Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008296
Chuanyang Zhang, Yufei Pang, Yu Guo
From the perspective of medical big data utilization, analyze the current situation of China's medical informatization development, build a medical big data processing and service framework for digital and intelligent transformation, and provide new ideas and methods for medical informatization and intelligent medical development. Based on information life cycle management theory, combined with a large number of literature research, a medical big data processing and service framework is constructed. The framework discusses the functions of the original data module, data collection module and integrated system module, and further expounds the content and application of digital intelligent medical services.
{"title":"Construction of Medical Big Data Processing and Service Framework for Digital Intelligent Transformation","authors":"Chuanyang Zhang, Yufei Pang, Yu Guo","doi":"10.1109/CICN56167.2022.10008296","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008296","url":null,"abstract":"From the perspective of medical big data utilization, analyze the current situation of China's medical informatization development, build a medical big data processing and service framework for digital and intelligent transformation, and provide new ideas and methods for medical informatization and intelligent medical development. Based on information life cycle management theory, combined with a large number of literature research, a medical big data processing and service framework is constructed. The framework discusses the functions of the original data module, data collection module and integrated system module, and further expounds the content and application of digital intelligent medical services.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116961961","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008314
Lamia Ouchene, Sadik Bessou
Twitter Arabic Sentiment Analysis refers to identify and classify the sentiments expressed in the tweet. The Algerian dialect is one of the Arabic dialects used on Twitter and has some peculiarities and few resources. Our study aims to prepare and annotate a gold standard dataset for the Algerian dialect and then make a classification model with robust predictions using deep learning techniques such as pre-trained transformers which are now the de facto models in Natural Language Processing. Due to their state-of-the-art results in many tasks such as Arabic Sentiment Analysis. In this paper, we used our dataset of 20400 tweets to train three traditional machine learning classifiers (Support Vector Machine SVM, Bernoulli Naive Bayes BNB, Multinomial Naive Bayes MNB) and two deep learning architectures (Long Short-Term Memory (LSTM) and Pre-trained language model like BERT. We find that our pre-trained model performs best with 82,36% accuracy.
{"title":"Sentiment analysis for Algerian Dialect tweets","authors":"Lamia Ouchene, Sadik Bessou","doi":"10.1109/CICN56167.2022.10008314","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008314","url":null,"abstract":"Twitter Arabic Sentiment Analysis refers to identify and classify the sentiments expressed in the tweet. The Algerian dialect is one of the Arabic dialects used on Twitter and has some peculiarities and few resources. Our study aims to prepare and annotate a gold standard dataset for the Algerian dialect and then make a classification model with robust predictions using deep learning techniques such as pre-trained transformers which are now the de facto models in Natural Language Processing. Due to their state-of-the-art results in many tasks such as Arabic Sentiment Analysis. In this paper, we used our dataset of 20400 tweets to train three traditional machine learning classifiers (Support Vector Machine SVM, Bernoulli Naive Bayes BNB, Multinomial Naive Bayes MNB) and two deep learning architectures (Long Short-Term Memory (LSTM) and Pre-trained language model like BERT. We find that our pre-trained model performs best with 82,36% accuracy.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117163767","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008363
Fatim F. Alanazi
Deep learning and artificial intelligence are important knowledge areas that have provided solutions allowing the successful resolution of complex problems. Some of these problems include, but are not limited to, human-level control, data analytics and other digitisation challenges. One of the offshoots of deep learning is a concept termed ‘deepfake’, which can be described as the imposition of video of a face image from a source to video of the face image of a target individual in order to make the targeted person appear to express the content of the source video [2]. It is important to establish the fact that deepfakes have been used for malicious purposes, becoming a threat to national security, privacy, democracy, and society at large. It is, therefore, fundamental to review the science behind the method, and the available detection techniques to curtail this digital innovation, so as to reduce its level of threat; that is the focus of this paper.
