Search Results - (( a classification using algorithm ) OR ( media applications testing algorithm ))

Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad by Ahmad, Nurul Atirah

“…The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. …”
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Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data by Mohammad Aman Ullah

Cyberbullying detection: a machine learning approach by Yeong, Su Yen

“…Those algorithms are used in the classification or regression model to predict an input. …”
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Classification and visualization on eligibility rate of applicant’s LinkedIn account using Naïve Bayes / Nurul Atirah Ahmad, Khyrina Airin Fariza Abu Samah and Nuwairah Aimi Ahmad... by Ahmad, Nurul Atirah, Abu Samah, Khyrina Airin Fariza, Ahmad Kushairi, Nuwairah Aimi

“…This project implements the Naive Bayes algorithm as the classification algorithm. The classification is set to two categories: Eligible or Ineligible. …”
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Image authentication using Zernike moment watermarking by Shojanazeri, Hamid

“…Using two different watermarks lead to a good classification of incidental and malicious modifications and locating the tampered areas. …”
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Hybrid binary whale with harris hawks for feature selection by Alwajih, R., Abdulkadir, S.J., Al Hussian, H., Aziz, N., Al-Tashi, Q., Mirjalili, S., Alqushaibi, A.

“…To determine the significance of BWOAHHO, a statistical one-way ANOVA test was used. When compared to existing algorithms, the proposed approach produced better results. …”
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Performance Review of Modern AI Algorithms Utilized for Medical Waste Sorting Works by Moktar M.H., Hajjaj S., Mohamed H., Weng L.Y.

Sentiment analysis for airline services on Twitter using deep learning with word embedding / Mawada Mohamed Nour El Daim El Khalifa by Mawada Mohamed , Nour El Daim El Khalifa

“…The role of Sentiment Analysis (SA) is to classify people's opinions into different categories, such as positive and negative from text, using existing algorithms. However, existing approaches such as the Bag of Words (BOW) model is frequently used for text classification, where a document is mapped to a feature vector before the construction of the actual model, using machine learning techniques, like Logistical Regression and Support Vector algorithms. …”
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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim

“…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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Robust tweets classification using arithmetic optimization with deep learning for sustainable urban living by Hamza, Manar Ahmed, Hassan Abdalla Hashim, Aisha, Motwakel, Abdelwahed, Elhameed, Elmouez Samir Abd, Osman, Mohammed, Kumar, Arun, Singla, Chinu, Munjal, Muskaan

“…At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. …”
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Medical Waste Detection and Classification Through YOLO Algorithms by Moktar M.H.B., Hajjaj S.S.H., Mohamed H.

Design and implementation of a deep learning based hand gesture recognition system for Rehabilitation Internet-Of-Things (RIOT) environments using MediaPipe by Mohd Dhuzuki, Nurul Hanis, Zainuddin, Ahmad Anwar, Kamarul Zaman, Nur Anis Sofea, Ahmad Razmi, Alin Nur Maisarah, Kaitane, Wonderful Shammah, Ahmad Puzi, Asmarani, Johar, Mohd Naqiuddin, Yazid, Maslina, Mohd Nordin, Nor Azlin, Sidek, Shahrul Naim, Mohd Zaki, Hasan Firdaus

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Segmentation Word to Improve Performance Sentiment Analysis for Indonesian Language by Siti Mujilahwati, Siti Mujilahwati, M. Safar, Noor Zuraidin, Supriyanto, Catur

“…Subsequently, sentiment analysis classification testing was conducted using both deep learning and machine learning models to assess data quality and accuracy. …”
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Machine learning classification model for identifying internet addiction among university students by Tan, Hui Theng, Mun, Hou Kit, Handayani, Dini Oktarina Dwi

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Distributed Multi-Feature Recognition Scheme for Greyscale Images by Muhamad Amin , Anang Hudaya, Khan, Asad I.

“…Our scheme is able to disseminate recognition of each feature within an image to a separate computational subnetwork. Thereby allowing a number of features being analysed simultaneously using a uniform recognition process. …”
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Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction by Sase, Takumi, Othman, Marini

“…In this paper, we propose an adaptive EEG feature extraction approach using TBR and PCA. Repeated TBR-PCA feature extraction, SVM classification and statistical testing were applied on a small EEG sample with ADHD/typically developing (TD) labels. …”
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Fuzzy Evaluation and Benchmarking Framework for Robust Machine Learning Model in Real-Time Autism Triage Applications by Shayea G.G., Zabil M.H.M., Albahri A.S., Joudar S.S., Hamid R.A., Albahri O.S., Alamoodi A.H., Zahid I.A., Sharaf I.M.

Translating conventional wisdom on chicken comb color into automated monitoring of disease-infected chicken using chromaticity-based machine learning models by Bakar M.A.A.A., Ker P.J., Tang S.G.H., Baharuddin M.Z., Lee H.J., Omar A.R.

Dengue classification system using clonal selection algorithm / Karimah Mohd by Mohd, Karimah

“…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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Classification of tropical rainforest using different classification algorithm based on remote sensing imagery: A study of Gunung Basor by Intan Noradybah Md Rodi

“…Thehighest accuracy for classification map of Gunung Basor is by using maximum likelihood algorithm with an accuracy of 82.90%. …”
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