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Frequent itemset-based feature selection and rider Moth Search algorithm for document clustering

JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第4期34卷 1098-1109页

作者： Yarlagadda, Madhulika Rao, K. Gangadhara Srikrishna, A. JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India RVRJC Coll Engn Dept Informat Technol Chowdavaram Andhra Pradesh India Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India

Document clustering has recently been paid great attention in retrieval, navigation, and summarization of huge volumes of documents. With a better document clustering approach, computers can organize a document corpus automatically to a meaningful cluster for enabling efficient navigation, and browsing of the corpus. Document navigation and browsing is a valuable complement to the deficiencies of informa-tion retrieval technologies. This paper introduces Modsup-based frequent itemset and rider optimization-based Moth Search algorithm (Rn-MSA) for clustering the documents. At first, the input documents are given to the pre-processing step, and then, the extraction is carried out based on TF-IDF and Wordnet features. Once the extraction is done, the feature selection is carried out based on fre-quent itemset for the establishment of feature knowledge. At last, the document clustering is done using the proposed Rn-MSA, which is designed by combining rider optimization algorithm (ROA), and the Moth Search algorithm (MSA). The performance of the document clustering based on proposed Modsup + Rn-MSA is evaluated in terms of precision, recall, F-Measure, and accuracy. The developed doc-ument clustering method achieves the maximal precision of 95.90%, maximal recall of 96.41%, maximal F-Measure of 96.41%, and the maximal accuracy of 95.12% that indicates its superiority.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Document clustering Moth Search algorithm rider optimization algorithm Frequent itemset Stop word removal

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Secure medical data on cloud storage via DNA homomorphic encryption technique

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PHYSICAL COMMUNICATION 2024年 64卷

作者： Liu, Qiong Zhou, Feng Chen, Han Chinese Peoples Liberat Army Gen Hosp Hainan Hosp Med Support Ctr Sanya 572013 Hainan Peoples R China

Background: Cloud computing offers considerable flexibility and financial savings to data owners for outsourcing their complicated data management systems from local sites to the commercial public cloud. Methods: However, an effective encryption scheme is needed to protect the private medical data of the user in the cloud without leaking it. Therefore, in this paper an efficient Deoxyribonucleic Acid Homomorphic Encryption algorithm (DNA-HE) has been proposed to encrypt and store the medical images in the cloud securely. Initially, the data is mapped to DNA sequences and then encrypted using homomorphic encryption. The key for the homomorphic algorithm is generated using rider optimization Technique which ensures security. It acts as a double encryption technique. The same procedure tends to decrypt the encrypted data. Results: The efficiency of the proposed technique is assessed employing several parameters such as execution time, encryption time, and decryption time. The experimental results shows that the proposed DNA-HE system reduces encryption time of 10 %, 20 %, and 35 % than OHE, HE and ECC algorithms.

关键词： DNA homomorphic algorithm rider optimization algorithm Encrypting medical images Decryption

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Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment

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VISUAL COMPUTER 2021年第8期37卷 2369-2390页

作者： Vinolin, V. Sucharitha, M. Noorul Islam Ctr Higher Educ Dept Elect & Commun Engn Kanyakumari Tamil Nadu India Malla Reddy Coll Engn & Technol Dept Elect & Commun Engn Medchal Telangana India

Video forgery detection is one of the challenges in this digital era, where the focus is on discovering authenticity. Though there are so many methods available to detect forgeries in the video, there is no method that utilizes illumination-based forgery detection. Hence, this research focuses on establishing the 3D model of the video frame to generate light coefficients in order to detect the forgeries in the video. On the other hand, this paper proposes dual adaptive-Taylor-rider optimization algorithm-based deep convolutional neural network (DA-Taylor-ROA-based DCNN) for video forgery detection, where DCNN is trained using the dual adaptive-Taylor-rider optimization algorithm (DA-TROA) that inherits the adaptive concept and Taylor series within the standard rider optimization algorithm (ROA). For the detection process, the distance-based features from the light coefficients and face objects detected using the Viola-Jones algorithm from the video frames are used. The significance of the method is analyzed using the real images for varying noise conditions based on the performance metrics, such as accuracy, true positive rate, and true negative rate. The percentage improvement of accuracy for proposed DA-Taylor-ROA-based DCNN with respect to Taylor-ROA-Based deep CNN is 4.3626% in the absence of noise, and 1.5985% of accuracy improvement in the presence of speckle noise, respectively.

