2023 IEEE 8th International Conference for Convergence in Technology (I2CT)最新文献_第6页

An Architecture for Microprocessor-Executable Skin Cancer Classification 一种微处理器可执行皮肤癌分类体系结构

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126432

Carlos Vicente Niño Rondón, Diego Andrés Castellano Carvajal, B. M. Delgado, Sergio Alexander Castro Casadiego, Dinael Guevara Ibarra

{"title":"An Architecture for Microprocessor-Executable Skin Cancer Classification","authors":"Carlos Vicente Niño Rondón, Diego Andrés Castellano Carvajal, B. M. Delgado, Sergio Alexander Castro Casadiego, Dinael Guevara Ibarra","doi":"10.1109/I2CT57861.2023.10126432","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126432","url":null,"abstract":"Skin cancer ranks as the most common malignant tumor among all types of cancer. Melanoma accounts for 1% of all cancer cases. However, it is responsible for the majority of deaths from this type of cancer. According to the American Cancer Society, it is expected that 99,780 new cases of melanoma will be diagnosed and about 7,650 people will die from this type of cancer. This work presents an executable architecture on reduced plate systems for skin cancer classification, complemented with image enhancement and feature enhancement stages, information extraction using VGG16 network architecture, feature reduction applying Principal Component Analysis and classification stage using gradient augmented decision trees (XGBoost). The architecture was tested on Raspberry Pi 4B reduced board system and developed with Python programming language and open-source libraries. In turn, the images processed and used are part of the ISIC Challenge Dataset. An average power value of 2.93 W out of a maximum of 3.6 W was obtained in the execution of the diagnostic tool. In turn, the minimum required software architecture response time was 0.09 seconds. The demand for the execution of the diagnostic tool in the Central Processing Unit was on average 20.63 % over a maximum value of 24.5 % respectively. On the other hand, the results at the software level of the architecture were compared with the scientific literature and presented improvements of about 9 % in terms of accuracy in skin cancer classification. The diagnostic tool is replicable and affordable due to reduced hardware requirements and cost of implementation.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132369697","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}

引用次数: 0

Design of Medium to Low Bitrate Neural Audio Codec 中低比特率神经音频编解码器的设计

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126323

Samarpreet Singh, Saurabh Singh Raghuvanshi, Vinal Patel

引用次数: 0

A Real-Time P2P Bot Host Detection in a Large-Scale Network Using Statistical Network Traffic Features and Apache Spark Streaming Platform 基于统计网络流量特征和Apache Spark流媒体平台的大规模网络实时P2P Bot主机检测

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126429

S. Saravanan, G. Prakash, B. Uma Maheswari

{"title":"A Real-Time P2P Bot Host Detection in a Large-Scale Network Using Statistical Network Traffic Features and Apache Spark Streaming Platform","authors":"S. Saravanan, G. Prakash, B. Uma Maheswari","doi":"10.1109/I2CT57861.2023.10126429","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126429","url":null,"abstract":"Nowadays, Peer-to-Peer (P2P) bots play a significant role in launching attacks such as phishing, distributed denial-of-service (DDoS), email spam, click fraud, cryptocurrency mining, etc. The analysis of statistical network traffic features of hosts is one of the commonly used methods to detect P2P bots. Modern P2P bot detection systems need to extract features from massive streaming network traffic as the size of the Internet keeps increasing every day. However, traditional detection systems have trouble detecting bots in real-time in large-scale networks as they are not implemented on big data streaming platforms. Hence, this work proposes a network flow-based P2P bot detection system implemented on Apache Spark Structured Streaming Platform to detect P2P bots in real time by analyzing massive streaming network traffic data generated from large-scale networks. Such detection of P2P bots is based on statistical network traffic features: destination diversity ratio, control packets ratio, and total source bytes sent in a flow. There are two components in the proposed system: the first component detects potential P2P hosts using the Destination Diversity Ratio (DDR), and the second component finds out P2P bot hosts from the P2P hosts identified by the first component. Furthermore, the performance of the detection components depends on the time window at which statistical features are extracted. Hence, this work also conducted experiments to study the effect of different time windows on detection components. The proposed system is evaluated using real-world datasets and achieves a True Positive Rate (TPR) of 99.87%.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122805542","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}

引用次数: 0

Artificial Neural Network Based Double Stage Grid Connected Solar Photovoltaic Supply System 基于人工神经网络的双级并网太阳能光伏供电系统