{"title":"Comparative Analysis of Deep Fake Detection Techniques","authors":"Fatim F. Alanazi","doi":"10.1109/CICN56167.2022.10008363","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008363","url":null,"abstract":"Deep learning and artificial intelligence are important knowledge areas that have provided solutions allowing the successful resolution of complex problems. Some of these problems include, but are not limited to, human-level control, data analytics and other digitisation challenges. One of the offshoots of deep learning is a concept termed ‘deepfake’, which can be described as the imposition of video of a face image from a source to video of the face image of a target individual in order to make the targeted person appear to express the content of the source video [2]. It is important to establish the fact that deepfakes have been used for malicious purposes, becoming a threat to national security, privacy, democracy, and society at large. It is, therefore, fundamental to review the science behind the method, and the available detection techniques to curtail this digital innovation, so as to reduce its level of threat; that is the focus of this paper.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127164056","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008374
S. Yerima, A. Bashar, Ghazanfar Latif
PDF is one of the most popular document file formats due to its flexibility, platform independence and ability to embed different types of content. Over the years, PDF has become a popular attack vector for spreading malware and compromising computer systems. Existing signature-based defense systems have extremely high recall rates, but quickly become obsolete and ineffective against zero-day attacks, which makes them easy to circumvent by malicious PDF files. Recently, Machine Learning (ML) has emerged as a viable tool to improve discovery of previously unseen attacks. Hence, in this paper we present enhanced ML-based models for the detection of malicious PDF documents. We develop an approach for ML-based detection with static features derived from PDF documents leveraging existing tools and propose new, previously unused features to enhance the performance of the ML-based classifiers. Our investigative study is conducted on the recently published Evasive-PDFMal2022 dataset, which was used to evaluate seven ML classifiers based on our proposed method. The EvasivePDFMal2022 dataset consists of 4,468 benign samples and 5,557 malicious PDF samples. The results of the experiments show that our proposed approach with the enhanced features enabled improved accuracies in five out of seven of the classifiers that were evaluated. The results demonstrate the potential of the new features to increase the robustness of feature-based PDF malware detection.
{"title":"Malicious PDF detection Based on Machine Learning with Enhanced Feature Set","authors":"S. Yerima, A. Bashar, Ghazanfar Latif","doi":"10.1109/CICN56167.2022.10008374","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008374","url":null,"abstract":"PDF is one of the most popular document file formats due to its flexibility, platform independence and ability to embed different types of content. Over the years, PDF has become a popular attack vector for spreading malware and compromising computer systems. Existing signature-based defense systems have extremely high recall rates, but quickly become obsolete and ineffective against zero-day attacks, which makes them easy to circumvent by malicious PDF files. Recently, Machine Learning (ML) has emerged as a viable tool to improve discovery of previously unseen attacks. Hence, in this paper we present enhanced ML-based models for the detection of malicious PDF documents. We develop an approach for ML-based detection with static features derived from PDF documents leveraging existing tools and propose new, previously unused features to enhance the performance of the ML-based classifiers. Our investigative study is conducted on the recently published Evasive-PDFMal2022 dataset, which was used to evaluate seven ML classifiers based on our proposed method. The EvasivePDFMal2022 dataset consists of 4,468 benign samples and 5,557 malicious PDF samples. The results of the experiments show that our proposed approach with the enhanced features enabled improved accuracies in five out of seven of the classifiers that were evaluated. The results demonstrate the potential of the new features to increase the robustness of feature-based PDF malware detection.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126154139","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}
This study developed an improved lighting system called i-Light that incorporates three major factors in the growth of broilers which include color, photoperiod, and light intensity. Arduino Mega microcontroller was used to control the overall system. This system was compared to the existing traditional lighting system (TL) and LED lighting system (LL) in terms of broiler growth mortality rate and power usage. The results showed that the i-Light system performed better than both TL and LL systems. In terms of growth, the i-Light system demonstrates its ability to outperform TL and LL as seen by the fact that the i-Light system's weight gain on the final week of experimentation is 55.45% heavier than TL and 36.36% heavier than LL. It was also shown that the system logs the lowest mortality rate compared to the TL and LL system which records a 30% mortality rate which is more ideal than the 50% shown on TL and 40% for LL. The power usage results show that the i-Light used the least electricity. The i-Light system is 75.44% more efficient than the TL which saves up to 95.914 PHP monthly. On the other hand, i-Light is 63.12% more efficient than LL which saves up to 53.42 PHP per month. The i-Light system developed in the study can be a viable option for poultry farmers.
