检索结果-内蒙古大学图书馆

Network Intrusion Detection and Mitigation Using Hybrid optimization Integrated Deep Q Network

CYBERNETICS AND SYSTEMS 2024年第1期55卷 107-123页

作者： Emil Selvan, G. S. R. Daniya, T. Ananth, J. P. Suresh Kumar, K. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai 625015 Tamil Nadu India GMR Inst Technol Dept Informat Technol Rajam India Sri Krishna Coll Engn & Technol Dept CSE Coimbatore Tamil Nadu India Saveetha Engn Coll Dept Informat Technol Chennai Tamil Nadu India

Secure communication between network resources is a key issue, and researchers are persistently developing new intrusion detection schemes to detect intruders to enhance security. However, the traditional intrusion detection techniques have security challenges due to their poor detection performance. Hence, an effective intrusion detection model, namely spider monkey social optimization algorithm (SMSOA)-based Deep Q network is introduced for the detection of intrusion in the network. Here, the intrusion is detected based on the following three steps, such as pre-processing, feature fusion step, and detection. Estimated value replaced the missed value using missing value imputation in the pre-processing phase. Jaccard index by means of deep belief network (DBN) is performed in feature fusion. Deep Q network is used for intrusion detection process, where SMSOA method is used for training. The developed SMSOA scheme is performed by the integration of the social optimization algorithm (SOA) and spider monkey optimization (SMO). In addition, the attack mitigation process prevents the intruder enter into the network. Moreover, the developed model attained a better performance with respect to f-measure, recall values, and precision of 0.9455, 0.9585, and 0.9590, respectively.

关键词： Deep belief network Deep Q network Jaccard index social optimization algorithm spider monkey optimization

来源：评论

学校读者我要写书评

暂无评论

Hybrid optimization enabled deep learning-based ensemble classification for heart disease detection

引用

SIGNAL IMAGE AND VIDEO PROCESSING 2023年第8期17卷 4235-4244页

作者： Jayasudha, R. Suragali, Chanti Thirukrishna, J. T. Kumar, B. Santhosh Sri Krishna Coll Engn & Technol Dept Comp Sci & Engn Coimbatore 641008 India GMR Inst Technol Dept Comp Sci & Engn Rajam India Dayananda Sagar Acad Technol & Management Dept Informat Sci & Engn Bangalore 560082 Karnataka India Guru Nanak Inst Technol Dept Comp Sci & Engn Ibrahimpatnam 501506 Telangana India

Heart diseases (HD) in humans are the most common cause of death. In the current global environment, the early detection of HD is a challenging process. The goal of this work is to develop a deep learning technique and to test the necessary classification model to improve HD detection. Hybrid optimization deep learning-based ensemble classification for heart disease is devised in this research for HD detection. Here, the input data are acquired from the dataset and preprocessed. Then, preprocessed data are subjected to the feature fusion scheme that is carried out by congruence coefficient and overlap coefficient enabled deep belief network. Consequently, with the feature fusion output, heart disease prediction classification is done by the proposed social water cycle driving training optimization (SWCDTO) ensemble classifier, which is devised using the driver training-based optimization algorithm and social water cycle algorithm. This method can efficiently train multiple classifiers to improve their efficiency. These results are combined to produce the final results. Moreover, the introduced SWCDTO-based ensemble classifier approach compared with different heart disease prediction algorithms shows better performance regarding the evaluation measures such as specificity, accuracy, and sensitivity with better values of 95.84%, 94.80%, and 95.36%. Overall the proposed method has low computational time and thus improves efficiency.

关键词： Driver training-based optimization Deep belief network Ensemble classifier social optimization algorithm social water cycle driving training optimization

来源：评论

学校读者我要写书评

暂无评论

Autism Spectrum Disorder Detection Using Fractional social Driving Training-Based optimization Enabled Deep Learning

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第13期83卷 37523-37548页

作者： Vidyadhari, Ch Karrothu, Aravind Manickavasagam, Prabhakar Devi, S. Anjali Gokaraju Rangaraju Inst Engn & Technol Dept Informat Technol Hyderabad India GMR Inst Technol Dept Informat Technol Rajam 532127 Andhra Pradesh India REVA Univ Sch Comp Sci & Engn Bangalore 560064 India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India

