Condition based Ensemble Deep Learning and Machine Learning Classification Technique for Integrated Potential Fishing Zone Future Forecasting

Artificial Intelligence (AI) technologies have become a popular application in order to improve the sustainability of smart fisheries. Although the ultimate objective of AI applications is often described as sustainability, there is yet no proof as to how AI contributes to sustainable fisheries. The proper monitoring of the longitudinal delivery of different human impacts on activities such as fishing is a major concern today in aquatic conservation. The term "potential fishing zone" (PFZ) refers to an anticipated area of any given sea where a variety of fish may congregate for some time. The forecast is made based on factors including the sea surface temperature (SST) and the sea superficial chlorophyll attentiveness. Fishing advisories are a by-product of the identification procedure. Normalization and preliminary processing are applied to these unprocessed data. The gathered attributes, together with financial derivatives and geometric features, are then utilised to make projections about IPFZ's Technique are used to get the final determination (CECT). In this study, we offer a technique for identifying and mapping fishing activity. Experimentations are performed to validate the efficacy of the CECT method in comparison to machine learning (ML) and deep learning (DL) methods across a variety of measurable parameters. Results showed that CECT obtained 94% accuracy, while Convolutional neural network only managed 92% accuracy on 80% training data and 20% testing data

Sparrow Search Algorithm based BGRNN Model for Animal Healthcare Monitoring in Smart IoT

Rural regions rely heavily on agriculture for their economic survival. Therefore, it is crucial for farmers to implement effective and technical solutions to raise production, lessen the impact of issues associated to animal husbandry, and improve agricultural yields. Because of technological developments in computers and data storage, huge volumes of information are now available. The difficulty of extracting useful information from this mountain of data has prompted the development of novel approaches and tools, such as data mining, that can help close the informational gap. To evaluate data mining methods and put them to use in the Animal database to create meaningful connections was the goal of the suggested system. The study's primary objective was to develop an IoT-based Integrated Animal Health Care System. Various sensors were used as the research tool to collect physical and environmental data on the animals and their habitats. Temperature, heart rate, and air quality readings were the types of information collected. This research contributes to the field of health monitoring by introducing an Optimised Bidirectional Gated Recurrent Neural Network approach. The BiGRNN is an improved form of the Gated Recurrent Unit (GRU) in which input is sent both forward and backward through a network and the resulting outputs are connected to the same output layer. Since the BiGRNN method employs a number of hyper-parameters, it is optimised by means of the Sparrow Search Algorithm (SSA). The originality of the study is demonstrated by the development of an SSA technique for hyperparameter optimisation of the BiGRNN, with a focus on health forecasting. Hyperparameters like momentum, learning rate, and weight decay may all be adjusted with the SSA method. In conclusion, the results demonstrate that the suggested tactic is more effective than the current methods

Frequency Reconfigurable Microstrip Patch Antenna for Multiband Applications with Shunt-Series MEMS Switch

The wireless communication system is well developed and lots of antennas are designed and fabricated for this application. Still, the evolution of the communication system, the performance of the antenna is required to enhance to adopt the present era. The design of the antenna is most important for the performance of the antenna. Therefore, this work is designed and developed a novel antenna design for wideband application by utilizing frequency reconfigurable technique. The proposed work utilized microstrip patch antenna for the application of wideband and the Shun-series MEMS switch is applied for switching the frequency. The proposed antenna is designed with two switches and investigated with the switching conditions like ON-ON, OFF-ON, and OFF-OFF. The performance of the proposed antenna is validated by utilizing the antenna performance metrices such as Return loss, bandwidth, gain, VSWR, and radiation pattern for each switching condition. The simulation results are shows that the proposed antenna design with shunt-series MEMS switch is effectively performed and it is most suitable for the application of wireless communication system.

Optimized Deep Belief Neural Network for Semantic Change Detection in Multi-Temporal Image

Nowadays, a massive quantity of remote sensing images is utilized from tremendous earth observation platforms. For processing a wide range of remote sensing data to be transferred based on knowledge and information of them. Therefore, the necessity for providing the automated technologies to deal with multi-spectral image is done in terms of change detection. Multi-spectral images are associated with plenty of corrupted data like noise and illumination. In order to deal with such issues several techniques are utilized but they are not effective for sensitive noise and feature correlation may be missed. Several machine learning-based techniques are introduced to change detection but it is not effective for obtaining the relevant features. In other hand, the only limited datasets are available in open-source platform; therefore, the development of new proposed model is becoming difficult. In this work, an optimized deep belief neural network model is introduced based on semantic modification finding for multi-spectral images. Initially, input images with noise destruction and contrast normalization approaches are applied. Then to notice the semantic changes present in the image, the Semantic Change Detection Deep Belief Neural Network (SCD-DBN) is introduced. This research focusing on providing a change map based on balancing noise suppression and managing the edge of regions in an appropriate way. The new change detection method can automatically create features for different images and improve search results for changed regions. The projected technique shows a lower missed finding rate in the Semantic Change Detection dataset and a more ideal rate than other approaches