{"title":"I-Light: An Improved Lighting System For Poultry Farms","authors":"Ertie Abana, Hubert Chester Damo, Ariel Lorenzo, Shedric Dimayuga, Peejay Paguirigan, Princess Gail Dineros, Korrrine Villaverde","doi":"10.1109/CICN56167.2022.10008367","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008367","url":null,"abstract":"This study developed an improved lighting system called i-Light that incorporates three major factors in the growth of broilers which include color, photoperiod, and light intensity. Arduino Mega microcontroller was used to control the overall system. This system was compared to the existing traditional lighting system (TL) and LED lighting system (LL) in terms of broiler growth mortality rate and power usage. The results showed that the i-Light system performed better than both TL and LL systems. In terms of growth, the i-Light system demonstrates its ability to outperform TL and LL as seen by the fact that the i-Light system's weight gain on the final week of experimentation is 55.45% heavier than TL and 36.36% heavier than LL. It was also shown that the system logs the lowest mortality rate compared to the TL and LL system which records a 30% mortality rate which is more ideal than the 50% shown on TL and 40% for LL. The power usage results show that the i-Light used the least electricity. The i-Light system is 75.44% more efficient than the TL which saves up to 95.914 PHP monthly. On the other hand, i-Light is 63.12% more efficient than LL which saves up to 53.42 PHP per month. The i-Light system developed in the study can be a viable option for poultry farmers.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114911306","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008323
Mahmoud Ellouh, Mustafa Ghaleb, Muhamad Felemban
IoT enables the communication of electronic devices and sensors with the Internet using standard protocols to achieve autonomy, robustness, and reliable data exchange among devices and real applications. The wide variety of IoT devices has led to raising concerns about the security of interconnected devices. IoT manufacturers have been increasing recently, which has resulted in building IoT devices with different standards, protocols, features, and technologies. However, the lack of implementation of security features for IoT devices has led the IoT devices to be susceptible to attacks and targeted by adversaries. In order to provide an efficient honeypot-based solution, it should benefit from the malicious traffic in the filtering phase to detect zero-day attacks. In this paper, we propose IoTZeroJar, a honeypot system to detect the attacker's malicious activities and analyze zero-day attacks.
{"title":"IoTZeroJar: Towards a Honeypot Architecture for Detection of Zero-Day Attacks in IoT","authors":"Mahmoud Ellouh, Mustafa Ghaleb, Muhamad Felemban","doi":"10.1109/CICN56167.2022.10008323","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008323","url":null,"abstract":"IoT enables the communication of electronic devices and sensors with the Internet using standard protocols to achieve autonomy, robustness, and reliable data exchange among devices and real applications. The wide variety of IoT devices has led to raising concerns about the security of interconnected devices. IoT manufacturers have been increasing recently, which has resulted in building IoT devices with different standards, protocols, features, and technologies. However, the lack of implementation of security features for IoT devices has led the IoT devices to be susceptible to attacks and targeted by adversaries. In order to provide an efficient honeypot-based solution, it should benefit from the malicious traffic in the filtering phase to detect zero-day attacks. In this paper, we propose IoTZeroJar, a honeypot system to detect the attacker's malicious activities and analyze zero-day attacks.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"216 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116525904","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008269
Manali Shukla, B. Tripathi, Malti Nagle, B. Chaurasia
Corona virus disease 2019 (COVID-19) is an infectious disease. We have proposed a COVID-19 disease detection using deep learning method in this paper. Novel disease coronavirus bring forth diverse effect on population. Exponential growth of virus and lack of knowledge of treatment was the biggest challenge for doctors to save patient's life. Due to less availability of ventilator and ICU clinical trial and testing overloaded of COVID-19 health status. Lung infection diagnosed by Chest X-ray found as best and fastest approach to detect severity of COVID-19. The work presents an AI model to detect the COVID-19 by diagnoses of chest X-ray report. Chest X-ray report finding has been conducted using CNN (convolution neural network) model with ResNet50 and VGG 19 model. The model classify the patients into four category COVID-19, normal, pneumonia, lung obesity. AI model train the X-ray image through image processing methods with an accuracy of 99.3%. The efficacy of proposed model also has been analyzed in terms of accuracy, specificity, and sensitivity, precision.
