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Effective Feature Selection and Soft Voting Classifier based Diabetes Detection Using Machine Learning Approaches

Effective Feature Selection and Soft Voting Classifier based...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Suchi, Tania Alam Rabbi, Md. Anayt Layek, Md. Abu Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

An unnecessarily increased blood glucose level is a symptom of the metabolic disorder diabetes mellitus (DM). Early diabetes detection is reduce the risk factor of kidney disease, heart failure, liver cirrhosis, it might be hazardous. Our motivation is to developed a machine learning models to forecast diabetes in the future. We applied efficient data cleaning, label encoding, normalization and pre-processing methods for better models accuracy. Prediction with synthetic minority over-sampling (SMOTE) implies the equilibrium of an unstable dataset. Different feature selection techniques such as Chi-square test, Information gain attribute evaluator, Extra trees classifier, SelectKbest, and correlation-based approach to finding the key variables. Machine learning algorithms including Random Forest(RF), K-nearest neighbor(KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting (GB), Decision Tree (DT), and Logistic Regression (LR), have been applied in the initial phase. Random Forest accuracy of 95.03% after normalizing the dataset with synthetic minority over-sampling (SMOTE) techniques. We using a voting classifier for ensemble all machine learning models in the second phase. The models achieved soft voting highest accuracy 96.69%. The findings of our models comparing with some current research indicate that it can offer greater accuracy. Now, we can accurately forecast diabetes. © 2023 IEEE.

关键词： Feature Selection

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On-chain Off-chain Blockchain Model for IoT using IPFS 27

On-chain Off-chain Blockchain Model for IoT using IPFS

引用

27th International Conference on Advanced Computing and Communications, ADCOM 2022

作者： Khobragade, Pooja Kumar, Ashok Department of Computer Science and Engineering NIT Rourkela India Turuk Dept. Computer Science and Engineering NIT Rourkela India

ISBN: (纸本)9781839539107

Blockchain has become the most revolutionary and trending technology since 2008 with the development of Bitcoin. Besides bitcoin, blockchain can be applied in different sectors, such as finance, healthcare, IoT, security, etc. Blockchain and IoT are two new emerging technologies. IoT has become an integral part of human digital life with the exponential growth of digitally connected devices to the internet. With the development of connected devices, data communication has also increased. Till now, IoT data management has been based on a centralized server. Blockchain technology can protect IoT devices against malicious attacks and single-point failure. This study analyzes the integration of blockchain technology with IoT and also discusses the advantages, challenges in integration, and associated cyber security threats. Also, it provides an architecture for blockchain-enabled secure IoT devices with the Interplanetary File System(IPFS). © ADCOM 2022. All rights *** right reserved.

关键词： Smart contract

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Optimizing Wind Speed Estimation with Least Squares Generative Adversarial Networks: A Case Study of Pakistan 6

Optimizing Wind Speed Estimation with Least Squares Generati...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ashfaq, Iqra Zahid, Hasan Naveed, Fajr Abid, Aaisha Uddin, Zaki NUST CEME Dept. of Electrical Engineering Islamabad Pakistan GIKI Dept. of Electrical Engineering Swabi Pakistan Fast NUCES Dept. of Computer Science Islamabad Pakistan NUST CEME Dept. of Mechatronics Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Wind energy has emerged as a key player in sustainable power generation. Accurate prediction of wind speed is critical for optimizing wind farm operations, energy output forecasting, and reducing dependency on traditional power sources. In this study, we propose a Least-Squares Generative Adversarial Network (LSGAN) for wind speed prediction in a specific region of Pakistan, utilizing data from Tandoghulamali, which includes features like wind speeds at various altitudes and wind direction. The model is designed to predict wind speed at 80 meters, a crucial target variable for wind turbine operations. The LSGAN model effectively leverages adversarial training, achieving high accuracy with a Mean Absolute Error (MAE) of 1.54 and a Root Mean Squared Error (RMSE) of 1.77. Through a detailed comparison of actual versus predicted values, the model demonstrates its ability to generalize well across unseen data, with stable performance during the adversarial learning process. This approach opens up new possibilities for applying advanced machine learning techniques in renewable energy forecasting, particularly in regions with sparse historical data. © 2024 IEEE.