关键词： Video forgery detection Deep learning network rider optimization algorithm Taylor series Spliced images

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Taylor-Based Least Square Estimation in MIMO-OFDM Systems for Multimedia Applications

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WIRELESS PERSONAL COMMUNICATIONS 2021年第1期120卷 609-631页

作者： Raut, Shital N. Jalnekar, Rajesh M. Vishwakarma Inst Technol Pune 411037 Maharashtra India

Recent mobile telecommunication systems are using multiple-input multiple-output system (MIMO) collective with the orthogonal frequency division multiplexing (OFDM), which is well-known as MIMO-OFDM, to offer robustness and higher spectrum efficiency. The most important challenge in this scenario is to achieve an accurate channel estimation to identify the information symbols once the receiver must have the channel state information to balance and process the received signal. Hence, an effective technique is introduced by proposing the Taylor-least square error algorithm (TLSE) to improve the performance of the MIMO-OFDM system in multimedia applications. In addition, the user admission control is done in multi user-MIMO (MU-MIMO) system using the priority-based scheduling based on Dolphin-rider optimization (DRO) algorithm that is integrated within the space-time block code (STBC) STBC-MIMO-OFDM system for efficient power allocation to ensure the energy efficiency. The DRO is the integration of rider optimization algorithm (ROA) and Dolphin Echolocation (DE). Here, channel estimation is done using the novel optimization algorithm, termed TLSE, which is designed by modifying LSE with the Taylor series. Moreover, the fitness parameters, such as power, priority, throughput, and Proportionally Fair, are computed. The experimentation is conducted in different fading environments with three modulation schemes, binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and quadrature amplitude modulation (QAM) with the performance metrics, namely bit error rate (BER) and throughput. The developed TLSE + DRO (QAM) outperformed other methods with minimal BER of 0.0001 based on channel-2 and maximal throughput of 0.9965 with respect to channel-1.

关键词： Channel estimation MIMO-OFDM system Taylor series rider optimization algorithm Least square error algorithm

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Improved rider optimization for optimal container resource allocation in cloud with security assurance

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INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS 2020年第3期16卷 235-258页

作者： Vhatkar, Kapil Netaji Bhole, Girish P. Veermata Jijabai Technol Inst Dept CE&IT Mumbai Maharashtra India

Purpose The containerization application is one among the technologies that enable microservices architectures, which is observed to be the model for operating system (OS) virtualization. Containers are the virtual instances of the OS that are structured as the isolation for the OS atmosphere and its file system, which are executed on the single kernel and a single host. Hence, every microservice application is evolved in a container without launching the total virtual machine. The system overhead is minimized in this way as the environment is maintained in a secured manner. The exploitation of a microservice is as easy to start the execution of a new container. As a result, microservices could scale up by simply generating new containers until the required scalability level is attained. This paper aims to optimize the container allocation. Design/methodology/approach This paper introduces a new customized rider optimization algorithm (C-ROA) for optimizing the container allocation. The proposed model also considers the impact of system performance along with its security. Moreover, a new rescaled objective function is defined in this work that considers threshold distance, balanced cluster use, system failure, total network distance and security as well. At last, the performance of proposed work is compared over other state-of-the-art models with respect to convergence and cost analysis. Findings For experiment 1, the implemented model at 50th iteration has achieved minimal value, which is 29.24%, 24.48% and 21.11% better from velocity updated grey wolf optimisation (VU-GWO), whale random update assisted LA (WR-LA) and rider optimization algorithm (ROA), respectively. Similarly, on considering Experiment 2, the proposed model at 100th iteration attained superior performance than conventional models such as VU-GWO, WR-LA and ROA by 3.21%, 7.18% and 10.19%, respectively. The developed model for Experiment 3 at 100th iteration is 2.23%, 5.76% and 6.56% superior to VU-

关键词： Microservices System Performance Container Resource Allocation rider optimization algorithm

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Optimal deep recurrent neural network for sentiment grade classification

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INTERNATIONAL JOURNAL OF WEB INFORMATION SYSTEMS 2021年第6期17卷 557-579页

作者： Jasti, Sireesha Gandhi Inst Technol & Management VNRVJIET Comp Sci & Engn Visakhapatnam Andhra Pradesh India

Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam wo

关键词： Sentiment classification Feature fusion Feedback artificial tree algorithm rider optimization algorithm Natural language processing Deep RNN

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rider Water Wave-enabled deep learning for disease detection in rice plant

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ADVANCES IN ENGINEERING SOFTWARE 2023年第1期182卷

作者： Daniya, T. Vigneshwari, S. GMR Inst Technol Dept Informat Technol Rajam Andhra Pradesh India Sathyabama Inst Sci & Technol Chennai Tamilnadu India