{"title":"COVID-19 & Lung Disease Detection using Deep Learning","authors":"Manali Shukla, B. Tripathi, Malti Nagle, B. Chaurasia","doi":"10.1109/CICN56167.2022.10008269","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008269","url":null,"abstract":"Corona virus disease 2019 (COVID-19) is an infectious disease. We have proposed a COVID-19 disease detection using deep learning method in this paper. Novel disease coronavirus bring forth diverse effect on population. Exponential growth of virus and lack of knowledge of treatment was the biggest challenge for doctors to save patient's life. Due to less availability of ventilator and ICU clinical trial and testing overloaded of COVID-19 health status. Lung infection diagnosed by Chest X-ray found as best and fastest approach to detect severity of COVID-19. The work presents an AI model to detect the COVID-19 by diagnoses of chest X-ray report. Chest X-ray report finding has been conducted using CNN (convolution neural network) model with ResNet50 and VGG 19 model. The model classify the patients into four category COVID-19, normal, pneumonia, lung obesity. AI model train the X-ray image through image processing methods with an accuracy of 99.3%. The efficacy of proposed model also has been analyzed in terms of accuracy, specificity, and sensitivity, precision.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122505002","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}
Due to the limited perception ability of the naked eye, it is difficult to accurately locate the micro scale retinal vessels, which is easy to cause organ damage in the process of surgery. Therefore, the retinal vascular reconstruction method based on binocular stereo vision is studied. Firstly, build a binocular vision detection system and then calibrate and correct the system. Secondly, filter and segment the collected 3D printed eyeball blood vessel model image, and calculate the parallax and depth map by constructing the error energy function. After that, the high-precision 3D point cloud model can be obtained. Finally, the error analysis of three-dimensional point cloud is carried out by binocular parallax principle. The experimental results show that this method can control the error within 0.5 mm and meet the requirements of retinal vascular surgery.
{"title":"Three-dimensional Reconstruction of Retinal Blood Vessels based on Binocular Vision","authors":"Chao-liang Wu, Zhiming Lv, Hua-zhu Liu, Xiaoqing Zhao","doi":"10.1109/CICN56167.2022.10008256","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008256","url":null,"abstract":"Due to the limited perception ability of the naked eye, it is difficult to accurately locate the micro scale retinal vessels, which is easy to cause organ damage in the process of surgery. Therefore, the retinal vascular reconstruction method based on binocular stereo vision is studied. Firstly, build a binocular vision detection system and then calibrate and correct the system. Secondly, filter and segment the collected 3D printed eyeball blood vessel model image, and calculate the parallax and depth map by constructing the error energy function. After that, the high-precision 3D point cloud model can be obtained. Finally, the error analysis of three-dimensional point cloud is carried out by binocular parallax principle. The experimental results show that this method can control the error within 0.5 mm and meet the requirements of retinal vascular surgery.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669778","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008283
Tejaswita Garg, S. K. Gupta
Sentiment analysis helps in the early detection of depression as identify unpleasant mental states in people who are at risk for developing mental disorders. By utilizing both syntactic and semantic information, modelling approaches for sentiment analysis rely on machine learning algorithms. In this paper, a hybrid stacked ensemble learning approach has been used for the detection of depression as a neurological disorder. With the help of pre-trained word embeddings, the Word2Vec, GloVe and Fasttext are chosen for data preprocessing and feature extraction. Then, to identify depressed and non depressed identities, we integrate a hybrid stacked ensemble learning approach over Random Forest (RF), Support vector machines (SVM), K-Nearest Neighbor (KNN) and Catboost classifier (CBC) as base models whereas logistic regression (LR) as meta model classifier. The results of the experiments show that suggested model performs best with our proposed model than individual models. It is also found that with Word2Vec word embedding model, the proposed model achieved the higher accuracy as 99% in comparison to GloVe and Fasttext that categorizes depressed over non depressed users on the social media platforms.
