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Design of a uniplanar printed triple band-rejected ultra-wideband antenna using particle swarm optimisation and the firefly algorithm

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第1期10卷 31-37页

作者： Mohammed, Husham J. Abdullah, Abdulkareem S. Ali, Ramzy S. Abd-Alhameed, Raed A. Abdulraheem, Yasir I. Noras, James M. Univ Basrah Dept Elect Engn Basrah Iraq Univ Bradford Sch Engn & Informat Bradford BD7 1DP W Yorkshire England

A compact planar monopole antenna is proposed for ultra-wideband applications. The antenna has a microstrip line feed and band-rejected characteristics and consists of a ring patch and a partial ground plane with a defective ground structure of rectangular shape. An annular strip is etched above the radiating element and two slots, one C-shaped and one arc-shaped, are embedded in the radiating patch. The proposed antenna has been optimised using bio-inspired algorithms such as the particle swarm optimisation and the firefly algorithm, based on a new software algorithm (Antenna Optimizer). Multi-objective optimisation achieves rejection bands at 3.3-3.7 GHz for Worldwide Interoperability for Microwave Access, 5.15-5.825 GHz for the 802.11a wireless local area network system or HIPERLAN/2, and 7.25-7.745 GHz for C-band satellite communication systems. Validated results show wideband performance from 2.7 to 10.6 GHz with S-11 < -10 dB. The antenna has compact dimensions of 28 x 30 mm(2). The radiation pattern is comparatively stable across the operating band with a relatively stable gain except in the notched bands.

关键词： microstrip antennas ultra wideband antennas monopole antennas microstrip lines defected ground structures particle swarm optimisation wireless LAN WiMax antenna radiation patterns satellite antennas multifrequency antennas band-stop filters microwave antennas compact planar monopole antenna microstrip line feed ring patch partial ground plane defective ground structure rectangular shape radiating element bio-inspired algorithm firefly algorithm Worldwide Interoperability for Microwave Access wireless local area network system HIPERLAN C-band satellite communication systems antenna radiation pattern uniplanar printed triple band rejected ultra wideband antenna particle swarm optimisation IEEE 802 11a frequency 3 3 GHz to 3 7 GHz frequency 5 15 GHz to 5 825 GHz frequency 7 25 GHz to 7 745 GHz frequency 2 7 GHz to 10 6 GHz

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A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data

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JOURNAL OF SUPERCOMPUTING 2020年第8期76卷 5679-5693页

作者： Edwin Dhas, P. Sankara Gomathi, B. Jayaraj Annapackiam CSI Coll Engn Dept Comp Sci & Engn Nazareth India Natl Engn Coll Dept Elect & Instrumentat Engn Kovilpatti India

The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the data into organized groups. Grouping similar gene expressions paves the way for effective analysis, and the relationship between the expressions can be figured out. Recognizing the benefits of clustering, this work intends to present a clustering algorithm by combining generalized hierarchical fuzzy C means (GHFCM) and grey wolf optimization (GWO) algorithms. The GWO algorithm is utilized for selecting the initial clustering point, and the GHFCM algorithm is employed for clustering the microarray gene data. The performance of the proposed clustering algorithm is tested with respect to precision, recall,F-measure and time consumption, and the results are compared with the existing approaches. The performance of the proposed work is satisfactory with betterF-measure rates and minimal time consumption.

关键词： Microarray gene data bio-inspired algorithm Clustering algorithm

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Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2024年第1期12卷 35页

作者： Alshardan, Amal Saeed, Muhammad Kashif Alotaibi, Shoayee Dlaim Alashjaee, Abdullah M. Salih, Nahla Marzouk, Radwa Princess Nourah Bint Abdulrahman Univ PNU Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Dept Comp Sci Abha Saudi Arabia Univ Hail Coll Comp Sci & Engn Dept Artificial Intelligence & Data Sci Hail Saudi Arabia Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Rafha 91911 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Dept Comp Sci Appl Coll Dammam Saudi Arabia

