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Person-independent facial expression recognition based on the fusion of HOG descriptor and cuttlefish algorithm

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第8期81卷 11563-11586页

作者： Hussein, Haval I. Dino, Hivi Ismat Mstafa, Ramadhan J. Hassan, Masoud Muhammed Univ Zakho Dept Comp Sci Fac Sci Duhok 42002 Kurdistan Regio Iraq Nawroz Univ Dept Comp Sci Coll Sci Duhok 42001 Kurdistan Regio Iraq

This paper proposes an efficient approach for person-independent facial expression recognition based on the fusion of Histogram of Oriented Gradients (HOG) descriptor and cuttlefish algorithm (CFA). The proposed approach employs HOG descriptor due to its outstanding performance in pattern recognition, which results in features that are robust against small local pose and illumination variations. However, it produces some irrelevant and noisy features that slow down and degrade the classification performance. To address this problem, a wrapper-based feature selector, called CFA, is used. This is because CFA is a recent bio-inspired feature selection algorithm, which has been shown to effectively select an optimal subset of features while achieving a high accuracy rate. Here, support vector machine classifier is used to evaluate the quality of the features selected by the CFA. Experimental results validated the effectiveness of the proposed approach in attaining a high recognition accuracy rate on three widely adopted datasets: CK+ (97.86%), RaFD (95.15%), and JAFFE (90.95%). Moreover, the results also indicated that the proposed approach yields competitive or even superior results compared to state-of-the-art approaches.

关键词： cuttlefish algorithm Histogram of oriented gradients Facial expression recognition Support vector machine

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A fused contextual color image thresholding using cuttlefish algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第1期33卷 271-299页

作者： Bhandari, Ashish Kumar Rahul, Kusuma Shahnawazuddin, Syed Natl Inst Technol Patna Dept Elect & Commun Engn Patna 800005 Bihar India

In this paper, we have proposed a fusion-based context-sensitive Masi energy curve model for multi-level thresholding exploiting cuttlefish algorithm (CFA). The proposed algorithm is simple and very efficient for the task of color image segmentation. Although Masi entropy exploits the additive/non-extensive information with the aid of a concordant entropic parameter, the performance is observed to be poor in the case of color image segmentation. Improved results can be obtained by using the concept of energy curve with Masi entropy at the cost of increased computational cost while selecting the suitable thresholds. To overcome the aforementioned drawbacks as well as to increase the quality of the segmented image, a simple multi-level thresholding method is proposed in this paper. The proposed color image segmentation scheme exploits the concept of local contrast fusion along with CFA to resolve the aforementioned issues. In order to prove the effectiveness of the proposed scheme, experimental evaluations on standard daily-life color images have been reported in this paper. The experimental outputs demonstrate that fusion-based multi-level thresholding is better than the existing dominant segmentation methods.

关键词： Image fusion Local contrast Masi entropy Energy curve cuttlefish algorithm Multi-level image segmentation

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A Migration-Based cuttlefish algorithm With Short-Term Memory for Optimization Problems

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IEEE ACCESS 2020年 8卷 70270-70292页

作者： Al Daweri, Muataz Salam Abdullah, Salwani Ariffin, K. A. Zainol Univ Kebangsaan Malaysia Ctr Artificial Intelligence Technol Bangi 43600 Malaysia Univ Kebangsaan Malaysia Ctr Cyber Secur Bangi 43600 Malaysia

cuttlefish algorithm (CFA) is a metaheuristic bio-inspired algorithm that mimics the color-changing behavior by the cuttlefish. It is produced by light reflected from different layers of cells and involves two processes, i.e., reflection and visibility. The reflection process simulates the light reflection mechanism, while the visibility process simulates the visible appearance of the matching pattern used by the cuttlefish. There is no cooperation strategy between the solutions of the CFA's sub-populations. The strategy can improve the capabilities of local exploitation and global exploration in terms of solution diversity and quality during the search process. This paper introduces two schemes to improve the performance of the cuttlefish algorithm in continuous optimization problems. Firstly, a migration strategy is employed between the multi-population cuttlefish to increase solutions diversity during the search process. Secondly, one of the exploitation strategies of the standard cuttlefish is replaced with a new exploitation strategy based on short-term memory. The test demonstrates that the proposed algorithm outperforms the standard cuttlefish algorithm. Besides, the performance of the proposed algorithm was investigated using the CEC2013 benchmarking test functions. Comparisons with several state-of-the-art algorithms were performed, and the outcomes indicated that the proposed method offers a competitive performance advantage over the alternatives.