情绪分析有助于早期发现抑郁症，因为它可以识别出有发展为精神障碍风险的人的不愉快的精神状态。通过利用句法和语义信息，情感分析的建模方法依赖于机器学习算法。在本文中，混合堆叠集成学习方法已被用于检测抑郁症作为一种神经系统疾病。在预训练词嵌入的帮助下，选择Word2Vec、GloVe和Fasttext进行数据预处理和特征提取。然后，为了识别抑郁和非抑郁身份，我们在随机森林(RF)，支持向量机(SVM)， k -近邻(KNN)和Catboost分类器(CBC)上集成了混合堆叠集成学习方法作为基本模型，而逻辑回归(LR)作为元模型分类器。实验结果表明，本文提出的模型比单个模型的性能更好。研究还发现，使用Word2Vec词嵌入模型，与GloVe和Fasttext对社交媒体平台上的抑郁用户和非抑郁用户进行分类相比，所提出的模型达到了99%的更高准确率。
{"title":"A Hybrid Stacked Ensemble Technique to Improve Classification Accuracy for Neurological Disorder Detection on Reddit posts","authors":"Tejaswita Garg, S. K. Gupta","doi":"10.1109/CICN56167.2022.10008283","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008283","url":null,"abstract":"Sentiment analysis helps in the early detection of depression as identify unpleasant mental states in people who are at risk for developing mental disorders. By utilizing both syntactic and semantic information, modelling approaches for sentiment analysis rely on machine learning algorithms. In this paper, a hybrid stacked ensemble learning approach has been used for the detection of depression as a neurological disorder. With the help of pre-trained word embeddings, the Word2Vec, GloVe and Fasttext are chosen for data preprocessing and feature extraction. Then, to identify depressed and non depressed identities, we integrate a hybrid stacked ensemble learning approach over Random Forest (RF), Support vector machines (SVM), K-Nearest Neighbor (KNN) and Catboost classifier (CBC) as base models whereas logistic regression (LR) as meta model classifier. The results of the experiments show that suggested model performs best with our proposed model than individual models. It is also found that with Word2Vec word embedding model, the proposed model achieved the higher accuracy as 99% in comparison to GloVe and Fasttext that categorizes depressed over non depressed users on the social media platforms.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129020862","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}
Pub Date : 2022-12-04DOI: 10.1109/CICN56167.2022.10008327
Ezdihar N. Bifari, W. Alhalabi
Court judgments from several nations have recently been published and made accessible online for study, adding to the growing number of online resources available today. As a recommendation, the legal databases can be incorporated into the training of crime scene investigators and made available as a learning resource. A text mining method was used to identify relevant documents, including crime scenes, and categorize them according to the kind of crime committed. The results from the statistical data support the possibility of useful information about crime scenes and murder cases in legal documents.
{"title":"Exploring Narrative Court Documents for Use in Police Academic Education","authors":"Ezdihar N. Bifari, W. Alhalabi","doi":"10.1109/CICN56167.2022.10008327","DOIUrl":"https://doi.org/10.1109/CICN56167.2022.10008327","url":null,"abstract":"Court judgments from several nations have recently been published and made accessible online for study, adding to the growing number of online resources available today. As a recommendation, the legal databases can be incorporated into the training of crime scene investigators and made available as a learning resource. A text mining method was used to identify relevant documents, including crime scenes, and categorize them according to the kind of crime committed. The results from the statistical data support the possibility of useful information about crime scenes and murder cases in legal documents.","PeriodicalId":287589,"journal":{"name":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"31 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121210318","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}
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