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Information and Communication Technology for Sustainable Development (ICICT4SD), International Conference on

作者： Md. Iqbal Hossain Maqsudur Rahman Tofael Ahmed A. Z. M. Touhidul Islam Port City International University Chattogram Bangladesh Comilla University Cumilla Bangladesh University of Rajshahi Rajshahi Bangladesh

ISBN: (数字)9781665414609

ISBN: (纸本)9781665447522

An online product's rating is an essential metric to understand the acceptability of that product to users. Shoppers use the rating to measure the quality and excellence of the online product. It helps an online shopper to decide to buy a product or not. It also helps the producer to further modification of that product during reproduction. Sometimes people buy a product online and give a text review of buying products but apathy towards giving a number rating, commonly a star rating. But producers need to know the rating of products for analysis of their business. Producers can use this rating for business analysis and can drive better revenue to their business. We have used some supervised machine learning approach to predict rating based on customer text review and compared the results between Random Forest Classifier, XGBoost and Logistic Regression algorithm with TF-IDF Vectorizer from extensive and series of experiments. We applied the algorithms mentioned above on the dataset named “GrammarandProductReviews” provided by Datafiniti. We analyzed the performance of each algorithm through the accuracy, precision, recall and f1-score. From the study, it is observed that Random Forest algorithm gained accuracy of 94% and precision, recall and f1-scores are 0.94, 0.94, and 0.94 respectively, which showed best compared to others.

关键词： Measurement machine learning algorithms Prediction algorithms Classification algorithms Sustainable development Random forests Business

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Skin Disease Classification using machine learning algorithms

Skin Disease Classification using Machine Learning Algorithm...

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Communication, Computing and Industry 4.0 (C2I4), International Conference on

作者： K. Veera Swamy B. Divya Vasavi College of Engineering Ibrahimbagh Hyderabad India

ISBN: (纸本)9781665420143

Skin diseases are the large number of spread diseases in the world. Their diagnoses are very difficult because of its difficulties in skin texture, presence of hair on skin and color. It is required to develop methods like machine learning in order to increase the accuracy of diagnosis for various types of skin diseases. machine learning techniques are widely used in medical fields for diagnosis. These algorithms use feature values from images as input to make a decision. The process consists of three stages-The feature extraction stage, the training stage and the testing stage. The process makes use of machine learning technology to train itself with the various skin images. The objective of this process is to increase accuracy of skin disease detection. Three important features in image classification are texture, color, shape, and combination of these. In this work, color and texture features are used to classify the skin disease. Normal skin color is different from the skin with disease. Smoothness, coarseness, and regularity is effectively identified using texture features in the images. Hence, these two features are explored to identify skin disease effectively. In this work, entropy, variance and maximum histogram value of Hue-Saturation-Value(HSV) features are used. These features are used to build machine learning algorithm by using Decision Tree(DT) and Support Vector machine(SVM). At first level, entropy measure is used to split the tree. At second level, variance is used to get leafs for textures. In color features, maximum histogram value of HSV measure is used to split the tree. Accuracy is used to test the performance of the proposed algorithm.

关键词： Support vector machines Histograms machine learning algorithms Image color analysis machine learning Feature extraction Skin

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Prediction of protein–peptide-binding amino acid residues regions using machine learning algorithms

Prediction of protein–peptide-binding amino acid residues r...

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International CSI Computer Conference, CSICC

作者： Shima Shafiee Abdolhossein Fathi RAZI UNIVERSITY Kermanshah Iran

In bioinformatics, it remains challenging to predict important amino acid residues for the binding amino acid residues regions and to perform binding region-based protein interactions. The present method focused on predicting protein-peptide binding amino acid residues regions using various distinct feature groups. Therefore, we employed machine learning methods to predict the protein-peptide binding amino acid residues and protein-peptide binding amino acid residues regions. Thus, predicting peptide-binding aminoacid residues regions computationally is useful to improve the efficiency and cost-effectiveness of experimental methods. The proposed method has three phases:pre-processing with normalization, processing with classification algorithm, and post-processing with a clustering algorithm. The proposed machine learning method of SVM+OPTICS achieves robust and consistent results for the prediction of protein-peptide-binding amino acid residues regions in terms of amino acid residues and regions.

关键词： Proteins machine learning algorithms Clustering algorithms machine learning Amino acids Prediction algorithms Classification algorithms

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Rainfall Prediction: Analysis of machine learning algorithms and Ensemble Techniques

Rainfall Prediction: Analysis of Machine Learning Algorithms...