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126355

Prateeksha Khare, Shailendra Kumar Sharma

引用次数: 0

Design A Smart Pillow for Detection and Management of Snoring 设计一种检测和管理打鼾的智能枕头

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126197

Aishwarya Agawane, R. Mudhalwadkar

引用次数: 0

Time Series Forecasting of Sea Level by Using Transformer Approach, with a Case Study in Pangandaran, Indonesia 用变压器法预测海平面的时间序列，以印度尼西亚Pangandaran为例

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126216

Ridho Nobelino Sabililah, D. Adytia

{"title":"Time Series Forecasting of Sea Level by Using Transformer Approach, with a Case Study in Pangandaran, Indonesia","authors":"Ridho Nobelino Sabililah, D. Adytia","doi":"10.1109/I2CT57861.2023.10126216","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126216","url":null,"abstract":"Sea level prediction is essential information for citizens who live in the coastal area and plan to build structures, especially in the construction stage around the inshore and offshore locations. The statistical method and tidal harmonic analysis have been used to predict the sea level but require long terms historical sea level data to achieve reasonable accuracy. This paper uses Transformer deep learning approaches to predict sea data levels. This paper uses only four months of data in Pangandaran, Indonesia. We use the sea level dataset obtained from the Inexpensive Device for Sea Level measurement (IDSL). The model is trained to predict 1, 7, and 14 days. We also study the sensitivity of the model in terms of lookbacks. The performance of the Transformer was compared with two other popular deep-learning methods; RNN and LSTM. To forecast 14 days, the Transformer model results in a higher coefficient correlation (CC) of 0.993 and a lower root mean squared error (RMSE) value of 0.055 compared to the other two models. Moreover, the Transformer has a faster computing performance than the other two models.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133963456","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}

引用次数: 1

Early detection of ADHD and Dyslexia from EEG Signals 从脑电图信号早期检测ADHD和阅读障碍

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126272

Nupur Gupte, Mitali Patel, Tanvi Pen, Swapnali Kurhade

{"title":"Early detection of ADHD and Dyslexia from EEG Signals","authors":"Nupur Gupte, Mitali Patel, Tanvi Pen, Swapnali Kurhade","doi":"10.1109/I2CT57861.2023.10126272","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126272","url":null,"abstract":"A learning impairment is a dysfunction in one or more fundamental psychological functions that might show up as a lack of proficiency in some areas of learning, such reading, writing, while doing mathematical calculations or while coordinating movements. Learning disabilities are typically not identified until the kid is of school age, Although they can also be developed in very young infants.We aim to develop a machine learning model to analyze EEG (electroencephalogram) signals from people with learning difficulties and provide results in minutes with the highest level of accuracy. Here we will be considering Learning disabilities namely Dyslexia and ADHD(Attention Deficit Hyperactivity Disorder). For the early detection of these disabilities, machine learning algorithms like Support vector machines, K-nearest neighbors, Random Forest, Decision Trees, and convolutional neural networks were used. In order to determine which lobe combination provides the maximum accuracy, we tested the ADHD model using a variety of lobe combinations. The finding indicated that EEG signals produced the highest classification accuracy and Machine learning applications have high potential in identifying ADHD and Dyslexia.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134211955","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}

引用次数: 1

Handwritten Character Recognition of Telugu Characters 泰卢固语字符的手写字符识别

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126377

Yash Prashant Wasalwar, Kishan Singh Bagga, Pvrr Bhogendra Rao, S. Dongre

{"title":"Handwritten Character Recognition of Telugu Characters","authors":"Yash Prashant Wasalwar, Kishan Singh Bagga, Pvrr Bhogendra Rao, S. Dongre","doi":"10.1109/I2CT57861.2023.10126377","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126377","url":null,"abstract":"Given the cursive structure of the writing and the similarity in shape of the letters, Telugu handwritten character identification is an interesting topic. The lack of Telugu-related handwritten datasets has slowed the development of handwritten word recognizers and forced researchers to compare various approaches. Modern deep neural networks find it difficult because they often need hundreds or thousands of photos per class. It has been demonstrated that learning important aspects of machine learning systems can be computationally expensive and challenging when there is a limited amount of data available. This research analysis work proposes a use case on the pre-existing model called EfficientNet and on top of that a custom pooling layer is added to check the trend as the dataset size increases of Telugu characters. The dataset has been divided into three categories, namely, Vowels only dataset, Consonant only dataset, and All character dataset. Proposed model was trained with a considerable amount of dataset containing half a thousand of handwritten Telugu characters and has produced some fascinating results which were worth observing. The accuracies had followed a certain trend. The model was tested on the dataset collected, which were filtered out to record any performance improvement and improvement was observed, where average accuracy went from 55% to 92%.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134240687","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}

