Fuzzy Expert System for Earthquake Prediction in Western Himalayan Range

Tehseen, Rabia; Farooq, Muhammad Shoaib; Abid, Adnan

doi:10.5755/j01.eie.26.3.25744

Cited by 12 publications

(7 citation statements)

References 30 publications

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“…These models were capable to numerically formulate non-linearity of flood due to their data-driven nature without requiring the knowledge of basic physical processes. It has been observed that data-driven models usually make faster predictions with minimal input using ML [47]. Multiple ML algorithms including decision tree (DT) [48], Random Forest (RF) [49] and Gradient Boost (GB) [50] have been applied to improve fault-tolerant accuracy in flood prediction handling more intricate information by applying their complex algorithms in a short time span [51].…”

Section: Related Workmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

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Floods are one of the most common natural disasters that occur frequently causing massive damage to property, agriculture, economy and life. Flood prediction offers a huge challenge for researchers struggling to predict floods since long time. In this article, flood forecasting model using federated learning technique has been proposed. Federated Learning is the most advanced technique of machine learning (ML) that guarantees data privacy, ensures data availability, promises data security, and handles network latency trials inherent in prediction of floods by prohibiting data to be transferred over the network for model training. Federated Learning technique urges for onsite training of local data models, and focuses on transmission of these local models on the network instead of sending huge data set towards central server for local model aggregation and training of global data model at the central server. In this article, the proposed model integrates locally trained models of eighteen clients, investigates at which station flooding is about to happen and generates flood alert towards a specific client with five days lead time. A local feed forward neural network (FFNN) model is trained at the client station where the flood has been expected. Flood forecasting module of local FFNN model predicts the expected water level by taking multiple regional parameters as input. The dataset of five different rivers and barrages has been collected from 2015 to 2021 considering four aspects including snow melting, rainfall-runoff, flow routing and hydrodynamics. The proposed flood forecasting model has successfully predicted previous floods happened in the selected zone during 2010 to 2015 with 84 % accuracy. INDEX TERMS hydraulic, meteorological, flood forecasting system, federated learning, feed-forward neural network.FIGURE 1. Natural hazards percentage as per occurrence in Pakistan during 2021 [6]

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Section: Related Workmentioning

confidence: 99%

FFM: Flood Forecasting Model Using Federated Learning

et al. 2023

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“…We have a "data mining" approach for Malicious Software identification and played out some test examination on malware location utilizing Logistic Regression Classifier. The objective of this work is to show genuine consequence of malware identification paces of (LR) [19] [20]. The LR filter is authorized to detect ambiguous examples of malware that may be 74 -83 percent.…”

Section: Related Workmentioning

confidence: 99%

Role of Logistic Regression in Malware Detection: A Systematic Literature Review

Farooq,

Akram,

Alvi

et al. 2022

VFAST trans. softw. eng.

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When brain, the first virus known introduced in computer systems, requirement of security was raised. Malware Detection turn out to be more vital when network is used for transferring Secret Information. Nowadays our central attributes i.e., Banking, Agriculture, Robotics, Virtual Social Life, Online Multiplayer Gaming, Private Conversations etc. is practicing internet and Malware will abolish everything if we discount it. Lots of new malwares are located by the passage of time, so we need a reliable, fast and trustworthy machine learning technique to handle them. Logistic Regression Classifier is useable for handling such a huge data, majorly counted in this paper. This is a complete SLR that delivers progressive approach in the field of malware detection. It legally reduces time and the cost of researchers. Limitations and future directions of machine learning classifiers to detect malwares are discussed in this paper.

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“…The main concern in mobile environment is energy or power. So the mechanisms adapt the applications and helps in saving the energy [9]. All the device which follows mobility have to be scheduling properly with time management mechanism for better efficiency.…”

Section: Distributed Systemmentioning

confidence: 99%

Pervasive Computing Issues, Vision an Exclusive Relationship Of Pervasive And Cloud Computing

Farooq,

Rabbani,

Alvi

et al. 2022

VFAST trans. softw. eng.

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Pervasive computing also known by the name of ubiquitous computing is a computer paradigm which focuses the integration of self-communicating and intelligent devices into user’s environment and daily life. Its major principle is to infuse computing into real world environment in such way that user cannot even realize it is existing. One of the best available source is mobile devices which can help with everything happening in real life. Mobile devices are not the only sources but they can be use to perform all the operations like downloading, uploading, computing the data, processing any type of data including audio video formats. The major issue could be the storage of such devices and the load taking to perform complex tasks. To tackle such issues, there is a need of cloud computing. Virtual Cloud computing could be the helpful as it provides the environment in which the larger data can be access pervasively. In this paper we will discuss how pervasive computing and cloud computing are associated with each other and how they can be beneficial to each other.

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Fuzzy Expert System for Earthquake Prediction in Western Himalayan Range

Cited by 12 publications

References 30 publications

FFM: Flood Forecasting Model Using Federated Learning

FFM: Flood Forecasting Model Using Federated Learning

Role of Logistic Regression in Malware Detection: A Systematic Literature Review

Pervasive Computing Issues, Vision an Exclusive Relationship Of Pervasive And Cloud Computing

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