Prediction of Alzheimer Disease using LeNet-CNN model with Optimal Adaptive Bilateral Filtering

Alzheimer's disease is a kind of degenerative dementia that causes progressively worsening memory loss and other cognitive and physical impairments over time. Mini-Mental State Examinations and other screening tools are helpful for early detection, but diagnostic MRI brain analysis is required. When Alzheimer's disease (AD) is detected in its earliest stages, patients may begin protective treatments before permanent brain damage has occurred. The characteristics of the lesion sites in AD affected role, as identified by MRI, exhibit great variety and are dispersed across the image space, as demonstrated in cross-sectional imaging investigations of the disease. Optimized Adaptive Bilateral filtering using a deep learning model was suggested as part of this study's approach toward this end. Denoising the pictures with the help of the suggested adaptive bilateral filter is the first stage (ABF). The ABF improves denoising in edge, detail, and homogenous areas separately. After then, the ABF is given a weight, and the Adaptive Equilibrium Optimizer is used to determine the best possible value for that weight (AEO). LeNet, a CNN model, is then used to complete the AD organization. The first step in using the LeNet-5 network model to identify AD is to study the model's structure and parameters. The ADNI experimental dataset was used to verify the suggested technique and compare it to other models. The experimental findings prove that the suggested method can achieve a classification accuracy of 97.43%, 98.09% specificity, 97.12% sensitivity, and 89.67% Kappa index. When compared against competing algorithms, the suggested model emerges victorious

Tom and Jerry Based Multipath Routing with Optimal K-medoids for choosing Best Clusterhead in MANET

Given the unpredictable nature of a MANET, routing has emerged as a major challenge in recent years. For effective routing in a MANET, it is necessary to establish both the route discovery and the best route selection from among many routes. The primary focus of this investigation is on finding the best path for data transmission in MANETs. In this research, we provide an efficient routing technique for minimising the time spent passing data between routers. Here, we employ a routing strategy based on Tom and Jerry Optimization (TJO) to find the best path via the MANET's routers, called Ad Hoc On-Demand Distance Vector (AODV). The AODV-TJO acronym stands for the suggested approach. This routing technique takes into account not just one but three goal functions: total number of hops. When a node or connection fails in a network, rerouting must be done. In order to prevent packet loss, the MANET employs this rerouting technique. Analyses of AODV-efficacy TJO's are conducted, and results are presented in terms of energy use, end-to-end latency, and bandwidth, as well as the proportion of living and dead nodes. Vortex Search Algorithm (VSO) and cuckoo search are compared to the AODV-TJO approach in terms of performance. Based on the findings, the AODV-TJO approach uses 580 J less energy than the Cuckoo search algorithm when used with 500 nodes

Telefacturing Based Distributed Manufacturing Environment for Optimal Manufacturing Service by Enhancing the Interoperability in the Hubs

Recent happenings are surrounding the manufacturing sector leading to intense progress towards the development of effective distributed collaborative manufacturing environments. This evolving collaborative manufacturing not only focuses on digitalisation of this environment but also necessitates service-dependent manufacturing system that offers an uninterrupted approach to a number of diverse, complicated, dynamic manufacturing operations management systems at a common work place (hub). This research presents a novel telefacturing based distributed manufacturing environment for recommending the manufacturing services based on the user preferences. The first step in this direction is to deploy the most advanced tools and techniques, that is, Ontology-based Protege 5.0 software for transforming the huge stored knowledge/information into XML schema of Ontology Language (OWL) documents and Integration of Process Planning and Scheduling (IPPS) for multijobs in a collaborative manufacturing system. Thereafter, we also investigate the possibilities of allocation of skilled workers to the best feasible operations sequence. In this context, a mathematical model is formulated for the considered objectives, that is, minimization of makespan and total training cost of the workers. With an evolutionary algorithm and developed heuristic algorithm, the performance of the proposed manufacturing system has been improved. Finally, to manifest the capability of the proposed approach, an illustrative example from the real-time manufacturing industry is validated for optimal service recommendation.This work has been supported by by COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

Development of Deep Learning based Intelligent Approach for Credit Card Fraud Detection

Credit card fraud (CCF) has long been a major concern of institutions of financial groups and business partners, and it is also a global interest to researchers due to its growing popularity. In order to predict and detect the CCF, machine learning (ML) has proven to be one of the most promising techniques. But, class inequality is one of the main and recurring challenges when dealing with CCF tasks that hinder model performance. To overcome this challenges, a Deep Learning (DL) techniques are used by the researchers. In this research work, an efficient CCF detection (CCFD) system is developed by proposing a hybrid model called Convolutional Neural Network with Recurrent Neural Network (CNN-RNN). In this model, CNN acts as feature extraction for extracting the valuable information of CCF data and long-term dependency features are studied by RNN model. An imbalance problem is solved by Synthetic Minority Over Sampling Technique (SMOTE) technique. An experiment is conducted on European Dataset to validate the performance of CNN-RNN model with existing CNN and RNN model in terms of major parameters. The results proved that CNN-RNN model achieved 95.83% of precision, where CNN achieved 93.63% of precision and RNN achieved 88.50% of precision