Autism Spectrum Disorder (ASD) is neurodevelopment-based impact on interactive communication and social skills. Diagnosing ASD is one of serious issues that start manifesting at low ages, and is difficult to diagnose at early stages. Autism is characterized by both environmental and genetic factors. Lack of communication issues, social interaction, and limited interest behaviors are possible individuality of autism noticed in children, along other symptoms. This paper aims at ASD detection by Deep Quantum Neural Network (DQNN), wherein this network is trained by proposed Fractional social Driving Training-Based optimization (FSDTBO). The initial stage of this processing starts with acquisition of image from dataset, and further pre-processing is carried out using Gaussian filter, and this filtered image is suspended for Regions of Interest (ROI) extraction. Also, extraction of nub region is done by proposed social Driving Training-Based optimization (SDTBO), from which classification process is done by considering extracted features too. Here, proposed FSDTBO is integration process among Fractional Calculus (FC) and SDTBO, wherein SDTBO is collaboration between social optimization algorithm (SOA) and Driving Training-Based optimization (DTBO). Moreover, classification performance of ASD is found based on three metrics, like accuracy, specificity, and sensitivity with superior values of 0.90, 0.94, and 0.96.

关键词： Gaussian filter social optimization algorithm Autism Spectrum Disorder Driving Training-Based optimization Fractional Calculus

来源：评论

学校读者我要写书评

暂无评论

Fractional IWSOA-LB: Fractional Improved Whale social optimization Based VM Migration Strategy for Load Balancing in Cloud Computing

引用

INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS 2023年第1期30卷 58-74页

作者： George, Shelly Shiju Pramila, R. Suji Noorul Islam Ctr Higher Educ Kumaracoil Tamil Nadu India Amal Jyothi Coll Engn Dept Comp Applicat Kottayam Kerala India

Data centres have seen significant growth recently as a result of the phenomenal rise of cloud computing. These data centres typically use more energy, which significantly raises operational costs. The management of server consolidation involves moving all Virtual Machines (VMs) to idle servers. However, performance suffers as a result of migration as migration volume and time increase. The Cloud computing model generates computational cooperative of huge computing services and systems. Recently, resource sharing, task scheduling and resource management between users are familiar research areas. In this paper, Fractional Improved Whale social optimization algorithm (Fractional IWSOA) is developed for load balancing in the cloud model. The developed Fractional IWSOA is newly devised by incorporating social optimization algorithm (SOA) and Improved Whale optimization algorithm (IWOA) along with Fractional Calculus (FC). Moreover, the categorization of VM is performed based on Deep Embedded Clustering (DEC) which is categorized into two types, underloaded VMs and overloaded VMs. Additionally, the tasks in underloaded VM is assigned based on various factors. As a result, the developed Fractional IWSOA performed better than other existing techniques in terms of load, capacity, and resource usage, which were respectively 0.1160, 0.5898, and 0.7168.

关键词： Load balancing Fractional calculus Deep embedded clustering social optimization algorithm Resource utilization

来源：评论

学校读者我要写书评

暂无评论

IoT enabled lung cancer detection and routing algorithm using CBSOA-based ShCNN

引用

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2023年第1期37卷 224-243页

作者： Raj, Emil Selvan Gnanasigamani Samuel Jingle, Issac Diana Jeba Maram, Balajee Ananth, John Patrick Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai 625015 Tamil Nadu India Christ Univ Dept Comp Sci & Engn Bengal Uru Karnataka India Chitkara Univ Sch Engn & Technol Dept Comp Sci & Engn Baddi Himachal Prades India Sri Krishna Coll Engn & Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