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International Conference on Signal Processing and Communication (ICSC)

作者： Abhinav Sharma Akshay Khanna Muskaan Bhargava Rutwik Pendse VESIT Chembur India

ISBN: (纸本)9781665427401

In 2018, approximately 60% of land in India was reported as Agricultural Farmland. Most of the farmers in India depend on rainfall as the primary source of irrigation which implies that rainfall is directly linked to the yield. The quantity of rain a land receives helps in planning which crop to sow and also in which month the farmer should begin farming. Precipitation is not only limited to agriculture, even the infrastructure development sector has to keep track of the monsoon season and amount of rainfall that a construction site will receive because of the influence it has on construction projects. It also serves as one of the most important sources of freshwater for all living things on the planet. As rainfall prediction model gives data on the impact of various climatological variables on rainfall amounts it has many applications across many sectors. We propose a study with analysis of various machine learning algorithms for rainfall prediction. An accuracy of 95% was obtained with XGBoost, which is a gradient boosting framework. This also serves to inform the robusticity of the proposed model in comparison.

关键词： machine learning algorithms Rain Planets Monsoons Predictive models Signal processing Mice

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Quantum machine learning: A comprehensive review on optimization of machine learning algorithms

Quantum Machine Learning: A comprehensive review on optimiza...

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Microelectronics, Signals & Systems (ICMSS), International Conference on

作者： R Divya J. Dinesh Peter Sahrdaya College of Engineering and Technology Thrissur India Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9781665448864

Quantum technologies can provide innovative solutions to many complex problems, and thus quantum machine learning has taken a unique place in the world of computing. Quantum technology reaches an advanced level when the potential of quantum computing features is used for machine learning. Applying quantum computing features in traditional algorithms provides an exceptional parallel computing capability for solving complex problems. The essence of this paper is a comparative study of the basic concepts of quantum computing and their superior capabilities over classical computing. This article describes the application based algorithms such as QSVM, QPCA, and Q-KNN along with Grover's algorithm, which is the most popular and fundamental quantum machine learning algorithm. This study aims to understand various learning models that incorporate the advantages of computing into quantum circuits for enhancing classical machine learning functionalities.

关键词： Technological innovation machine learning algorithms Computational modeling Decision making machine learning Computer architecture Parallel processing

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Flood Inundation Mapping of a River Stretch Using machine learning algorithms in the Google Earth Engine Environment

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Journal of Flood Risk Management 2025年第2期18卷

作者： Ashhar, Maaz Keesara, Venkata Reddy Sridhar, Venkataramana Department of Civil Engineering National Institute of Technology Warangal India Department of Biological Systems Engineering Virginia Polytechnic Institute and State University Blacksburg VA United States

Floods are among the most common natural disasters in India, causing significant socio-economic and environmental impacts. This study focuses on a frequently flooded stretch of the Godavari River in Telangana, India, to analyze the flood event that occurred between 14th July 2022 and 20th July 2022. Sentinel-1 SAR data from 6th July 2022 to 20th July 2022 were used to perform flood inundation mapping. Various machine learning algorithms, including Support Vector machine (SVM), Random Forest (RF), Gradient Boosting Tree (GBT), and Classification and Regression Tree (CART), were employed. The analysis revealed that out of the total study area of 1,556,544 ha, SVM classified 59,823 ha, RF classified 60,088 ha, GBT classified 57,497 ha, and CART classified 58,374 ha as flooded areas. In contrast, Otsu's Thresholding technique identified a significantly larger flooded area of 359,253 ha. For validation, 70 flooded and 30 non-flooded points were randomly selected from the flood map provided by the National Remote Sensing Center (NRSC). The RF algorithm achieved the best performance, correctly classifying 58 flooded points and 26 non-flooded points, resulting in an overall accuracy of 84%. The findings highlight the effectiveness of machine learning algorithms, particularly Random Forest, in flood inundation mapping. © 2025 The Author(s). Journal of Flood Risk Management published by Chartered Institution of Water and Environmental Management and John Wiley & Sons Ltd.

关键词： flood inundation map Google earth engine machine learning algorithms SAR data

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Classification of Water Potability Using machine learning algorithms

Classification of Water Potability Using Machine Learning Al...

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Artificial Intelligence and Big Data Analytics, International Conference on

作者： Muhammad I’tikafi Khoirul Haq Fauzian Dwi Ramadhan Fatimah Az-Zahra Linda Kurniawati Afrida Helen Universitas Padjadjaran Research Center for Artificial Intelligence and Big Data Universitas Padjadjaran Universitas Padjadjaran Sumedang Indonesia

ISBN: (纸本)9781665408912

Clean water is one of the basic needs of everyday life. Recently, an ongoing process has been shown to improve water quality, making water less suitable for use. To solve this problem, research is done using a machine learning model. The Decision Tree Algorithm is used by Naïve Bayes algorithm in this type of machine learning to support drinking water quality. The two types of performance are compared in this work. K-fold cross credentials are used to evaluate our machine learning model. Results obtained in the decision tree algorithm have the best results in the configuration with an accuracy value of 97.23%.

