2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)最新文献

{"title":"Modeling of Green Communication based VLLC system","authors":"Meet Kumari","doi":"10.1109/ICSTSN57873.2023.10151593","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151593","url":null,"abstract":"As a combined indoor as well as outdoor application of visible laser light communication (VLLC) systems is emerging as green communication networks in various applications. Therefore, a green communication based full-duplex VLLC system at 10Gbps data rate over $50mathrm{~m}$ range is modeled. The results show that the design model offers minimum signal to noise ratio (SNR) of 14 and $5mathrm{~dB}$ in downlink and uplink transmission respectively. Also, the minimum received optical power upto -15 and -18dBm in downlink and uplink transmission respectively. Further, the proposed model offers best performance than others existing models.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precise Prediction of Cardiovascular Diseases Using Machine Learning","authors":"G. Revathy, S. Venkateswaran, V. Senthil Murugan, V. Devi, A. Mohanadevi, G. Saravanan","doi":"10.1109/ICSTSN57873.2023.10151627","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151627","url":null,"abstract":"Cardiovascular disease is among the conditions that pose the greatest risk to life. Nearly 17 million people die as a result of its high mortality rate worldwide. To treat the illness quickly and reduce mortality, early diagnosis is helpful. The occurrence and absenteeism of the ailment can be hush-hush consuming a variability of ML techniques. The UCI dataset is to categorize heart disease using the techniques of LR, NB,SVM and Convolution Neural Networks(CNN). To progress the model’s recital, the dataset was cleaned, missing value searches were carried out, and feature selection was done through correlation by the goal worth for all of the features. The traits with the highest favourable associations were picked. The dataset is then alienated into train and trial sets, and classification is completed experimenting with a 70:30:80 ratio. The most accurate dividing ratio is 80:20. The best outcome will be recorded and used in the suggested model, which will compare Logistic regression, Naive Bayes, and Support vector machines with and without feature selection. Among all the models CNN shows the best result.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122958640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid CNN-GRU Model for Handwritten Text Recognition on IAM, Washington and Parzival Datasets","authors":"Madhav Sharma, Renu Bagoria, Praveen Arora","doi":"10.1109/ICSTSN57873.2023.10151552","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151552","url":null,"abstract":"The aim of using the CNN-GRU Hybrid Model for HTR is to improve the accuracy of existing HTR systems by developing more robust models that can handle the variability of handwriting styles and the complexity of language. The proposed model combines CNN and GRU and is evaluated on multiple datasets, including IAM, Washington, and Parzival, to provide a comprehensive analysis and comparison with existing models. The CNN-GRU architecture proposed in the study has been tested on IAM, Washington, and Parzival datasets, and it was found to have lower CER and WER scores compared to many other models. The model achieved CER scores of 7.16%, 6.S%, and S.06% and WER scores of16.16%, 17.24%, and 19.13% on the IAM, Washington, and Parzival datasets, respectively.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Analysis of Electric Bike(E-Bike)","authors":"Avinash Dash, Ayush Kumar Sahu, Saurabh Sharma, Deepak Yadav, Mohammad Fuad, Shamik Chatterjee","doi":"10.1109/ICSTSN57873.2023.10151644","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151644","url":null,"abstract":"The primary !objective of this research is to give a clear picture of the various forms of energy that people can use. This project yields a Li-ion battery-powered electric bike as well as it will also provide an idea to design an e-bike. The purpose of this research was to develop a blueprint of an electric bike and then analyze it mathematically. Boundary layer theory can also be used to understand the behavior of a system by looking at it. The simulation results show that at different levels, riders need different types of assistance. The rider may change the modes of riding as per requirement which will lead to uninterrupted motion of the bike.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129535857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of Hybrid Cryptography in Steganography for Augmented Security","authors":"V. Kalaichelvi, P. Devi, S. Hemamalini, S. Swaminathan, S. Suganya","doi":"10.1109/ICSTSN57873.2023.10151554","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151554","url":null,"abstract":"Now-a-days, many billions and trillions of data exchanged over the internet per second. Ensuring the security of data is a major priority in the realm of information technology.To provide data security, many technologies have been evolved like cryptography, steganography, visual cryptography, watermarking, etc,. This article combines both cryptography and steganography for providing security. Using modified hill cipher algorithms, it first turns the original message into a secret message. Traditionally, only the alphabets are encrypted with hill cipher. But, the proposed modified hill cipher algorithm can encrypt any type of message using Radix64 encoding/decoding conversion. And the keys which are used in hill cipher algorithm encrypted using modified RSA algorithm. The 2-bit LSB technique is then used to embed the encrypted keys and secret message into the cover image to create the stego-image. Then, the stego-image is transferred to the recipient side through secure channel. The recipient must follow the entire process backwards after receiving the stego-image in order to receive the original message. Finally, the parameters of several performance measures such as Entropy, MSE, PSNR, and histogram are measured. A substantial improvement in efficiency and security can be observed with the proposed system.