The Internet of Things (IoT) has tremendously spread worldwide, and it influenced the world through easy connectivity, interoperability, and interconnectivity using IoT devices. Numerous techniques have been developed using IoT-enabled health care systems for cancer detection, but some limitations exist in transmitting the health data to the cloud. The limitations can be accomplished using the proposed chronological-based social optimization algorithm (CBSOA) that effectively transmits the patient's health data using IoT network, thereby detecting lung cancer in an effective way. Initially, nodes in the IoT network are simulated such that patient's health data are collected, and for transmission of such data, routing is performed in order to transmit the health data from source to destination through a gateway based on cloud service using CBSOA. The fitness is newly modeled by assuming the factors like energy, distance, trust, delay, and link quality. Finally, lung cancer detection is carried out at the destination point. At the destination point, the acquired input data is fed to preprocessing phase to make the data acceptable for further mechanism using data normalization. Once the feature selection is done using Canberra distance, then the lung cancer detection is performed using shepard convolutional neural network (ShCNN). The process of routing as well as training of ShCNN is performed using the CBSOA algorithm, which is devised by the inclusion of the chronological concept into the social optimization algorithm. The proposed approach has achieved a maximum accuracy of 0.940, maximum sensitivity of 0.941, maximum specificity of 0.928, and minimum energy of 0.452.

关键词： cancer detection Internet of Things routing shepherd convolutional neural network social optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Shuffled shepherd social optimization based deep learning for rice leaf disease classification and severity percentage prediction

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第4期35卷 e7523-e7523页

作者： Daniya, Thavasilingam Srinivasan, Vigneshwari Sathyabama Inst Sci & Technol Sch Comp Dept Comp Sci & Engn Chennai Tamil Nadu India GMR Inst Technol Dept Informat Technol Rajam Andhra Pradesh India

The earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization-based deep learning (SSSO-based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak-based UNet (BLSNet), multilayer maxout, resistance spot welding-based deep recurrent neural network (RSW-based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN) + deep LSTM.

关键词： deep learning deep maxout network rice plant shuffled shepherd optimization algorithm social optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

FSOA-DNFNet: Incremental indexing and image classification using hybrid optimization-based deep neuro Fuzzy network

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第19期34卷 e7049-e7049页

作者： Nadendla, Hanumantha Rao Srikrishna, Atluri Rao, Kancherla Gangadhara JNTUK Dept Comp Sci & Engn Kakinada Andhra Pradesh India RVR & JC Coll Engn Dept Comp Sci & Engn Flat 401 Guntur 522006 Andhra Pradesh India RVR & JC Coll Engn Dept Informat Technol Guntur Andhra Pradesh India Acharya Nagarjuna Univ Dept Comp Sci & Engn Guntur Andhra Pradesh India

The rapid evolution and tremendous growth of internet has provided massive growth of unstructured data that leads to a complexity while retrieving dynamic data effectively. The rapid growth in data volume has imposed many challenging constraints, such as necessity to retrieve data completely even if newly arrived samples are occurred and storing of huge volume of data. This has paved a way for concentrating more on incremental learning that functions on information streams. To speed up retrieval, clustering methods and indexes are utilized and periodic updating of clusters is very substantial because of dynamic nature of databases. Moreover, the standard of clustering techniques purely based on data representation techniques, in which traditional methods faced problems like dimensionality explosion and sparsity. To address such limitations, an effectual strategy is developed for incremental indexing and image classification using proposed Feedback social optimization algorithm (FSOA). The image classification is effectively carried out using Deep neuro fuzzy optimizer and it is trained by employing the proposed FSOA and newly FSOA is derived by the integration of Feedback Artificial Tree (FAT) algorithm and social optimization algorithm (SOA). Moreover, the proposed FSOA has achieved the maximum clustering accuracy of 93.382, the maximum testing accuracy of 94.4, the maximum sensitivity of 91.892, and the maximum specificity of 96.058.

关键词： deep neuro fuzzy network Feedback Artificial Tree algorithm image classification incremental indexing social optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Heap-based optimizer inspired by corporate rank hierarchy for global optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2020年 161卷 113702-113702页

作者： Askari, Qamar Saeed, Mehreen Younas, Irfan GIFT Univ Dept Comp Sci Gujranwala Pakistan Natl Univ Comp & Emerging Sci Dept Comp Sci Lahore Pakistan

