The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different ***,data security and private are challenging tasks to acc...
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The advancements made in Internet of Things(IoT)is projected to alter the functioning of healthcare industry in addition to increased penetration of different ***,data security and private are challenging tasks to accomplish in IoT and necessary measures to be taken to ensure secure *** this background,the current paper proposes a novel lightweight cryptography method for enhance the security in *** proposed encryption algorithm is a blend of Cross Correlation Coefficient(CCC)and Black Widow Optimization(BWO)*** the presented encryption technique,CCC operation is utilized to optimize the encryption process of cryptography *** projected encryption algorithm works in line with encryption and decryption *** key selection is performed with the help of Artificial Intelligence(AI)tool named BWO *** the combination of AI technique and CCC operation,optimal security operation is improved in *** different sets of images collected from databases,the projected technique was validated in MATLAB on the basis of few performance metrics such as encryption time,decryption time,Peak Signal to Noise Ratio(PSNR),CC,Error,encryption time and decryption *** results were compared with existing methods such as Elliptical Curve cryptography(ECC)and Rivest-Shamir-Adleman(RSA)and the supremacy of the projected method is established.
Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep lea...
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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of mult...
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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd
Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** centr...
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Self-starting Q-switching,Q-switched mode-locking and mode-locking operation modes are achieved sequentially in an all-fiber erbium-doped fiber laser with thulium-doped fiber saturable absorber for the first *** central wavelengths of Q-switching,Q-switched mode-locking and mode-locking operation modes are 1569.7 nm,1570.9 nm,and 1572 nm,*** mode-locking operation of the proposed fiber laser generates stable dark soliton with a repetition rate of 0.99 MHz and signal-to-noise ratio of 65 *** results validate the capability of generating soliton pulse by doped fiber saturable ***,the proposed fiber laser is beneficial to the applications of optical communication and signal processing system.
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 ...
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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 ***, 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.
Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in Io...
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Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.
In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization ...
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ISBN:
(数字)9798350379648
ISBN:
(纸本)9798350379655
In addition to its use in building and agriculture, global solar irradiance is one of the most critical aspects in designing and considering any solar station's volume. Because the Iraqi metrological organization and seismology lack exact information regarding irradiance, this project aims to use historical global data, develop numerical analysis using artificial neural networks, and forecast hourly irradiance. The test is run over Basra to determine references to their nearest places. The learning technique in this study is a forward neural network (FNN) and time series neural network. Their comparison depends on seven input variables: temperature, wind speed, wind direction, sunshine duration, and date. One of the most essential integrated and impactful events for managing the system of new and renewable energy sources in the power grid is accurate solar energy forecasting. The study offers a valuable method for forecasting solar energy generation using neural networks. It is difficult to apply neural networks to anticipate PV power generation because of the unpredictability of variations in the weather; unlike traditional mathematical models, which struggle to handle complicated nonlinear interactions and provide poor predictions, neural network approaches achieve superior prediction performance. The suggested forecasting model based on neural networks provides a practical means of raising the accuracy of PV generation estimation
Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator...
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Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of *** of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too *** this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi *** association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput *** implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network *** performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.
Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as me...
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Wireless Sensor Network(WSN)forms an essential part of *** is embedded in the target environment to observe the physical parameters based on the type of *** nodes inWSN are constrained by different features such as memory,bandwidth,energy,and its processing *** WSN,data transmission process consumes the maximum amount of energy than sensing and processing of the ***,diverse clustering and data aggregation techniques are designed to achieve excellent energy efficiency in *** this view,the current research article presents a novel Type II Fuzzy Logic-based Cluster Head selection with Low Complexity Data Aggregation(T2FLCH-LCDA)technique for *** presented model involves a two-stage process such as clustering and data ***,three input parameters such as residual energy,distance to Base Station(BS),and node centrality are used in T2FLCH technique for CH selection and cluster ***,the LCDA technique which follows Dictionary Based Encoding(DBE)process is used to perform the data aggregation at ***,the aggregated data is transmitted to the BS where it achieves energy *** experimental validation of the T2FLCH-LCDAtechnique was executed under three different scenarios based on the position of *** experimental results revealed that the T2FLCH-LCDA technique achieved maximum energy efficiency,lifetime,Compression Ratio(CR),and power saving than the compared methods.
Wireless power transfer (WPT) presents a promising approach for charging electric vehicles (EVs), offering increased convenience, reliability, and safety for EV customers. Recently, the incorporation of multiple coil ...
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