关键词： Optimization Sociology Statistics Standards Convergence Color Diversity reception cuttlefish algorithm metaheuristics migration strategy optimization short-term memory unimodal and multimodal test functions

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Optimizing Clustering with cuttlefish algorithm 3rd

Optimizing Clustering with Cuttlefish Algorithm

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3rd Conference on Information Technology, Systems Research and Computational Physics (ITSRCP)

作者： Kowalski, Piotr A. Lukasik, Szymon Charytanowicz, Malgorzata Kulczycki, Piotr AGH Univ Sci & Technol Fac Phys & Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland Polish Acad Sci Syst Res Inst Ul Newelska 6 PL-01447 Warsaw Poland Lublin Univ Technol Elect Engn & Comp Sci Fac Nadbystrzycka 36A PL-20618 Lublin Poland

ISBN: (纸本)9783030180584;9783030180577

The cuttlefish algorithm, a modern metaheuristic procedure, is a very recent solution to a broad-range of optimization tasks. The aim of the article is to outline the cuttlefish algorithm and to demonstrate its usability in data mining problems. In this paper, we apply this metaheuristic procedure for a clustering problem, with the Calinski-Harabasz index used as a measure of solution quality. To examine the algorithm performance, selected datasets from the UCI Machine Learning Repository were used. Furthermore, the well-known and commonly utilized k-means procedure was applied to the same data sets to obtain a broader, independent comparison. The quality of generated results were assessed via the use of the Rand Index.

关键词： Clustering cuttlefish algorithm Biologically inspired algorithm Optimization Metaheuristic

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Enhancing the Performance of Photovoltaic Systems under Partial Shading Conditions Using cuttlefish algorithm 8

Enhancing the Performance of Photovoltaic Systems under Part...

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International Conference on Renewable Energy Research and Applications

作者： Sameh, Mariam A. Badr, M. A. Marei, Mostafa, I Attia, Mahmoud A. Future Univ Egypt FUE Cairo Egypt Ain Shams Univ ASU Cairo Egypt Ain Shams Univ Cairo Egypt

ISBN: (纸本)9781728135878

Improving the control of Photovoltaic (PV) power plants is an increasing interest worldwide. This improvement would hopefully help in reaching the maximum benefit of PV performance all the time. A lot of challenges are facing the control of PV systems such as Maximum Power Point Tracking (MPPT) of PV. Partial shading has become recently one of the most important challenges facing MPPT. This calls for improving the control strategy of PV power plants to cope with it. The work in this paper utilizes a relatively new optimization method which is the cuttlefish algorithm (CFA) to tune a Second Order Amplifier (SOA) for enhancing the PV system performance. The CFA has proved to be a very effective and fast optimization technique that can reach an accurate optimum solution with minimum effort and time. In addition, the SOA also has been found capable of enhancing the PV system performance at any condition if well-tuned. The main objective of this paper is to enhance the performance of a PV system under partial shading conditions through the use of a well selected combination of both the CFA and SOA. The needed mathematical models along with the required computer simulations are developed and the obtained results are analyzed. The reached at conclusions prove that the proposed control system and strategy are successful in achieving the declared objectives of the paper

关键词： cuttlefish algorithm Second Order Amplifier Maximum power point tracking Partial shading Photovoltaic

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Optimising Fractal Encoding Using cuttlefish Optimisation algorithm

Optimising Fractal Encoding Using Cuttlefish Optimisation Al...

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International Conference on Computing, Power and Communication Technologies (GUCON)

作者： Sehgal, Smriti Ahuja, Laxmi Bindu, M. Hima Amity Univ Amity Sch Engn & Technol Noida UP India Amity Univ Amity Inst Informat Technol Noida UP India North Orissa Univ Baripada Odisha India

ISBN: (纸本)9789811503399;9789811503382

Increase in demand for better appearance and less storage requirement of an image has led to explore various image compression techniques. Due to technological advancement of photo capturing devices such as single-lens reflex camera (SLR), digital SLR, smart phone cameras, and satellite sensors, more detailed information can be recorded in a single image. A coloured image captured by high wavelength sensors produces large-sized image as it contains highly correlated data. Many image compression and analysis techniques have been developed to aid the interpretation of images and to compress as much information as possible in it. The goal of image compression is to recreate original image with less number of bits and minimal data loss. For generating computer graphic images and compression of objects, it has been suggested that by storing images in the form of transformation instead of pixels lead to compression and can be achieved through fractal coding. In fractal image compression, encoding image blocks into fractal codes using iterated function system (IFS) takes large amount of time taken to compress it. A study of various meta-heuristics techniques, which are designed to solve complex problems approximately, has been conducted to improve upon computational time of fractal coding as well as compression ratio, while maintaining image visually. In this paper, using the property of pattern adaption of surroundings, cuttlefish optimisation algorithm is applied to minimise the time taken for fractal coding. Compression results have been compared with other meta-heuristic techniques, such as particle swarm optimisation and genetic algorithm, and has shown high compression ratio of approximately 31%.

