Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Lakshmanna, Kuruva; Neelakandan, S.; Alotaibi, Youseef; Alghamdi, Saleh; Khalafand, Osamah Ibrahim; Nanda, Ashok Kumar

doi:10.3390/su14137712

Cited by 106 publications

(51 citation statements)

References 70 publications

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“…Literature [27] framed the scope of children's psychological adaptation in combination with the particularity of children: emotional experience, interpersonal experience, self-evaluation, and reaction style. Literature [28] summarizes a large number of research results and proposes that when examining the psychological adaptation of individuals, aspects such as loneliness, depression, anxiety, subjective well-being, positive and negative emotions, and subjective vitality are often used as research dimensions. Reference [29] divides adaptation into the following categories from different perspectives.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Analysis of Professional Psychological Adaptability of Students Majoring in Hotel Management and Digital Operation for Higher Vocational Education under Deep Learning

2022

Wireless Communications and Mobile Computing

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Under the background of rapid and comprehensive development of society and technology, the accommodation industry has shown a relatively prosperous development trend, which has promoted the reform and development of the hotel industry. The scale, quantity, and mode of hotel enterprises have undergone great changes, and there has been a structural surplus. Among them, the most obvious change is the integration of digital technology, which integrates technology and hotel services to form a new hotel format. Combined with a series of policy encouragement trends promulgated by the state, the hotel management and digital operation majors in higher vocational education are more targeted for the cultivation of talents, which can cultivate talents with innovative ability and industry skills in the hotel industry. In this context, it is very important to carry out an adaptive analysis on the professional psychology of students majoring in hotel management and digital operations, which can tap their potential and promote the improvement of teaching quality. Aiming at this subject, this work proposes a neural network (PPANet) for analyzing the psychological adaptability of students in this major. Aiming at the problem that CNN cannot capture the periodicity and trend of data, this work proposes a multiscale deep residual network. The network adds convolution kernels with different scales to learn data features and enhances generalization ability and improves accuracy through residual short-circuit structure and deep structure. Furthermore, based on ReLU, this work proposes an improved IReLU activation function with better learning properties. The systematic experiment verifies the feasibility of applying PPANet to the analysis of professional psychological adaptability of students majoring in hotel management and digital operations.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Analysis of Professional Psychological Adaptability of Students Majoring in Hotel Management and Digital Operation for Higher Vocational Education under Deep Learning

2022

Wireless Communications and Mobile Computing

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“…For solving global optimization problems, the maximum number of iterations is 500, but the maximum number of iterations when solving FS problems is thirty. Finally, the population size for global optimization problems is set to fifty, whereas the population size for FS problems is set to twenty [28][29][30][31]. Initially, the search agents are assigned in a random manner.…”

Section: Parameter Initializationmentioning

confidence: 99%

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets

Banu

Neelakandan

Geetha

et al. 2022

Computational Intelligence and Neuroscience

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The introduction of artificial intelligence (AI) and machine learning (ML) technologies in recent years has resulted in improved company performance. Customer churn forecast is a difficult problem in many corporate sectors, particularly the telecommunications industry. Because customer churns have a direct impact on a company's total revenue, telecommunications firms have begun to develop 76 models to reduce churns at an earlier stage. Previous research has revealed that AI and ML models are effective CCP solutions. According to this viewpoint, this study proposes a unique AI-based CCP model for Telecommunication Business Markets (AICCP-TBM). The AICCP-TBM model's purpose is to control the existence of churners and non-churners in the telecom sector. The proposed AICCP-TBM model employs a Chaotic Salp Swarm Optimization-based Feature Selection (CSSO-FS) method for the best feature assortment. In addition, a Fuzzy Rule-based Classifier(FRC) is used to distinguish between client churners and non-churners. A technique known as Quantum Behaved Particle Swarm Optimization (QPSO) is used to pick the membership functions for the FRC model in order to improve the classification performance of the FRC model. The performance of the AICCP-TBM model is validated using a benchmark CCP dataset and the experimental results are reviewed from several angles. In relations of presentation, the imitation consequences demonstrated that the AICCP-TBM model surpassed the most recent state-of-the-art CPP models. The suggested AICCP-TBM method's comparative accuracy was thoroughly tested on the three datasets used. Using datasets 1-3, this technique obtained better levels of accuracy, with the maximum attainable values being 97.25 %, 97.5 % and 94.33 %. The simulation results for the AICCP-TBM model demonstrated improved prediction performance.

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“…Innovative technologies are helping the plurality of people everywhere the experience in a type of ways. The Internet of Things (IoT) and dossier data, such as grown dossier data and data learning, are immediately playing a more and more critical duty in people's everyday lives, admitting them to change their environment more surely [1][2][3]. In general, IoTs and data reasoning are secondhand in the agromodern and environmental subdivisions for two together diagnostics and control of brilliant culture arrangements, to provide essential facts to the final laborer and services about the footing and properties of agroproduction and structures [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

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Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Cited by 106 publications

References 70 publications

Analysis of Professional Psychological Adaptability of Students Majoring in Hotel Management and Digital Operation for Higher Vocational Education under Deep Learning

Analysis of Professional Psychological Adaptability of Students Majoring in Hotel Management and Digital Operation for Higher Vocational Education under Deep Learning

Artificial Intelligence Based Customer Churn Prediction Model for Business Markets

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

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