引用次数: 0

Enhancing Safety in Vehicles using Emotion Recognition with Artificial Intelligence 利用人工智能的情感识别技术提高车辆安全性

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126274

Moyank Giri, Muskan Bansal, Aditya Ramesh, D. Satvik, U. D

{"title":"Enhancing Safety in Vehicles using Emotion Recognition with Artificial Intelligence","authors":"Moyank Giri, Muskan Bansal, Aditya Ramesh, D. Satvik, U. D","doi":"10.1109/I2CT57861.2023.10126274","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126274","url":null,"abstract":"Safety is the singular most important aspect to improve when it comes to automobiles. The most efficient way to improve road safety would be to consider the driver factor since it has been found that over 90% of all road accidents are due to driver's fault. The emotion of the driver plays a crucial role in ensuring safety while driving. While external parameters can help improve safety, these external parameters are not of much help unless the driver is emotionally stable. Thus, detecting the driver's emotion and enhancing it could significantly improve road safety. Hence, this paper involves identifying and improving the emotional stability of drivers in order to drastically raise automobile safety. Artificial Intelligence (AI) technologies have helped automate and improve many aspects of driving, and have created an environment with a comfortable passenger experience. This paper intends to use some of these AI technologies to detect the emotional state of the driver as Happy, Sad, Angry, Surprised, Fear, Disgust or Neutral by considering both speech and facial expressions, and then generate alerts based on the detected emotion which includes audio and visual alerts for safety, and finally improve the driver's emotional state using appropriate suggestions provided by a music recommendation system. This study makes use of deep learning models (i.e., CNN Models) for automatic emotion detection from audio and video where the validation accuracy obtained for video emotion detection and audio emotion detection is 83% and 78% respectively. The paper also details the use of a developed algorithm for combination of the emotions from Audio and Video. Furthermore, this study uses the Spotify API for the music recommendation system.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115661856","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}

引用次数: 0

Character and Word Level Gesture Recognition of Indian Sign Language 印度手语字字级手势识别

2023 IEEE 8th International Conference for Convergence in Technology (I2CT) Pub Date : 2023-04-07 DOI: 10.1109/I2CT57861.2023.10126314

Rohini K Katti, S. C, Padmashri Desai, Shankar G

{"title":"Character and Word Level Gesture Recognition of Indian Sign Language","authors":"Rohini K Katti, S. C, Padmashri Desai, Shankar G","doi":"10.1109/I2CT57861.2023.10126314","DOIUrl":"https://doi.org/10.1109/I2CT57861.2023.10126314","url":null,"abstract":"Communication is essential to humans because it allows the dissemination of knowledge and the formation of interpersonal connections. We communicate through speaking, facial expressions, hand gestures, reading, writing, and sketching, among other things. However, speaking is the most often utilized means of communication. People having speech and hearing disabilities can only communicate using hand gestures, making them extremely reliant on nonverbal modes of communication. Hearing-impaired persons can communicate via sign language. Globally, around 1 percent(5 million) of the Indian population falls into this group. ISL is a complete language with its own vocabulary, semantics, lexicon, and a variety of other distinctive linguistic features. In our work, we present the methods for Indian sign language recognition at the character and word levels. The Bag of Visual Words(BoVW) technique recognizes ISL at character level(A-Z, 0-9) with an accuracy of 99 percent. Indian Lexicon Sign Language Dataset - INCLUDE-50 dataset is used for word-level sign language recognition. Inception model, a deep Convolutional Neural Network(CNN) is used to train the spatial features and LSTM RNN(Recurrent Neural Network) is used to train the temporal features of the video. Using CNN predictions as input to RNN, we achieved an accuracy of 86.7 %. In order to optimize the training process, only 60 % of the dataset is trained using the Meta-Learning model along with LSTM RNN and obtained an accuracy of 84.4 %, thus reducing the training time by 70 % and reaching nearly as close accuracy as the previous pre-trained model.","PeriodicalId":150346,"journal":{"name":"2023 IEEE 8th International Conference for Convergence in Technology (I2CT)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113994575","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}

引用次数: 0