关键词： Measurement Analytical models machine learning algorithms machine learning Water quality Big Data Data models

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An Analysis of machine learning algorithms and Deep Neural Networks for Email Spam Classification using Natural Language Processing

An Analysis of Machine Learning Algorithms and Deep Neural N...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Md. Mohidul Hasan Syed Mahbubuz Zaman Md. Asif Talukdar Ayesha Siddika Md. Golam Rabiul Alam Computer Science(Software Engineering) University of Hertfordshire London UK Computer Science & Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9781665467230

Due to the extensive use of technology in our daily lives, email has become essential for online correspondence between individuals from all walks of life. As such certain individuals have weaponized this service by bulk mailing malicious emails to recipients with the goal of retrieving some form of classified information. Thus, Email classification has become a major area of research as it enables identification and isolation of such malicious emails. The objectives of this paper include a robust comparison of several traditional machine learning (ML) algorithms, exploring transfer learning with static (non-trainable) pretrained GLOVE (Global word vector representation) embedding, comparison of several deep learning models trained with GLOVE and keras embedding separately. Among ML classifiers, XGBoost achieved the highest evaluation scores. Among deep learning algorithms, keras embedding based models outperformed GLOVE embedding based models by a small margin which shows the efficiency of transfer learning in downstream NLP tasks (parts of speech tagging).

关键词： Deep learning machine learning algorithms Weapons Transfer learning Neural networks Natural language processing Electronic mail

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Analysis and Prediction of Stroke using machine learning algorithms

Analysis and Prediction of Stroke using Machine Learning Alg...

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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： V. JalajaJayalakshmi V. Geetha M. Mohammed Ijaz Kumaraguru College of Tech Cbe India

ISBN: (纸本)9781665428309

Stroke is a medical emergency that occurs when a section of the brain’s blood supply is cut off. The brain cells die when they are deprived of the oxygen and glucose needed for their survival. It is the world’s second prevalent disease and can be fatal if it is not treated on time. An early intervention and prediction could prevent the occurrence of stroke. machine learning algorithms are transforming the healthcare and is widely used in early diagnosis of diseases. This work focuses on the prediction of the occurrence of stroke at an early stage using machine learning algorithms. This paper establishes Ada Boost’s superiority over other well-known classification algorithms for early stroke prediction.

关键词： machine learning algorithms Medical services machine learning Stroke (medical condition) Telecommunication computing Glucose Classification algorithms

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To Detect the Distributed Denial-of-Service Attacks in SDN using machine learning algorithms

To Detect the Distributed Denial-of-Service Attacks in SDN u...

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International Conference on Computing, Communication, and Intelligent Systems (ICCCIS)

作者： Shruti Banerjee Partha Sarathi Chakraborty M. Tech. CSE SRM Institute of Science and Technology NCR Campus Ghaziabad India

ISBN: (数字)9781728185293

ISBN: (纸本)9781728185309

The reason for Software Defined Network (SDN) to gain importance in both the academics and industry as a new emerging way of network management, is its architecture which decouples the data plane (forwarding devices) and the control plane (controller) making it possible to upgrade and update into newer versions. SDN is a new or novel way of `programmable networks' which has led to growth of innovative technologies and scenarios in terms of network virtualization, flexibility, enhanced growth control, dynamic network policy, reduced operational cost. Despite, these advantages; it is also one of main reasons for cyber threats. Amongst them the most vulnerable is the DDoS attacks. DDoS attack in SDN is quite a threat to the security in SDN network. It attacks at the network layer or application layer of the infrastructure. It can cause problems as simple as inability to refresh a particular page to as severe as failure of an entire server. In this paper, DDoS is taken into consideration with SDN and proposed a IDS which studied for detection of the attackers in the real time incoming traffic. machine learning algorithms such as Naïve Bayes, KNN, K-Means clustering, and Linear Regression are used to form the module 1 of the IDS (the Signature IDS) and Module 2 form for uses three way handshake to identify the exact host which is an intruder. On finding the intruder it is being placed in the Access Control List (ACL). Also, analysis of efficiency of different machine learning algorithm is performed to understand the effectiveness.

关键词： machine learning algorithms Linear regression Real-time systems Servers Software defined networking Virtualization Intelligent systems

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