In an organization, a group of people working for a common goal may not achieve their goal unless they organize themselves in a hierarchy called Corporate Rank Hierarchy (CRH). This principle motivates us to map the concept of CRH to propose a new algorithm for optimization that logically arranges the search agents in a hierarchy based on their fitness. The proposed algorithm is named as heap-based optimizer (HBO) because it utilizes the heap data structure to map the concept of CRH. The mathematical model of HBO is built on three pillars: the interaction between the subordinates and their immediate boss, the interaction between the colleagues, and self-contribution of the employees. The proposed algorithm is benchmarked with 97 diverse test functions including 29 CEC-BC-2017 functions with very challenging landscapes against 7 highly-cited optimization algorithms including the winner of CEC-BC-2017 (EBO-CMAR). In the first two experiments, the exploitative and explorative behavior of HBO is evaluated by using 24 unimodal and 44 multimodal functions, respectively. It is shown through experiments and Friedman mean rank test that HBO outperforms and secures 1st rank. In the third experiment, we use 29 CEC-BC-2017 benchmark functions. According to Friedman mean rank test HBO attains 2nd position after EBO-CMAR;however, the difference in ranks of HBO and EBO-CMAR is shown to be statistically insignificant by using Bonferroni method based multiple comparison test. Moreover, it is shown through the Friedman test that the overall rank of HBO is 1st for all 97 benchmarks. In the fourth and the last experiment, the applicability on real-world problems is demonstrated by solving 3 constrained mechanical engineering optimization problems. The performance is shown to be superior or equivalent to the other algorithms, which have been used in the literature. The source code of HBO is publicly available at https://***/qamar-askari/HBO. (c) 2020 Elsevier Ltd. All rights rese

关键词： social optimization algorithm Corporate hierarchy based optimization Nature-inspired meta-heuristic Global optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid optimization enabled multi-aggregator-based charge scheduling of electric vehicle in internet of electric vehicles

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第9期35卷

作者： Suresh, Pandian Shobana, Selvaraj Ramya, Ganesan Ananth, Manasea Selvin Belsam Jeba St Joseph Coll Engn Dept EEE Chennai India Prathyusha Engn Coll Dept EEE Thiruvallur India SRM Inst Science&Technol Dept EEE Ramapuram Campus Chennai India SRM Inst Sci & Technol Dept Mechatron Engn Chengalpattu India St Joseph Coll Engn Chennai India

In modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time-consuming process. In this article, an effective charge scheduling model is devised using the fractional social sea lion optimization (Fr-SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator-based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr-SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr-SSLO model is designed by integrating fractional calculus (FC) and sea lion optimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr-SSLO approach. Moreover, various fitness functions are also considered for computing better performance.

关键词： charging scheduling electric vehicles fractional calculus sea lion optimization algorithm social optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Hybrid optimization enabled deep learning technique for multi-level intrusion detection

引用

ADVANCES IN ENGINEERING SOFTWARE 2022年第0期173卷

作者： Selvan, G. S. R. Emil Azees, M. Vinodkumar, CH. Rayala Parthasarathy, G. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai 625015 India VIT AP Univ Sch Comp Sci & Engn Amravati 522237 Andhra Pradesh India Estuate Software Serv Pvt Ltd Data Analyst Bangalore India REVA Univ Sch Comp & Informat Technol Bengaluru India

The intrusion detection system identifies the attack through the reputation and progression of network meth-odology and the Internet. Moreover, conventional intrusion recognition techniques usually utilize mining as-sociation rules for identifying intrusion behaviors. However, the intrusion detection model failed to extract typical information of user behaviors completely and experienced several issues, including poor generalization capability, high False Alarm Rate (FAR), and poor timeliness. This paper uses a hybrid optimization-based Deep learning technique for the multi-level intrusion detection process. First, the fisher score scheme is applied to extract the important features. Then, in the data augmentation the data size is increased. In this model, Rider optimization algorithm-Based Neural Network (RideNN) is employed for first level detection, where the data is categorized as normal and attacker. Besides, the RideNN classifier is trained by devised Rider social optimization algorithm (RideSOA). Additionally, the Deep Neuro Fuzzy network (DNFN) is utilized for the second level classification process in which attack types are categorized. Besides, the DNFN classifier is trained through devised social Squirrel Search algorithm (SSSA). The introduced intrusion detection algorithm outperformed with maximum precision of 0.9254, recall of 0.8362, and F-measure 0.8718.

关键词： Multi -level intrusion detection Deep Neuro Fuzzy Network Neural network Fisher score social optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：