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| 1 | Oppositional Harris Hawks Optimization with Deep Learning-Based Image Captioning显示文摘Image Captioning is an emergent topic of research in the domain of artificial intelligence(AI).It utilizes an integration of Computer Vision(CV)and Natural Language Processing(NLP)for generating the image descriptions.Itfinds use in several application areas namely recommendation in editing applications,utilization in virtual assistance,etc.The development of NLP and deep learning(DL)modelsfind useful to derive a bridge among the visual details and textual semantics.In this view,this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning(OHHO-DLIC)technique.The OHHO-DLIC technique involves the design of distinct levels of pre-processing.Moreover,the feature extraction of the images is carried out by the use of EfficientNet model.Furthermore,the image captioning is performed by bidirectional long short term memory(BiLSTM)model,comprising encoder as well as decoder.At last,the oppositional Harris Hawks optimization(OHHO)based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models.The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches. | V.R.Kavitha K.Nimala A.Beno K.C.Ramya Seifedine Kadry Byeong-Gwon Kang Yunyoung Nam | 2023 | Computer Systems Science & Engineering2023，44，1： | 0 |
| 2 | An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model显示文摘Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific patterns.Therefore, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI. | T.Gopalakrishnan Periakaruppan Sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal | 2022 | Computers, Materials & Continua2022，，4： | 0 |
| 3 | Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET显示文摘A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and infrastructure.The nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery process.Route planning in MANET is a multi-hop communication process due to the restricted transmission range of the nodes.Location aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy consumption.Though few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design issue.In this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for MANETs.The EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR protocol.The fitness value of the created solutions is determined next to pass the solutions to the objective function.The MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy utilization.To ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take place.The resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared methods.The resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques. | E.Ahila Devi K.C.Ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang | 2022 | Computers, Materials & Continua2022，，4： | 0 |
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