Pub Date : 2023-06-01DOI: 10.36548/jucct.2023.2.003
Jagrati Dhakar, Keshav Gaur, Satbir Singh, Arun K Khosla
Vehicle detection in degraded hazy conditions poses significant challenges in computer vision. It is difficult to detect objects accurately under hazy conditions because vision is reduced, and color and texture information is distorted. This research paper presents a comparative analysis of different YOLO (You Only Look Once) methodologies, including YOLOv5, YOLOv6, and YOLOv7, for object detection in mixed traffic under degraded hazy conditions. The accuracy of object detection algorithms can be significantly impacted by hazy weather, so creating reliable models is critical. An open-source dataset of footage obtained from security cameras installed on traffic signals is used for this study to evaluate the performance of these algorithms. The dataset includes various traffic objects under varying haze levels, providing a diverse range of atmospheric conditions encountered in real-world scenarios. The experiments illustrate that the YOLO-based techniques are effective at detecting objects in degraded hazy conditions and give information about how well they perform in comparison. The findings help object detection models operate more accurately and consistently under adverse weather conditions.
雾霾条件下的车辆检测对计算机视觉提出了重大挑战。在模糊条件下，由于视觉降低，颜色和纹理信息失真，难以准确检测物体。本文对YOLOv5、YOLOv6和YOLOv7三种不同的YOLO (You Only Look Once)方法在混流条件下的目标检测进行了对比分析。雾霾天气会严重影响目标检测算法的准确性，因此建立可靠的模型至关重要。本研究使用从安装在交通信号上的安全摄像头获得的视频的开源数据集来评估这些算法的性能。该数据集包括不同雾霾水平下的各种交通对象，提供了现实场景中遇到的各种大气条件。实验表明，基于yolo的技术可以有效地检测退化雾霾条件下的目标，并给出了它们在比较中表现如何的信息。这些发现有助于目标检测模型在恶劣天气条件下更准确、更一致地运行。
{"title":"Object Detection for Mixed Traffic under Degraded Hazy Vision Condition","authors":"Jagrati Dhakar, Keshav Gaur, Satbir Singh, Arun K Khosla","doi":"10.36548/jucct.2023.2.003","DOIUrl":"https://doi.org/10.36548/jucct.2023.2.003","url":null,"abstract":"Vehicle detection in degraded hazy conditions poses significant challenges in computer vision. It is difficult to detect objects accurately under hazy conditions because vision is reduced, and color and texture information is distorted. This research paper presents a comparative analysis of different YOLO (You Only Look Once) methodologies, including YOLOv5, YOLOv6, and YOLOv7, for object detection in mixed traffic under degraded hazy conditions. The accuracy of object detection algorithms can be significantly impacted by hazy weather, so creating reliable models is critical. An open-source dataset of footage obtained from security cameras installed on traffic signals is used for this study to evaluate the performance of these algorithms. The dataset includes various traffic objects under varying haze levels, providing a diverse range of atmospheric conditions encountered in real-world scenarios. The experiments illustrate that the YOLO-based techniques are effective at detecting objects in degraded hazy conditions and give information about how well they perform in comparison. The findings help object detection models operate more accurately and consistently under adverse weather conditions.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130500366","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}
To choose the best forecasting model, it is essential to comprehend time series data since external influences like social, economic, and political events may affect the way the data behave. This study considers outside variables that could have an impact on the target variable used in improving the predictions. India Machinery and Transport Equipment Dataset is gathered from various sources, are cleaned, pre-processed, the missing values are removed, data types are converted, and dependent variables are identified before being used. By incorporating the SARIMAX model with the GARCH model and experimenting with various parameters and conditions, the current study seeks to enhance it. The SARIMAX-GARCH Model is a time series forecasting method used to predict market swings and export values. A helper model is developed to forecast the exogenous value to forecast the export value, which is then used as input for the final model. The ideal parameters for boosting the hybrid model's performance were identified through hyperparameter tuning. The results of this study provide estimates for future export values and contribute to a better understanding of India's Machinery and Transport Equipment export market. This research work focuses on export value forecasting with the use of future exogenous variables. Exogenous factors are essential for predicting market changes and, as a result, support the forecasting of precise export values.
