A Classification Algorithm-Based Hybrid Diabetes Prediction Model

Onyema, Edeh Michael; Khalaf, Osamah Ibrahim; Tavera, Carlos A.; Tayeb, Sofiane; Ghouali, Samir; Abdulsahib, Ghaida Muttashar; Richard-Nnabu, Nneka Ernestina; Louni, AbdRahmane

doi:10.3389/fpubh.2022.829519

Cited by 39 publications

(18 citation statements)

References 17 publications

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“…In the future, we intend to design an intelligent App to provide support for people diagnosed with breast cancer and also more awareness about the disease, particularly in the rural areas. [34][35][36][37][38][39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets

Kharya

Onyema

Zafar

et al. 2022

Computational Intelligence and Neuroscience

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There are growing concerns about the mortality due to Breast cancer many of which often result from delayed detection and treatment. So an effective computational approach is needed to develop a predictive model which will help patients and physicians to manage the situation timely. This study presented a Weighted Bayesian Belief Network (WBBN) modeling for breast cancer prediction using the UCI breast cancer dataset. New automated ranking method was used to assign proper weights to attribute value pair based on their impact on causing the disease. Association between attributes was generated using weighted association rule mining between two attributes, multiattributes, and with class labels to generate rules. Weighted Bayesian confidence and weighted Bayesian lift measures were used to produce strong rules to build the model. To build WBBN, the Open Markov tool was used for structure and parametric learning using generated strong rules. The model was trained using 70% records and tested on 30% records with a threshold value of minimum support = 36% and confidence = 70% which produced results with an accuracy of 97.18%. Experimental results show that WBBN achieved better results in most cases compared to other predictive models. The study would contribute to the fight against breast cancer and the quality of treatment.

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Section: Discussionmentioning

confidence: 99%

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets

Kharya

Onyema

Zafar

et al. 2022

Computational Intelligence and Neuroscience

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“…All strategies used to lower energy-specific hardware components/levels are covered in extreme detail. There is much emphasis on techniques deployed at the hardware-level (network-or server-level) that can lead to energy-efficient or ecologically friendly data centers [122][123][124][125][126][127][128][129][130][131][132].…”

Section: Related Surveysmentioning

confidence: 99%

“…Ref. [S30] also proved that optimization is equally significant while working on energy efficiency [130][131][132]. They proposed a method for scheduling VM workflows in hybrid and private clouds in which pre-power techniques and the least-load-first algorithms were used to make it energy efficient.…”

Section: Rq5: Describe Various Energy Efficiency Techniques Employed ...mentioning

confidence: 99%

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A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

et al. 2022

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Global warming is one of the most compelling environmental threats today, as the rise in energy consumption and CO2 emission caused a dreadful impact on our environment. The data centers, computing devices, network equipment, etc., consume vast amounts of energy that the thermal power plants mainly generate. Primarily fossil fuels like coal and oils are used for energy generation in these power plants that induce various environmental problems such as global warming ozone layer depletion, which can even become the cause of premature deaths of living beings. The recent research trend has shifted towards optimizing energy consumption and green fields since the world recognized the importance of these concepts. This paper aims to conduct a complete systematic mapping analysis on the impact of high energy consumption in cloud data centers and its effect on the environment. To answer the research questions identified in this paper, one hundred nineteen primary studies published until February 2022 were considered and further categorized. Some new developments in green cloud computing and the taxonomy of various energy efficiency techniques used in data centers have also been discussed. It includes techniques like VM Virtualization and Consolidation, Power-aware, Bio-inspired methods, Thermal-management techniques, and an effort to evaluate the cloud data center’s role in reducing energy consumption and CO2 footprints. Most of the researchers proposed software level techniques as with these techniques, massive infrastructures are not required as compared with hardware techniques, and it is less prone to failure and faults. Also, we disclose some dominant problems and provide suggestions for future enhancements in green computing.

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“…Depending on the forecast, the WUSN middleware develops a storage approach. Additionally, the storage method is matched up with these storage servers so that preemptive storage can happen over time [31].…”

mentioning

confidence: 99%

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

Kanthavel

Dhaya

Khalaf

et al. 2022

Sensors

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Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Cooperative communication channel model for Wireless Underground Sensor Networks for accurate and reliable monitoring in hostile underground locations. Furthermore, the proposed communication model aims at the effective utilization of cluster-based Cooperative models through the relay nodes. However, by keeping the cost effectiveness, reliability, and user-friendliness of wireless underground sensor networks through inter-cluster Cooperative transmission between two cluster heads, the determination of the overall energy performance is also measured. The energy co-operative channel allocation routing (ECCAR), Energy Hierarchical Optimistic Routing (EHOR), Non-Cooperative, and Dynamic Energy Routing (DER) methods were used to figure out how well the proposed WUSN works. The Quality of Service (QoS) parameters such as transmission time, throughput, packet loss, and efficiency were used in order to evaluate the performance of the proposed WUSNs. From the simulation results, it is apparently seen that the proposed system demonstrates some superiority over other methods in terms of its better energy utilization of 89.71%, Packet Delivery ratio of 78.2%, Average Packet Delay of 82.3%, Average Network overhead of 77.4%, data packet throughput of 83.5% and an average system packet loss of 91%.

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A Classification Algorithm-Based Hybrid Diabetes Prediction Model

Cited by 39 publications

References 17 publications

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets

Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets

A Systematic Survey on Energy-Efficient Techniques in Sustainable Cloud Computing

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks

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