Gastrointestinal (GI) cancer detection includes the detection of cancerous or potentially cancerous lesions within the GI tract. Earlier diagnosis is critical for increasing the success of treatment and improving patient outcomes. Medical imaging plays a major role in diagnosing and detecting GI cancer. CT scans, endoscopy, MRI, ultrasound, and positron emission tomography (PET) scans can help detect lesions, abnormal masses, and changes in tissue structure. Artificial intelligence (AI) and machine learning (ML) methods are being gradually applied to medical imaging for cancer diagnosis. ML algorithms, including deep learning methodologies like convolutional neural network (CNN), are applied frequently for cancer diagnosis. These models learn features and patterns from labelled datasets to discriminate between normal and abnormal areas in medical images. This article presents a new Harbor Seal Whiskers Optimization algorithm with Deep Learning based Medical Imaging Analysis for Gastrointestinal Cancer Detection (HSWOA-DLGCD) technique. The goal of the HSWOA-DLGCD algorithm is to explore the GI images for the cancer diagnosis. In order to accomplish this, the HSWOA-DLGCD system applies bilateral filtering (BF) approach for the removal of noise. In addition, the HSWOA-DLGCD technique makes use of HSWOA with Xception model for feature extraction. For cancer recognition, the HSWOA-DLGCD technique applies extreme gradient boosting (XGBoost) model. Finally, the parameters compared with the XGBoost system can be selected by moth flame optimization (MFO) system. The experimental results of the HSWOA-DLGCD technique could be verified on the Kvasir database. The simulation outcome demonstrated a best possible solution of the HSWOA-DLGCD method than other recent methods.

关键词： Medical image analysis Gastrointestinal cancer Computer-aided diagnosis bio-inspired algorithm Artificial intelligence

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Recent trends in intelligent transportation systems using big data analysis

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2023年第1期72卷 29-49页

作者： Qurashi, Mahvash Iftikhar Ali, Zain Anwar Shafiq, Muhammad Lodhi, Arsalan Khan Sir Syed Univ Engn & Technol Comp Engn Dept Karachi Sindh Pakistan JiaYing Univ Sch Phys & Elect Engn Meizhou Peoples R China Sir Syed Univ Engn & Technol Elect Engn Dept Karachi Sindh Pakistan

Internet of Things (IoT) is the core concept for smart cities. The foremost functionality of IoT is to access the data collected from the shared devices over the communication technologies from the sensors. The data from the various sources are mostly heterogeneous. Big data organises the data and brings efficiency by increasing performance. Big data works on 3Vs (Volume, Velocity, Variety). The massive generation of data refers to Volume, the speediness of data generation refers to Velocity and the generation of data in either structured or unstructured format is Variety. The Intelligent Transportation System is an advanced technology that provides state-of-the-art facilities for different traffic management modes and keeps the users well informed about the safe journey. In this research the recent applications of ITS with different trends like data pre-processing, artificial intelligent algorithms, computer vision, bio-inspired algorithms and future works are discussed.

关键词： smart city intelligent transportation system AI algorithm bio-inspired algorithm computer vision big data analytics

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Controlling Musculoskeletal System with Spinal Cord-inspired Constraint Force Field Primitives

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UNMANNED SYSTEMS 2024年第2期12卷 293-304页

作者： Zhong, Shanlin Wu, Yaxiong Wu, Wei Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China

Organisms naturally have extraordinary motor ability. They can autonomously control the highly redundant and nonlinear musculoskeletal system to complete fast and flexible movements. Motor primitive theory proposed that a complex movement can be realized by modular organization of simple movement patterns. This theory provides a convincing theoretical basis for explaining the extraordinary motor ability of organisms, and has been supported by plenty of experimental evidences in neurosciences. As a kind of typical motor primitive related to the modular regulation of the spinal motor system, convergent force field was thought to be significant for reducing the complexity of controlling numerous muscles and joints simultaneously. inspired by the primitive property of convergent force field, in this paper, we proposed a new algorithm to efficiently construct constraint force field on musculoskeletal system with highly redundant actuators by taking optimized parameters of constraint force field as motor primitives. Compared with previous methods of constructing constraint force field, the proposed algorithm is able to reduce the dimension of the solution space so as to effectively improve the computational efficiency of constructing constraint force field in musculoskeletal system with redundant muscles. Validation experiments were carried out in a musculoskeletal system with 10 redundant muscles. The number of optimization iterations and computational time required for constructing constraint force field at a new position were significantly reduced. The system can accurately reach the target position using constant activations. This work may bring in new inspiration for realizing efficient and accurate motion control of musculoskeletal robots with limited feedback accuracy.