关键词： Affine transformation cuttlefish algorithm Fractal image compression Optimisation Meta-heuristics

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Performance evaluation of cuttlefish optimization-based contention control in Wireless Rechargeable Sensor Network

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JOURNAL OF SUPERCOMPUTING 2024年第16期80卷 24241-24268页

作者： Susan, T. Siron Anita Nithya, B. SRM Inst Sci & Technol Dept CSE Trichy Tamil Nadu India Natl Inst Technol Dept CSE Tiruchirappalli Trichy Tamil Nadu India

Wireless rechargeable sensor network (WRSN) is one of the most important networks in today's world for extending network lifetime. To ensure a collision-free contention in WRSN, this paper proposes the Contention Window Optimization using the cuttlefish algorithm (CWO-CA) and addresses existing issues such as the capture effect, the fairness problem, and the queue hike. For safe channel access, the fitness function of CWO-CA is proposed using the retransmission count, queue size, and residual energy of the sensor node. The proposed algorithm divides the Contention Window interval into two halves based on the fitness function, and the nodes are assigned in either half to guarantee service differentiation. The fitness function among groups of the proposed algorithm constitutes the best fitness value. By selecting the best fitness value, the proposed algorithm facilitates adaptive contention management by dynamically balancing resource utilization of the network with the optimal result. The nodal behavior of CWO-CA has been modeled using a discrete Markov model and also simulated to measure throughput, packet delivery ratio, packet loss ratio, average queue size, residual energy, and delay. These results confirm that the proposed CWO-CA algorithm attained better performance than other existing algorithms.

关键词： Wireless sensor networks Wireless rechargeable sensor networks Contention window optimization cuttlefish algorithm Green communication

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A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第5期42卷 2670-2679页

作者： Eesa, Adel Sabry Orman, Zeynep Brifcani, Adnan Mohsin Abdulazeez Zakho Univ Dept Comp Sci Fac Sci Duhok City Krg Iraq Istanbul Univ Dept Comp Engn Fac Engn TR-34320 Istanbul Turkey Duhok Polytech Univ Dept IT Duhok Tech Inst Duhok City Krg Iraq

This paper presents a new feature-selection approach based on the cuttlefish optimization algorithm which is used for intrusion detection systems (IDSs). Because IDSs deal with a large amount of data, one of the crucial tasks of IDSs is to keep the best quality of features that represent the whole data and remove the redundant and irrelevant features. The proposed model uses the cuttlefish algorithm (CFA) as a search strategy to ascertain the optimal subset of features and the decision tree (DT) classifier as a judgement on the selected features that are produced by the CFA. The KDD Cup 99 dataset is used to evaluate the proposed model. The results show that the feature subset obtained by using CFA gives a higher detection rate and accuracy rate with a lower false alarm rate, when compared with the obtained results using all features. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Feature selection cuttlefish algorithm Intrusion detection systems Decision trees ID3 algorithm

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An effective and secure video watermarking using hybrid technique

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MULTIMEDIA SYSTEMS 2021年第5期27卷 953-967页

作者： Dhevanandhini, G. Yamuna, G. Alagappa Chettiar Govt Coll Engn Dept Elect & Commun Engn Karaikkudi Tamil Nadu India Annamalai Univ Fac Engn & Technol Dept Elect & Commun Engn Chidambaram Tamil Nadu India

Now, all satellite channels handle high-quality data, including HD video files (1 K, 2 K, and 4 K). High-quality compression software is required to archive and transmit these files accurately and efficiently. HEVC is a standard development technique for high-quality video compression which can produce double compression while preserving video quality. On the other hand, the use of a robust digital video watermark algorithm can solve copyright issues for these types of categories. Therefore, in this paper, an efficient compression-based secure digital watermarking is proposed. In this paper, initially, the video are converted into number of frames. Then, we de-compose the frame using dual-tree complex wavelet transform. After that, to increase the security of the system, the embedding positions are optimally selected using adaptive cuttlefish optimization algorithm. Then, the secrete images are encrypted using elliptic curve cryptography algorithm. After that, the encrypted images are converted into binary bit. Then, the binary bits are embedded into the selected position of video frame. After the encryption process, to decreases the size of encrypted document, H.265 video encoding approach is applied. This method is, effectively, reduce the size of the image, without affecting quality of the image. Finally, the compressed image is transmitted to the receiver. The reverse process of embedding is used for extraction process. The performance of proposed approach is analyzed in terms of different metrics and effectiveness compared with different methods.

关键词： Crows algorithm cuttlefish algorithm Dual-tree complex wavelet transform Video watermarking Hybrid technique Video compression

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An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

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IEEE ACCESS 2020年 8卷 34717-34727页

作者： Khan, Mohammad Ayoub Univ Bisha Coll Comp & Informat Technol Bisha 67714 Saudi Arabia

Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.

关键词： Heart Diseases Biomedical monitoring Cloud computing Monitoring Electrocardiography AEHO cuttlefish algorithm MDCNN IoT Cleveland dataset sensors wearable device CAGR electrocardiogram LSTM and CNN

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