Search Results - probable estimation ((using algorithm) OR (((learning algorithm) OR (learning algorithms))))

Applying learning to filter text by Sainin, Mohd Shamrie

“…Text filtering has been a successful application especially in e-mail filtering. The use of probabilistic approaches such as naïve Bayes algorithm is the effective algorithms currently known for learning to filter or classify text document.Naïve Bayes algorithm is one of the algorithms in Machine Learning that manipulates probability estimation or reasoning about the observed data.The growing of bulk e-mail or known as spam e-mail becomes a threat to users’ privacy and network load and in the case of e -mail filtering,naïve Bayes classifier can be trained to automatically detect spam messages.Similar to the e-mail, forum application may be misused by the user to send bad messages and in some extent may offence other readers.Forum filtering may be less important compared to e-mail spam filtering; however there is a possibility of using naïve Bayes to learn the messages and automatically detect bad messages.Most of the forum application found in the web is applying keyword based text filtering which scan the words and change the detected words into certain representation.Instead of defining a set of keywords to filter the forum messages, this paper will explains the experiment in applying a learning to filter text especially in the educational and anonymous forum message, where there is no user registration required to submit messages.…”
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Towards a Malay derivational lexicon: learning affixes using expectation maximization by Sulaiman, Suriani, Gasser, Michael, Kubler, Sandra

“…Using a morphology-based language model, we first estimate the probability of a given segmentation. …”
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Revolutionizing Perimeter Intrusion Detection: A Machine Learning-Driven Approach with Curated Dataset Generation for Enhanced Security by Pitafi, S., Anwar, T., Dewa Made Widia, I., Yimwadsana, B.

“…To overcome these issues in this algorithm, we have improved the existing DBSCAN to ST-DBSCAN where we have used the estimation for the epsilon value and used the Manatton distance formula to find out the distance between points which produces 94.9853 accuracy on our dataset. …”
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Bayesian Network of Traffic Accidents in Malaysia by Zamzuri, Zamira Hasanah, Shabadin, Akmalia, Ishak, Siti Zaharah

“…By using Hill Climb (HC) and Tabu algorithms, the structure of the data was learnt and their relationship is estimated through the conditional probability based on the Bayes theorem. …”
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New learning algorithm based on Hidden Markov Model (HMM) as stochastic modelling for pattern calssification by Balakrishnan, Malarvili, Ting, Chee Ming, Shaikh Salleh, Sheikh Hussain

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Affective computation on EEG correlates of emotion from musical and vocal stimuli by Khosrowabadi, Reza, Abdul Rahman, Abdul Wahab, Ang, Kai Keng, H Baniasad, Mohammad.

“…A classification algorithm is subsequently used to learn and classify the extracted EEG features. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Therefore, feature selection using Relief-f with self-adaptive Differential Evolution (rsaDE) algorithm is proposed to select the most significant features. …”
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Application of Machine Learning and Deep Learning Algorithms for Landslide Susceptibility Assessment in Landslide Prone Himalayan Region by Bhattacharya S., Ali T., Chakravortti S., Pal T., Majee B.K., Mondal A., Pande C.B., Bilal M., Rahman M.T., Chakrabortty R.

“…This study employs various machine learning and deep learning algorithms, specifically Random Forest (RF), Artificial Neural Network (ANN), and Deep Learning Neural Network (DLNN), to estimate landslide susceptibility in Chamoli district, Uttarakhand, India?…”

A New Probabilistic Output Constrained Optimization Extreme Learning Machine by Wong S.Y., Yap K.S., Li X.C.

Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets by Saeed, Sana

“…The key idea of this methodology is to estimate the probabilities for each case in both classes separately. …”
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Decision Support Tools: Machine Learning Application in Smart Planner by Baharom, M.A.A., Rahman, M.S.A., Sabudin, A.R., Nor, M.F.M.

“…Immaculate Project Planning and Execution (PPE) is capital to edge over competitors, decrease costs and honour delivery dates.Project Management Information System (PMIS) is necessary towards an improved and efficient quality of any project.Machine Learning (ML) Algorithms enabled learned the date of experience to develop insights into various associations between data and outcomes.A defined set of rules prescribed by the analysts makes the probability of the fault possible.In this paper, Regression Model compute across all viable sectors expending the tool for Downstream Business and other Facilities Upstream, including Resource Estimation Schedule Generation.Extending structured information into a reliable database allows super users to define the data structures and completely configurable the settingâ��s dynamics.The model used to decrease the approximation error and measure the closest possible outcome.This subset of artificial intelligence has tremendous potential in improving schedule generation configuration to develop Project Planning timely and financially smartly.This paper aims to share standard protocols and methods applied in ML-aided as a tool in PPE decision making.Additionally, the abundant used data resources devoted to implementing ML are outlined.Finally, ML success as a Decision Support tool in project management by having a Smart Planner in supporting project recommendation accelerates the decision process, increases stakeholder confidence, and minimizes uncertainty; results are reviewed and analyzed where gaps and potential improvement for future projects are being noted and highlighted. …”
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Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting by YAXING, WEI, HUZAIFA, HASHIM, Lai, Sai Hin, CHONG, KAI LUN, HUANG, YUK FENG, ALI NAJAH, AHMED, MOHSEN, SHERIF, AHMED, EL-SHAFIE

“…Deep learning excels at managing spatial and temporal time series with variable patterns for streamflow forecasting, but traditional machine learning algorithms may struggle with complicated data, including non-linear and multidimensional complexity. …”
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ESTIMATED PROBABILITY OF THE NUMBER OF BUILDINGS DAMAGED BY THE FLOODS AND THE COST OF REPAIRS by Mohd Khairul Amri, Kamarudin, Abdul Rahman, Hasan, Fathurrahman, Lananan

“…The probability estimation of building damage was carried out using a logistic regression model with a genetic algorithm approach. …”
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Estimation of Transformers Health Index Based on Condition Parameter Factor and Hidden Markov Model by Selva A.M., Yahaya M.S., Azis N., Ab Kadir M.Z.A., Jasni J., Yang Ghazali Y.Z.

“…Electric transformers; Health; Hidden Markov models; Nonlinear programming; Probability distributions; Quality control; Viterbi algorithm; Condition parameters; Dissolved gas analysis; Distribution transformer; Emission probabilities; Health indices; Non-linear optimization; Remaining useful lives; Transition probabilities; Parameter estimation…”

Simulation algorithm of bayesian approach for choice-conjoint model by Zulhanif

“…Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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Performance of MIMO space-time coded system and training based channel estimation for MIMO-OFDM system by Abdolee, Reza

“…At the next stage of the project, the effect of channel estimation algorithm on performance of MIMO-OFDM system was investigated. …”
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Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

“…These algorithms are inspired by the estimation capability of the well-known Kalman filter estimation method. …”
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Tree-based contrast subspace mining method by Florence Sia Fui Sze

“…CSMiner and CSMiner-BPR) use density-based likelihood contrast scoring function to estimate the likelihood of a query object to target class against other class in a subspace. …”
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Improved undetected error probability model for JTEC and JTEC-SQED coding schemes by Flayyih, Wameedh Nazar, Samsudin, Khairulmizam, Hashim, Shaiful Jahari, Rokhani, Fakhrul Zaman, Ismail, Yehea I.

“…The probabilities of the failing patterns are used to build the new models. …”
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Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation by Rahimizadeh, Hamid

“…The estimation of unknown PDF is a common problem and in this study Gaussian kernel function which is most widely used nonparametric density estimation method has been used for PDF calculation. …”
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