A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network...
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A mobile ad hoc network (MANET) is an independent wireless temporary network established by employing a set of mobile nodes (i.e. laptops, smartphones, iPods, etc.) appropriate for the environment in which the network infrastructures are not fixed. The most common problems faced by MANET are energy efficiency, high energy consumption, low network lifetime as well as high traffic overhead which create an impact on overall network topology. Hence, it is necessary to provide an energy-effective CH election to take steps against such issues. Therefore, this paper proposes a novel model to enhance the network lifetime and energy efficiency by performing a routing strategy in MANET. In this paper, an optimal CH is selected by proposing a novel Fuzzy Marine White Shark optimization (FMWSO) algorithm which is obtained by integrating fuzzy operation with two optimization algorithms namely the marine predator algorithm and white shark optimizer. The proposed approach comprises three diverse stages namely Generation of data, Cluster Generation and CH selection. A novel FMWSO algorithm is proposed in such a way to determine the CH selection in MANET thereby enhancing the network topology, network lifetime and minimizing the overhead rate, and energy consumption. Finally, the performance of the proposed FMWSO approach is compared with various other existing techniques to determine the effectiveness of the system. The proposed FMWSO approach consumes minimum energy of 0.62 mJ which is lower than other approaches.
Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarit...
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Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire *** recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining *** cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival *** analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection *** upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and ***,the histopathology biopsy images are taken from standard data ***,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are ***,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer *** efficacy of the model is evaluated using divergent *** compared with other methods,the proposed work reveals that it offers impressive results for detection.
Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices expand, network vulnerabilities may rise, leading to more netwo...
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Approximately 20% of the world’s population suffers from mental health disorders. Despite this, resources for mental health around the world remain scarce, inequitable, and inefficient. With the rapid development of ...
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Vehicular ad hoc networks (VANETs) role a vital play in allowing technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs transmit real-time data on their movement, traffic, and...
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Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced...
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The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towar...
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The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towards the center of the Pareto front due to inadequate selection forces. The study proposes the utilization of a novel approach known as MOEA/D, which partitions complex multi-objective problems into smaller, more feasible single-objective sub-problems. Each sub-problem may then be addressed using an equal amount of computational resources. The predetermined size of the neighborhood used by MOEA/D may lead to a delay in the algorithm's merging and reduce the effectiveness of the failure. The paper proposes the Adaptive Neighbourhood Adjustment Strategy (ANAS) as a novel approach to improve the efficiency of multi-objective optimisation algorithms in order to tackle this issue. The ANAS algorithm allows for adaptive adjustment of the subproblem neighborhood size, hence enhancing the trade-off between merging and variety. In the following section of the study, a novel feature selection technique called MOGHHNS3/D-ANA is introduced. This technique utilizes ANAS to expand the potential solutions for a particular subproblem. The approach evaluates the chosen features using the Regulated Extreme Learning Machine (RELM) classifier on sixteen benchmark datasets. The experimental results demonstrate that MOGHHNS3/D-ANA outperforms four commonly employed multi-objective techniques in terms of accuracy, precision, recall, F1 score, coverage, hamming loss, ranking loss, and training time, error. The APBI approach in decomposition-based multi-objective optimization focuses on handling constraints by adjusting penalty parameters to guide the search towards feasible solutions. On the other hand, the ANA approach focuses on dynamically adjusting the neighborhood size or search direction based on the proximity of solutions in the detached space to adapt the search process.
The uneven growth in internet technologies and electronic information is giving birth to the information overload. Diverse algorithms handle the problem of information overload with uneven outcomes. Recent trends in r...
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Cloud providers frequently utilize two tightly coupled resource management strategies like task scheduling & data replication to boost the performance of the system generally, guaranteeing service level agreement ...
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Perusing web data items such as shopping products is a core online user activity. To prevent information overload, the content associated with data items is typically dispersed across multiple webpage sections over mu...
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