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122024127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Archimedes Optimization with Enhanced Deep Learning based Recommendation System for Drug Supply Chain Management","authors":"Ketan Rathor, S. Chandre, A. Thillaivanan, M. Naga Raju, Vinit Sikka, Kamlesh Singh","doi":"10.1109/ICSTSN57873.2023.10151666","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151666","url":null,"abstract":"Recently,pharmaceutical corporations are confronting difficulties while tracking their products in the supply chain process, allowing counterfeiters to include their fake medicines into market. Counterfeit drugs were examined as a great challenge for pharmaceutical sector worldwide. Sentiment analysis can be used to analyse customer reviews of drugs to determine overall sentiment towards the drug. Positive reviews can indicate that a drug is effective and well-tolerated, while negative reviews may indicate potential side effects or lack of effectiveness. However, it’s important to note that sentiment analysis is a subfield of natural language processing which uses statistical and machine learning techniques to identify and extract subjective information from source materials. Therefore, this article introduces an Archimedes Optimization with Enhanced Deep Learning based Recommendation System (AOAEDL-RS) for Drug Supply Chain Management. The proposed AOAEDL-RS technique majorly examines the drug reviews for the recommendation of drugs. It follows a three stage process: preprocessing, classification, and parameter tuning. Firstly, the AOAEDL-RS technique performs preprocessing and word2vec embedding processes. Secondly, the context based BiLSTM-CNN (CBLSTM-CNN) model is applied for drug review classification and classification. Thirdly, the AOAEDL-RS technique uses AOA for the optimal hyperparameter tuning of CBLSTM-CNN method. The result analysis of the AOAEDL-RS technique is tested on drug reviews dataset and the outcomes show the improved outcomes of the AOAEDL-RS method.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126417862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Tumor MRI Image Segmentation and Classification based on Deep Learning Techniques","authors":"Ali Arafat, Dipesh Mamtani, K. Jansi","doi":"10.1109/ICSTSN57873.2023.10151504","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151504","url":null,"abstract":"Detection and diagnosis of brain tumors is important for improving the possibility of successful treatment and recovering. Magnetic resonance imaging (MRI) is widely used imaging method for treating and recovering brain tumors. However, manual identification of brain tumors from a large amount of MRI images is time-consuming and requires specialized expertise. To overcome these challenges, computer- assisted intelligent systems are increasingly being used to speed up the medical assessment as well as treatment recommendations. The aim of our research is for coming up with a deep learning system that can segment and classify tumors in brain. The U-Net model is used for segmentation of the MRI images, while Convolution Neural Network (CNN) is used for the classifying brain tumors. Performance metrics such as accuracy, precision and recall are used to evaluate the effectiveness of this approach. The suggested CNN classifier has given the accuracy of nearly 98% for both the training and validation data. By using deep learning techniques, the following system attempts to provide accurate and effective segmenting and classifying tumors.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"302 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121465171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Machine Learning for Intelligent Transportation in Bike-Sharing","authors":"Simon Onen, Marvin Ggaliwango, Samuel Mugabi, Joyce Nabende","doi":"10.1109/ICSTSN57873.2023.10151456","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151456","url":null,"abstract":"In recent years, the benefits of bike-sharing have become increasingly clear, with cities around the world benefiting from increased road resource utilisation, reduced traffic congestion, and improved urban mobility. Bike-sharing has proven to be an essential mode of transportation and a cornerstone of smart city initiatives. To ensure that bike-sharing service providers can provide an optimal experience to their customers, it is crucial to have accurate information about the total number of bikes available at each station, as imbalances caused by bike shortages can negatively impact the service. While many previous studies have used machine learning techniques to