{"title":"Indian Machinery and Transport Equipment Exports - Forecasting with External Factors Using Chain of Hybrid Sarimax-Garch Model","authors":"Ramneet Singh Chadha, Shahzadi Parveen, Jugesh, Jasmehar Singh","doi":"10.36548/jucct.2023.2.005","DOIUrl":"https://doi.org/10.36548/jucct.2023.2.005","url":null,"abstract":"To choose the best forecasting model, it is essential to comprehend time series data since external influences like social, economic, and political events may affect the way the data behave. This study considers outside variables that could have an impact on the target variable used in improving the predictions. India Machinery and Transport Equipment Dataset is gathered from various sources, are cleaned, pre-processed, the missing values are removed, data types are converted, and dependent variables are identified before being used. By incorporating the SARIMAX model with the GARCH model and experimenting with various parameters and conditions, the current study seeks to enhance it. The SARIMAX-GARCH Model is a time series forecasting method used to predict market swings and export values. A helper model is developed to forecast the exogenous value to forecast the export value, which is then used as input for the final model. The ideal parameters for boosting the hybrid model's performance were identified through hyperparameter tuning. The results of this study provide estimates for future export values and contribute to a better understanding of India's Machinery and Transport Equipment export market. This research work focuses on export value forecasting with the use of future exogenous variables. Exogenous factors are essential for predicting market changes and, as a result, support the forecasting of precise export values.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123138804","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 : 2022-09-22DOI: 10.36548/jucct.2022.3.008
Vijay N. Yadav, S. Shakya
Sentiment analysis and topic modeling has wide range of applications from medical to entertainment industry, corporates, politics and so on. News media play vital role in shaping the views of public towards any product or people. The dataset used for this work is news headlines dataset of one of the leading new portals of India i.e., Times of India. This research aims to perform comparative study of both supervised and unsupervised learning for text analysis and use the best performing models in both the category for prediction of sentiment and topic classification of news headlines. For sentiment analysis, supervised techniques like Machine learning ensemble model and Bi-LSTM have used. Similarly, unsupervised techniques like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) have been for topic modeling.
{"title":"Sentiment Analysis and Topic Modeling on News Headlines","authors":"Vijay N. Yadav, S. Shakya","doi":"10.36548/jucct.2022.3.008","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.008","url":null,"abstract":"Sentiment analysis and topic modeling has wide range of applications from medical to entertainment industry, corporates, politics and so on. News media play vital role in shaping the views of public towards any product or people. The dataset used for this work is news headlines dataset of one of the leading new portals of India i.e., Times of India. This research aims to perform comparative study of both supervised and unsupervised learning for text analysis and use the best performing models in both the category for prediction of sentiment and topic classification of news headlines. For sentiment analysis, supervised techniques like Machine learning ensemble model and Bi-LSTM have used. Similarly, unsupervised techniques like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) have been for topic modeling.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327954","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 : 2022-09-21DOI: 10.36548/jucct.2022.3.007
T. Senthilkumar
This literature review article compiles works that describe the use of bio-inspired algorithms in Unmanned Aerial Vehicle (UAV) motion planning. This review demonstrates the usefulness of the various frameworks by presenting the contributions and limits of each article. The optimization method also decreases the amount of inaccuracy in the system’s convergence. Furthermore, this study discusses the assessment procedures and draws attention to the novelties and limitations of the explored methods. The paper wraps up with a detailed examination of the current difficulties and potential future research directions. This research will aid scholars in comprehending the state-of-the-art efforts made in UAV motion planning using a variety of optimization strategies.
{"title":"Comprehensive Review on UAV Efficient Path Planning Techniques for Optimized Applications","authors":"T. Senthilkumar","doi":"10.36548/jucct.2022.3.007","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.007","url":null,"abstract":"This literature review article compiles works that describe the use of bio-inspired algorithms in Unmanned Aerial Vehicle (UAV) motion planning. This review demonstrates the usefulness of the various frameworks by presenting the contributions and limits of each article. The optimization method also decreases the amount of inaccuracy in the system’s convergence. Furthermore, this study discusses the assessment procedures and draws attention to the novelties and limitations of the explored methods. The paper wraps up with a detailed examination of the current difficulties and potential future research directions. This research will aid scholars in comprehending the state-of-the-art efforts made in UAV motion planning using a variety of optimization strategies.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129544197","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 : 2022-09-16DOI: 10.36548/jucct.2022.3.006
Ravi Shankar Mishra
High-quality data might be difficult to be produced when there is a large quantity of information in a single educational dataset. Researchers in the field of educational data mining have recently begun to rely more and more on data mining methodologies in their investigations. However, instead of undertaking feature selection methods, many research investigations have focused on picking appropriate learning algorithms. Since these datasets are computationally complicated, they need a lot of computing time for categorization. This article examines the use of wrapper approaches for the purpose of managing high-dimensional datasets in order to pick appropriate features for a machine learning approach. This study then suggests a strategy for improving the quality of student or educational datasets. For future investigations, the suggested framework that utilizes filter and wrapper-based approaches may be used for many medical and industrial datasets.