关键词： Wind forecasting

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A Novel Approach for Recognizing Real-Time American Sign Language (ASL) Using the Hand Landmark Distance and Machine Learning Algorithms 9

A Novel Approach for Recognizing Real-Time American Sign Lan...

引用

9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Ahmed, Shahan Kar, Sumit Kumar Basak, Sarnali North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Savar Bangladesh

ISBN: (纸本)9798350319651

Several sign language recognition techniques and models have been prevalent to ease the communication gap between people with hearing disabilities and people who don't understand sign language. The proposed model is a vision-based approach aims to find a better system to recognize sign gestures using the distance between hand landmarks. This process doesn't rely on complex image processing techniques, making it robust towards challenging lighting conditions, noisy backgrounds, and image resolution. To accurately detect 26 American Sign Language (ASL) letters, the model only extracts 12 features. As a result, it can recognize the letters more rapidly than the traditional image-based method and can be trained with lesser computational resources. Therefore, low-configured devices can likewise utilize the method described in this paper. Despite a number of sign language recognition methods are currently in widespread use, the inability of those models to handle the orientation of some specific letter combinations (D, G, U, H, I, and J) has a severe effect on their overall effectiveness. This proposed algorithm, added with features 11 and 12, can solve the anomalies in the orientation of similar pair alphabets. Therefore, the four most popular algorithms, Naive Bayes, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN), have been used to predict the letters of the American Sign Language alphabet. Regardless of the algorithm employed, the model was highly efficient because of the carefully curated dataset, which included attributes directly related to the ASL alphabet signs. SVM demonstrated the best accuracy among these algorithms, outperformina the others by 97 %. © 2023 IEEE.

关键词： Support vector machines

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Image encryption using XOR-based continuous tone MSS and Cellular Automata 6

Image encryption using XOR-based continuous tone MSS and Cel...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Akanksha Garg, Hitendra Shivani, Shivendra GLA University Dept. of Computer Engineering and Applications Mathura India Thapar Institute of Engineering and Technology Dept. of Computer Science Patiala India

ISBN: (纸本)9798350346961

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC's drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.. © 2023 IEEE.

关键词： Cellular automata

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Fuzzy Clustering based Cluster Head Selection for IoT enabled WSNs 10

Fuzzy Clustering based Cluster Head Selection for IoT enable...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gangwar, Saumitra Prasad, Ikkurthi Bhanu Pal, Vipin Yogita Yadav, Satyendra Singh Nit Meghalaya Dept. of Computer Science and Engineering Shillong India

ISBN: (纸本)9789380544472

This work presents a fuzzy mechanism for selecting the optimum cluster head (CH) inside a cluster. During the preliminary simulations, different parameters with different weights are taken into account to make sure that the best CH is chosen, which ameliorates the wireless sensor network (WSN) lifetime. Fuzzy c-means (FCM) clustering is used in the proposed work to select a CH based on the remaining energy ratio, the distance to the sink, the node history, and the node degree. By varying the weights for the above parameters using a brute-force approach, the best-performing weights have been acquired that extend the longevity of a WSN. The simulation results confirm that by incorporating the above-mentioned parameters, network stability is improved by 140.10% and 45.09% as compared to LEACH and FCM, respectively. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Energy efficiency

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Interpretable MOOC Dropout Prediction using Different Ensemble Methods and XAI 2

Interpretable MOOC Dropout Prediction using Different Ensemb...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf Mostakim, Moin Hossain, Muhammad Iqbal Brac University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9781665456302

Massive open online course (MOOC) has been around for several years, which started to gain traction in 2012 when Coursera was established. MOOCs use pre-recorded lectures and scheduled weekly tests to provide content and access to students over the internet. The dataset used in this study is taken from the KDDCUP 2015 dataset, a publicly available dataset. Here, 12 features are considered, they are browser access, navigate, average chapter delays, etc to comprehend the possibility of dropout. This research study intends to predict the dropout of a learner so that it can be prevented through manual interaction. Additionally, XAI is implemented to inter-pret the models to suggest MOOC platforms, which feature impact dropout the most. Further, different ensemble learning techniques, namely voting classifier and stacking are used. The proposed voting classifier uses five of the best-performing machine learning models. Then, the performance of the model is evaluated by using multiple metrics such as precision, recall, and F1-score. Finally, a recall of 97.636 % has been obtained with stacking and f1-score of 91.603 % has been obtained with the hard voting classifier. © 2023 IEEE.

