reliability, security and interoperability to the end users. Log analysis is considered to be an indispensable component of security regulations and framework, since these computer-generated records help the organizations, businesses and networks to respond to different kinds of risks that are possible to cloud environment in a reactive and proactive manner. In this paper, an Integrated Deep Auto-Encoder and Q-learning-based Deep Learning (IDEA-QLDL) Scheme is proposed for attaining maximum prediction accuracy during the process of exploring log data and classifying them into genuine and anomalous. It initiates the process of acceptance or denial based on the continuous investigation of behavioral patterns that are highly applicable for classification. The results of the proposed IDEA-QLDL Scheme confirmed its predominance in improving the classification accuracy, precision, recall and detection time compared to the benchmarked schemes considered for investigation.

关键词： bio-inspired algorithm Q-Learning Deep auto-encoder Anomaly detection

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Novel yellow saddle goatfish algorithm for improving performance and efficiency of PV system under partial shading conditions

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SOLAR ENERGY 2022年 247卷 295-307页

作者： Azli, Hadjer Titri, Sabrina Larbes, Cherif Kaced, Karim Femmam, Karima Ecole Natl Polytech Dept Elect El Harrach Algeria Ctr Dev Technol Avancees CDTA Baba Hassen Alger Algeria Mohamed Khider Univ Dept Math Biskra Algeria

This paper proposes a novel maximum power point tracking (MPPT) technique based on a new optimization algorithm called yellow saddle goatfish algorithm (YSGA). The photovoltaic system loses a significant amount of power under partial shading conditions (PSC). Because of the presence of bypass diodes, several maximums appear in the Power-Voltage characteristic, making tracking the global maximum power point (GMPP) extremely difficult. A yellow saddle goatfish-based MPPT algorithm is proposed and implemented to address this issue and improve the efficiency of the PV system. This algorithm employs a subgroup research methodology and a very interesting strategy of particle collaboration during GMPP tracking, providing a balance between the exploration and exploitation phases of the research process. To investigate its performance, tests were conducted using six different partial shading patterns and a fast irradiance variation. The simulation was carried out in MATLAB/Simulink using a series of MSX60 solar panels. The results were compared to well-known and efficient algorithms such as the Grey wolf optimization algorithm (GWO), the Bat algorithm (BAT), particle swarm optimization (PSO), the Seagull optimization algorithm (SOA), and the Perturb and observe (P&O) algorithm. The findings demonstrate the proposed method's high robustness, as well as its high dynamic and accuracy in tracking the GMPP. Furthermore, a quantitative and statistical analysis of 100 tests revealed that it outperformed the other algorithms in terms of efficiency, success rate, convergence time, and solution dispersion.

关键词： Yellow saddle goatfish algorithm (YSGA) Maximum power point tracking (MPPT) Photovoltaic (PV) system Partial shading condition (PSC) bio-inspired algorithm

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A Survey on Deep Learning Models Embed bio-inspired algorithms in Cardiac Disease Classification

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Open biomedical Engineering Journal 2023年 17卷

作者： Pandiyan, Nandakumar Narayan, Subhashini School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Deep learning is a sub-field of machine learning that emerged as a noticeable model in the world, specifically for the disease classification field. This work aims to review the state-of-the-art deep learning models in Cardiac Disease prediction by examining several research papers. In this study, popular datasets listed and analyzed in the prediction process of cardiac disease with their performance using various deep learning techniques are presented. This review emphasizes the latest advancement in the six deep learning models, namely, deep neural networks, convolutional neural networks, recurrent neural networks, extreme learning machines, deep belief networks, and transfer learning with its applications. The important features of cardiac disease used by five different countries have been listed that guide researchers to analyze it for future purposes. Freshly, deep learning models have yielded an extended performance in cardiac disease detection that shows its rapid growth. Specifically, deep learning effectiveness concerted with the bio-inspired algorithms is reviewed. This paper also presents what major applications of deep learning techniques have been grasped in the past decade. © 2023 Pandiyan and Narayan.

关键词： algorithms bio-inspired algorithm Cardiac arrests Cardiac disease Deep learning Optimization

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Seasons optimization algorithm

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1845-1865页

作者： Emami, Hojjat Univ Bonab Bonab Iran

This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as aforest. Each individual in the forest is referred to as atree. Until the termination conditions are satisfied, the trees in the forest are updated to a new generation by applying four operators similar to the trees' life cycles in nature:renew,competition,seeding, andresistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The effectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and finding the global optimum on most benchmark functions.

关键词： Trees' growth cycle Optimization Meta-heuristic bio-inspired algorithm Seasons optimization (SO)

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Threat Hunting in Internet of Things Networks with bio-inspired Models

Threat Hunting in Internet of Things Networks with Bio-Inspi...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Lim, Jia Mei Grace Olayinka, Olakunle University of Sheffield Department of Computer Science Sheffield United Kingdom

ISBN: (纸本)9798350324457

Cyber threat hunting is time-consuming as large data quantities are analysed to hunt down attacks that have evaded existing security measures. With an increasing number of Internet of Things (IoT) devices, the subsequently increasing data quantities make finding attacks through traditional approaches impossible. Existing research has highlighted the use of signature-based, visualisation, analytical, and Artificial Intelligence (AI) approaches to threat detection and anomaly detection. This paper contributes to the research on threat hunting using AI and presents novel models for threat hunting in IoT networks. The proposed models effectively demonstrate the importance of preprocessing approaches and model adaptability while challenging the need for overly complicated models. © 2023 IEEE.

关键词： bio-inspired algorithm Internet of Things (IoT) Message Queuing Telemetry Transport (MQTT) Protocol multilayer perceptron reinforcement learning Threat hunting

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Whale optimization-orchestrated Federated Learning-based resource allocation scheme for D2D communication

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AD HOC NETWORKS 2024年 163卷

作者： Jadav, Nilesh Kumar Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India

Device -to -Device (D2D) communication plays a prominent role in mobile data offloading from the cellular infrastructure (e.g., base station). This paradigm empowers user equipment to communicate with each other directly, offering an efficient resort for data communication that eliminates the need for the base station. However, significant challenges, such as interference, resource allocation, and energy efficiency, impede the performance of D2D communication. In the context of resource allocation, most of the existing work primarily focuses on game and graph theoretical models, which raises the computational complexity as the number of D2D users increases. In this article, we formulated a sum rate maximization problem, which is solved using a combinatorial scheme comprised of Whale Optimization algorithm (WOA) and Federated Learning (FL). First, we discover the optimal CUs-D2D Groups (D2DGs) pairs by utilizing the social behavior of whales in the WOA. Only these optimal links are permitted to participate in the FL -based resource allocation, ensuring a physical layer access control. Next, we generated a dataset from the WOA-based optimal CU-D2DG links, which is employed by the Convolutional Neural Network (CNN) model for decentralized learning. FL offers a proactive decision for resource assignment, i.e., whose CU resources will be used by the D2DG. The proposed scheme is evaluated by considering different performance parameters, such as convergence rate, statistical measure (accuracy, loss), fairness (0.72), and overall sum rate ( approximate to 25 Mbps).

关键词： Resource allocation Physical layer access control D2D communication Federated Learning Whale Optimization algorithm bio-inspired algorithm

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