Dynamic MODCOD and power allocation in DVB-S2: a hybrid intelligent approach

Rahman, Atta; Dash, Sujata; Luhach, Ashish Kr.

doi:10.1007/s11235-020-00700-x

Cited by 30 publications

(20 citation statements)

References 14 publications

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“…Machine learning evaluation parameters settings is a phase where the conduction of confirmation and validation of balanced datasets and acceptable nominated features befall. Moreover, the selection of advantageous requirements can turn ineffectual performance of a learning algorithm into something influential whenever it shows values indicating a proper circumstance of a system's criteria [ 35 – 39 ]. Here, we followed the main commonly used criteria to evaluate different machine learning algorithms on predicting malware.…”

Section: Resultsmentioning

confidence: 99%

ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques

Alhaidari

Rahman

2022

Computational Intelligence and Neuroscience

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For the enormous growth and the hysterical impact of undocumented malicious software, otherwise known as Zero-Day malware, specialized practices were joined to implement systems capable of detecting these kinds of software to avert possible disastrous consequences. Owing to the nature of developed Zero-Day malware, distinct evasion tactics are used to remain stealth. Hence, there is a need for advance investigations of the methods that can identify such kind of malware. Machine learning (ML) is among the promising techniques for such type of predictions, while the sandbox provides a safe environment for such experiments. After thorough literature review, carefully chosen ML techniques are proposed for the malware detection, under Cuckoo sandboxing (CS) environment. The proposed system is coined as Zero-Day Vigilante (ZeVigilante) to detect the malware considering both static and dynamic analyses. We used adequate datasets for both analyses incorporating sufficient samples in contrast to other studies. Consequently, the processed datasets are used to train and test several ML classiﬁers including Random Forest (RF), Neural Networks (NN), Decision Tree (DT), k-Nearest Neighbor (kNN), Naïve Bayes (NB), and Support Vector Machine (SVM). It is observed that RF achieved the best accuracy for both static and dynamic analyses, 98.21% and 98.92%, respectively.

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Section: Resultsmentioning

confidence: 99%

ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques

Alhaidari

Rahman

2022

Computational Intelligence and Neuroscience

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“…The disadvantage of a neural network is that it is difficult to observe or grasp what happens within the network structure after applying the model on a set of inputs [27]. There are several applications of ANN in various areas of study like classification [28], security [29][30][31], prediction [32], estimation [33], engineering [34][35][36], healthcare [37][38][39] and many more [40].…”

Section: The Proposed Techniques 31 Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Empowered Electricity Consumption Prediction

Metrik¹,

Musleh²

2022

Computers, Materials &Amp; Continua

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Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes a medium-term prediction model that can predict electricity consumption for a given location in Saudi Arabia. Hence, this study implemented a standalone Artificial Neural Network (ANN) model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia. The dataset used in this research is gathered exclusively from the Saudi Electric Company. The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets. The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning. Finally, the standalone model was tested against the bagging ensemble using the optimized ANN. The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model. The results for the proposed model produced 0.9116 Correlation Coefficient (CC), 0.2836 Mean Absolute Percentage Error (MAPE), 0.4578, Root Mean Squared Percentage Error (RMSPE), 0.0298 MAE, and 0.069 Root Mean Squared Error (RMSE), respectively.

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“…USA government prefers to its doctors to use electronic way for fast processing. Figure 12 shows that in Utah (UT) state, the top Dx code for the month of July was R41.844 that corresponds to Frontal lobe and executive function deficit disease [31][32][33][34][35][36][37][38][39][40].…”

Section: Monthly Analysismentioning

confidence: 99%

Geo-Spatial Disease Clustering for Public Health Decision Making

Rahman

2022

IJCAI

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An explosion of interest has been observed in disease mapping with the developments in advanced spatial statistics, data visualization and geographic information system (GIS) technologies. This technique is known as "Geo-Spatial Disease Clustering," mainly used for visualization and future disease expansion prediction. Its importance has been overwhelmingly observed since the COVID-19 pandemic outbreak. Government, Medical Institutes, and other medical practices gather large amounts of data from surveys and other sources. This data is in the form of notes, databases, spread sheets and text data files. Mostly this information is in the form of feedback from different groups like age group, gender, provider (doctors), region, etc. Incorporating such heterogeneous nature of data is quite challenging task. In this regard, variety of techniques and algorithms have been proposed in the literature, but their effectiveness varies due to data types, volume, format and structure of data and disease of interest. Mostly, the techniques are confined to a specific data type. To overcome this issue, in this research, a data visualization technique combined with data warehousing and GIS for disease mapping is proposed. This includes data cleansing, data fusion, data dimensioning, analysis, visualization, and prediction. Motivation behind this research is to create awareness about the disease for the guidance of patients, healthcare providers and government bodies. By this, we can extract information that describes the association of disease with respect to age, gender, and location. Moreover, the temporal analysis helps earlier prediction and identification of disease, to be care of and necessary avoiding arrangements can be taken. Povzetek: Analizirana je vloga vizualizacije podatkov in rešitev za naloge prostorskega gručenja bolezni, tj. ugotavljanje in napovedovanje širjenja bolezni.Informatica 46 (2022) 21-31 A.u. Rahman et al.results and discussions, while section 5 concludes the paper.

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Dynamic MODCOD and power allocation in DVB-S2: a hybrid intelligent approach

Cited by 30 publications

References 14 publications

ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques

ZeVigilante: Detecting Zero-Day Malware Using Machine Learning and Sandboxing Analysis Techniques

Machine Learning Empowered Electricity Consumption Prediction

Geo-Spatial Disease Clustering for Public Health Decision Making

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