关键词： Forecasting

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An IOTML Based Food Freshness Detection System 2

An IOTML Based Food Freshness Detection System

引用

2nd International Conference for Innovation in Technology, INOCON 2023

作者： Rashmi Navyashree, V.A. Sai Geetha, S. Pavan, B.K. Deepti, C. PES University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350320923

Numerous food donation applications like ShareThe-Meal [1], Food Need [2], Waste No Food [3], etc., do not specify how food quality can be verified. The quality of food donated could be compromised. This report aims at verifying the quality of food donated to orphanages. The work described in this paper aims to provide authentic information regarding food quality. It also provides a platform to connect catering services and volunteers who carry out the process of food donation to the required beneficiaries. Sensors like MQ2 and MQ135 are used to detect freshness of food. Eclipse IDE, SQLYOG are used to develop the interactive platform. One of the major assumptions made is that the predicted food sensor values are based on samples collected from a few basic food items. It is highly unfeasible to ensure that the classification of food as spoilt or unspoilt is inclusive of all possible food items. This work allows us to assume the categorization of food quality based on only few basic food items. This work aims at a high rate of detection with regard to classification of food as SPOILT or FRESH. The methodology involves the detection of Carbon Monoxide(CO),Ammonia(NH3) and Benzene(C6H6) values in food. The mechanism involved in this work would definitely ensure the quality of food before it is donated to the orphanage. © 2023 IEEE.

关键词： Ammonia

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User Profiling and Interest-based Machine Learning to Solve the Cold-Start Problem for a Recommendation Engine 10

User Profiling and Interest-based Machine Learning to Solve ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Hasan, Sayed Nasir Khatwal, Ravi Sangam University Dept. of Computer Science and Engineering Bhilwara India

ISBN: (纸本)9789380544472

The cold-start problem is a major issue in recommender systems that becomes a challenge for e-commerce companies when processing large amounts of data. Several strategies have been proposed to overcome the cold-start problem using different approaches, but the user interest approach is a unique and standalone approach. In this study, we present an interest-based approach that establishes a relationship between user profile data and corresponding user interests. Our work will recommend the items to the cold-start users based on their pro-files and interest matching, and also promote the cold-start users. Our approach is unique in that we consider user interests rather than similarities between items and recommend items which are based on user interests. The experiments and related tests show that proposed method is efficient and successful as far as of classification time, training time, and precision. Although other classifiers were efficient in terms of accuracy, the Bayesian classifier and Chain Classifier had the shortest classification time as well as training time. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Recommender systems

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Understanding Facial Expression of Children with Autism Using Learning Theory

Understanding Facial Expression of Children with Autism Usin...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Zaman, Anushka Khan, Evea Zerin Rabiul Alam, Md. Golam Chakrabarty, Amitabha Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

It is challenging for autistic individuals to gather socially and empathically, as we understand, so this subject matter differs from the identification of a common human facial expression. In our research, we have taken pictures of children and teenagers from school of special children based in Bangladesh, aged between 5 to 20 with Autism Spectrum Disorder (ASD) and identified their emotions from their images. Our objective is to find a technique that will allow their emotions to be accurately perceived and thus, make it easy for them including other individuals around them to interact socially without any barriers. In our paper, we implemented VGG16 and six Machine Learning Algorithms along with one Feature Extraction technique to detect Facial Expression of children with autism. The proposed system has showcased an accuracy of 75% for VGG16, 68% for Random Forest Classifier, 61% for Random Forest Classifier along with PCA, 61% for Support Vector Machine (SVM) and 67% for SVM with PCA, 56% for Logistic Regression and 53% for Logistic Regression along with PCA, 52% for Linear Discriminant Analysis (LDA) and 54% for LDA with PCA, 46% for Decision Tree and 50% for Decision Tree along with PCA and 46% for Gaussian Naïve Bayes and 38% for Gaussian Naïve Bayes along with PCA. © 2023 IEEE.

关键词： Support vector machines

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