关键词： Musculoskeletal robot motor primitive bio-inspired algorithm constraint force field high-precision movement

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Dual-filtering (DF) schemes for learning systems to prevent adversarial attacks

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COMPLEX & INTELLIGENT SYSTEMS 2023年第4期9卷 3717-3738页

作者： Dasgupta, Dipankar Gupta, Kishor Datta Univ Memphis Memphis TN 38152 USA

Defenses against adversarial attacks are essential to ensure the reliability of machine-learning models as their applications are expanding in different domains. Existing ML defense techniques have several limitations in practical use. We proposed a trustworthy framework that employs an adaptive strategy to inspect both inputs and decisions. In particular, data streams are examined by a series of diverse filters before sending to the learning system and then crossed checked its output through anomaly (outlier) detectors before making the final decision. Experimental results (using benchmark data-sets) demonstrated that our dual-filtering strategy could mitigate adaptive or advanced adversarial manipulations for wide-range of ML attacks with higher accuracy. Moreover, the output decision boundary inspection with a classification technique automatically affirms the reliability and increases the trustworthiness of any ML-based decision support system. Unlike other defense techniques, our dual-filtering strategy does not require adversarial sample generation and updating the decision boundary for detection, makes the ML defense robust to adaptive attacks.

关键词： Adversarial machine learning Computer security bio-inspired algorithm Negative selection algorithm

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Improved African buffalo optimisation algorithm for petroleum product supply chain management

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INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2020年第6期11卷 769-779页

作者： Igiri, Chinwe Peace Singh, Yudhveer Bhargava, Deepshikha Shikaa, Samuel Amity Univ Jaipur Rajasthan India Univ Petr & Energy Studies Dehra Dun Uttarakhand India Taraba State Univ Jalingo Nigeria

Real-world supply chain network is complex due to large problem size and constraints. An optimum petroleum products scheduling would not only influence the distribution cost but also result in optimal product scheduling. The bio-inspired method is preferred alternative to exact algorithms because it does not require prior knowledge of the initial solution unlike the latter. The study proposes an improved African Buffalo Optimisation (ABO) algorithm for petroleum supply chain distribution. The ABO is a swarm intelligence-based bio-inspired algorithm with significant performance track record. It models the grazing and defending lifestyle of the African buffaloes in the savannah. The chaotic ABO and chaotic-Levy ABO are the ABO's improved variants with outstanding performance in recent studies. The present study applies the standard ABO and its improved variants to obtain a near optimum petroleum distribution scheduling solution. The comparative result shows that the proposed approach outperformed existing exact algorithms.

关键词： supply chain network computational intelligence petroleum product scheduling bio-inspired algorithm swarm intelligence African buffalo optimisation algorithm chaotic African buffalo optimisation algorithm chaotic-Levy flight African buffalo optimisation algorithm

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bio-inspired for Features Optimization and Malware Detection

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 6963-6979页

作者： Ab Razak, Mohd Faizal Anuar, Nor Badrul Othman, Fazidah Firdaus, Ahmad Afifi, Firdaus Salleh, Rosli Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia

The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. This technique shows that the use of Android permissions is a potential feature for malware detection. The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. In recognizing the Android malware, it appears that AdaBoost is able to achieve good detection accuracy with a true positive rate value of 95.6%, using Android permissions. The results show that particle swarm optimization (PSO) is the best feature optimization approach for selecting features.

关键词： Android Mobile devices bio-inspired algorithm Features optimization Machine learning

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Division of labor in a swarm of autonomous underwater robots by improved partitioning social inhibition

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ADAPTIVE BEHAVIOR 2016年第2期24卷 87-101页

作者： Zahadat, Payam Schmickl, Thomas Graz Univ A-8010 Graz Austria

In this paper, a distributed algorithm for adaptive task allocation and adaptable partitioning of a swarm into different work-groups is proposed and used in a swarm of underwater robots. The algorithm is based on local interactions of agents and is inspired by honeybee age-polyethism. It is adaptive to changes in the swarm size (workforce) and relative demands (workload) for different tasks and it limits the number of switchings of agents between different tasks enabling specialization of agents. The preliminary version of the algorithm was introduced previously. Here a fully decentralized version of the algorithm is proposed that improves the previous version by removing the need for global information. The algorithm is successfully implemented in swarms of physically-embodied underwater robots while the swarm size and the demands for the tasks change over the course of the experiments.

关键词： Swarm intelligence swarm robotics adaptive division of labor bio-inspired algorithm social inhibition distributed partitioning

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Solving 0-1 knapsack problems based on amoeboid organism algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2013年第19期219卷 9959-9970页

作者： Zhang, Xiaoge Huang, Shiyan Hu, Yong Zhang, Yajuan Mahadevan, Sankaran Deng, Yong Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China Guangdong Univ Foreign Studies Inst Business Intelligence & Knowledge Discovery Guangzhou 510006 Guangdong Peoples R China Vanderbilt Univ Sch Engn Nashville TN 37235 USA

The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model. (C) 2013 Elsevier Inc. All rights reserved.

关键词： 0-1 Knapsack problem bio-inspired algorithm Network converting algorithm Optimization Shortest path problem

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