Rice diseases have degraded the production of the rice plant, which produces economic loss. To control and minimize the effects of attacks, the diseases are required to be recognized at a premature stage. Premature detection of infections can improve the yield from quantitative as well as qualitative losses, diminish the usage of pesticides, and improve the economic growth of the country. Hence, this paper devises a new method, namely rider Water Wave-based neural network (RWW-NN) for finding the disease in the rice plant, where the training of NN is completed using the RWW, which is formed by assimilating rider optimization algorithm (ROA) and Water wave optimization (WWO). Initially, the pre-processing is done by using histogram equalization from the input image. Then, the segmentation is completed using Segmentation Network (SegNet), and then the CNN features are employed for feature mining in order to acquire the optimal features for disease recognition. These features are fed to NN for disease detection wherein the RWW is introduced for training the optimal weights. The RWW-based NN acquires greatest accuracy of 0.908, F-measure of 0.907, sensitivity of 0.862, and specificity of 0.947 based on K-value using Rice disease dataset.

关键词： rider optimization algorithm Disease detection Water wave optimization Neural network Histogram equalization

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Modified rider optimization-Based V Channel Magnification for Enhanced Video Super Resolution

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2021年第1期21卷 2150003-2150003页

作者： Jagdale, Rohita H. Shah, Sanjeevani K. Maharashtra Inst Technol Pune Maharashtra India Savitribai Phule Pune Univ Pune Maharashtra India Smt Kashibai Navale Coll Engn Pune Maharashtra India

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the rider optimization algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.

关键词： Video super resolution rider optimization algorithm RGB V-channel magnification HSV

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Affective state prediction of E-learner using SS-ROA based deep LSTM

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ARRAY 2023年 19卷

作者： Rathi, Snehal Hiran, Kamal Kant Sakhare, Sachin Vishwakarma Inst Informat Technol Dept Comp Engn Pune India Symbiosis Univ Appl Sci Sch Comp Sci & IT Indore India

An affective state of a learner in E-learning has gained enormous interest. The prediction of the emotional state of a learner can enhance the outcome of learning by including designated mediation. Many techniques are developed for anticipating emotional states using video, audio, and bio-sensors. Still, examining video, and audio will not confirm secretiveness and is exposed to security issues. Here the creator devises a fusion technique, to be specific Squirrel Search and rider optimization-grounded Deep LSTM for affect prediction. The Deep LSTM is trained to exercise the new fusion SS-ROA. Then, the SS-ROA-grounded Deep LSTM classifies the states like frustration, confusion, engagement, wrathfulness, and so on. It is based on the interaction log data of the E-learner. In conclusion, the course and student ID, predicted state, test marks, and course completion status are taken as result information to find out the correlations. The new algorithm gives the best performance in comparison to other present methods with the highest prediction accurateness of 0.962 and the most noteworthy connection of 0.379 respectively. After discovering affective states, students may get the advantage of getting real comments from a teacher for improving one's performance during learning. However, such systems should also give feedback about the learner's affective state or passion because it greatly affects the student's encouragement toward better learning.

关键词： Higher education Affective states Deep LSTM Squirrel search algorithm rider optimization algorithm and Interaction log E-learning

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RideNN-OptDRN: Heart disease detection using RideNN based feature fusion and optimized deep residual network

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第28期34卷 e7355-e7355页

作者： Merugula, Suneetha Padmaja, Buddi Veerubommu, Ragavi GITAM Dept CSE GITAM Sch Technol Visakhapatnam Andhra Pradesh India Inst Aeronaut Engn Dept CSE ML Hyderabad India Sri Krishna Coll Engn & Technol Dept Comp Applicat Coimbatore Tamil Nadu India

Heart disease detection through early-stage syndrome remains as a main confront in present world situation. If it is not detected appropriate time, then this turns out to be the major cause of death. Several existing heart disease detection techniques are developed with lower detection performance and therefore it is very significant to introduce a novel heart disease detection model that poses the potential to detect heart disease from input data. A novel detection approach named, social water cycle algorithm-based deep residual network (SWCA-based DRN) is proposed for classification of heart disease. The developed SWCA algorithm is a newly designed by the hybridization of social optimization algorithm and water cycle algorithm. Here, an input data is initially preprocessed and the feature fusion procedure is carried out RV coefficient enabled rider optimization algorithm-based neural network. With the fused feature result, heart disease classification is performed utilizing a DRN classifier where training procedure of DRN is done by proposed optimization algorithm, named SWCA. Furthermore, developed SWCA-enabled DRN technique outperformed different other present heart disease detection approaches and attained superior performance concerning the performance measures, like testing accuracy, sensitivity, and specificity with highest values of 0.941, 0.954, and 0.925.

关键词： deep residual network heart disease rider optimization algorithm social optimization algorithm water cycle algorithm

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