predict demand, few have addressed how these models make their predictions.This study focuses on developing explainable and interpretable models for predicting the total number of bikes. We employ a range of regression methods, including Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Regressor (GBR), to accurately forecast the distribution of bikes in a bike-sharing service. Our approach uses Exploratory Data Analysis (EDA), model selection, validation, and Explainable Artificial Intelligence (XAI) to provide a clear and transparent interpretation of the model predictions.To evaluate the models, industry-standard metrics such as Mean Absolute Error, Mean Squared Error, and R2 Score were used. The report showed an outstanding average R2 Score of 99%, demonstrating the efficacy of the models in accurately predicting the total number of bikes in a bike-sharing service. The approach used offers a transparent and interpretable methodology for predicting bike distribution, which can aid bike-sharing service providers in providing optimal service to their customers.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130786962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early Diagnosis of Parkinson’s Disease and Severity Assessment based on Gait using 1D-CNN","authors":"Narayan Sharma, Iman Junaid, S. Ari","doi":"10.1109/ICSTSN57873.2023.10151641","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151641","url":null,"abstract":"Gait irregularities are among the crucial signs that doctors should take into account when making a diagnosis. However, gait analysis is difficult and can depend on the knowledge of experts and the clinician’s subjectivity. To assess gait data, this research suggests a smart cutting-edge system, for diagnosis of Parkinson’s disease (PD) based on a deep learning approach. The proposed method analyzes 1-D inputs from sensors (which are connected to foot) that measure the virtual ground reaction force (VGRF). The first section of the network is composed of eighteen parallel ID-CNNs that correlate to the system’s inputs. In the second section, the eighteen number of ID-CNN outputs are concatenated into one unique deep array. In the third section, various classifiers such as support vector machine, multi-layer perceptron and random forest are used for final classification. The proposed methodology is used to predict between the two classes, i.e., control (CO) and PD subjects, as well as to predict the severity of Parkinson’s gait according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Our test shows that the suggested method is highly effective in detecting PD from gait data. Experiments were conducted on the Physionet dataset, and the results specify that the suggested model outperforms alternative methods in terms of classification outcomes. This model can assist in the severity diagnosis of PD by using gait data.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131662898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable Machine Learning Models for Predicting Malaria","authors":"Solimo Rajab, J. Nakatumba-Nabende, Ggaliwango Marvin","doi":"10.1109/ICSTSN57873.2023.10151538","DOIUrl":"https://doi.org/10.1109/ICSTSN57873.2023.10151538","url":null,"abstract":"Malaria remains one of the deadliest diseases in underdeveloped regions, particularly in Sub-Saharan Africa. The lack of high-quality healthcare services and accurate disease diagnosis systems has resulted in acute medical problems for patients. This necessitates reliable automated decision-making tools to aid medical professionals in their decision-making process. This paper presents a transparent approach to malaria diagnosis by applying Explainable Artificial Intelligence (XAI) techniques, namely Shapley Additive Explanation (SHAP) and Local Interpretable Model-agnostic Explanation (LIME), to provide meaningful interpretations of severe malaria predictions made by machine learning models. Various models, including Extreme Gradient Boosting, K-means, K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree, Logistic Regression (LR), Random Forest, Naive Bayes, AdaBoost, and Explainable Boosting Machines (EBMs) are deployed for this task. The results of the study showed that Random Forest and Explainable Boosting Machines achieved the highest accuracy of 84%. EBM also provided a practical clinical understanding of features that drive clear prediction. The LR achieved an accuracy of 81% after applying GridSearchCV to increase prediction accuracy. Furthermore, K-fold validation was used on XGBoost to estimate the model’s skill on new data. The interpretations were enhanced by XAI, which revealed features that contribute to severe malaria. The application of these techniques can significantly improve the accuracy of severe malaria predictions and aid medical professionals in making informed decisions. This paper provides a compelling argument for the urgent need for XAI techniques to address the challenges associated with severe malaria diagnosis and treatment. The study’s findings demonstrate the effectiveness of these techniques in enhancing the accuracy and interpretability of machine learning models, which can greatly benefit medical professionals in their decision-making process.","PeriodicalId":325019,"journal":{"name":"2023 2nd International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131095061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}