{"title":"High Dimensional Datasets Optimization handling by Wrapper Sequential Feature Selection in Forward Mode - A Comparative Survey","authors":"Ravi Shankar Mishra","doi":"10.36548/jucct.2022.3.006","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.006","url":null,"abstract":"High-quality data might be difficult to be produced when there is a large quantity of information in a single educational dataset. Researchers in the field of educational data mining have recently begun to rely more and more on data mining methodologies in their investigations. However, instead of undertaking feature selection methods, many research investigations have focused on picking appropriate learning algorithms. Since these datasets are computationally complicated, they need a lot of computing time for categorization. This article examines the use of wrapper approaches for the purpose of managing high-dimensional datasets in order to pick appropriate features for a machine learning approach. This study then suggests a strategy for improving the quality of student or educational datasets. For future investigations, the suggested framework that utilizes filter and wrapper-based approaches may be used for many medical and industrial datasets.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121093266","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 : 2022-09-15DOI: 10.36548/jucct.2022.3.005
Sivaraman Eswaran
This overview of study intends to provide a thorough taxonomy of sustainable cloud computing capacity planning strategies. Several academic and industrial organizations have suggested several approaches to sustainability, and this taxonomy is used to analyze them. These modern methods have been analyzed and grouped together according to their shared traits and characteristics. This study takes a holistic look at sustainable Cloud Data Centers (CDCs), surveying the supporting methods and technologies along the way. It provides examples of successful capacity planning in sustainable CDCs based on research and practice from academia and industry. Moreover, the paper presents the most recent findings on what it takes to make CDCs viable. In addition, the difficulties of integration and the unanswered questions of sustainable CDC research have been discussed.
{"title":"A Taxonomy and Capacity Planning Technique for Sustainable Cloud Computing – An Extensive Overview","authors":"Sivaraman Eswaran","doi":"10.36548/jucct.2022.3.005","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.005","url":null,"abstract":"This overview of study intends to provide a thorough taxonomy of sustainable cloud computing capacity planning strategies. Several academic and industrial organizations have suggested several approaches to sustainability, and this taxonomy is used to analyze them. These modern methods have been analyzed and grouped together according to their shared traits and characteristics. This study takes a holistic look at sustainable Cloud Data Centers (CDCs), surveying the supporting methods and technologies along the way. It provides examples of successful capacity planning in sustainable CDCs based on research and practice from academia and industry. Moreover, the paper presents the most recent findings on what it takes to make CDCs viable. In addition, the difficulties of integration and the unanswered questions of sustainable CDC research have been discussed.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133824935","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 : 2022-09-15DOI: 10.36548/jucct.2022.3.004
Surekha Lanka
Everyone has their own distinct musical preferences; it's safe to assume that each music will find an appreciative audience. It's important to note that there isn't a single human society that has ever survived without music. There are two major gains from this study. Initially, a multi-strategy approach is taken to develop hybrid recommendation algorithms that give more accuracy than the existing algorithms. Also this hybrid algorithm is used to find new music in real time. This allows the algorithm to make an educated guess as to which musician and song best suit the user. As a second step, a general context-aware and emotion-based customized music framework is offered to facilitate the quick growth of context-aware music recommendation systems and to shed light on the whole recommendation procedure. Multiple methods exist for responding to requests, and a general framework is required for both collecting these methods and interpreting them within the context of the proposed framework. The kind of recommendation algorithm used is decided by the format of the input.
{"title":"Application of Hybrid Filtering Strategies in Music Recommendation System","authors":"Surekha Lanka","doi":"10.36548/jucct.2022.3.004","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.004","url":null,"abstract":"Everyone has their own distinct musical preferences; it's safe to assume that each music will find an appreciative audience. It's important to note that there isn't a single human society that has ever survived without music. There are two major gains from this study. Initially, a multi-strategy approach is taken to develop hybrid recommendation algorithms that give more accuracy than the existing algorithms. Also this hybrid algorithm is used to find new music in real time. This allows the algorithm to make an educated guess as to which musician and song best suit the user. As a second step, a general context-aware and emotion-based customized music framework is offered to facilitate the quick growth of context-aware music recommendation systems and to shed light on the whole recommendation procedure. Multiple methods exist for responding to requests, and a general framework is required for both collecting these methods and interpreting them within the context of the proposed framework. The kind of recommendation algorithm used is decided by the format of the input.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"1996 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132441353","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 : 2022-09-15DOI: 10.36548/jucct.2022.3.003
R. Krishnan
Known for excellent convenience and abundant facilities, smart cities offer CCTV, delivery robots, security robots, and so on to its residents. Along with the collaboration of IoT (Internet Of Things), the innovation of smart city has gained immense attraction at present. Besides, the risks and challenging in the field of telecommunication still persists as the implemented wireless networks results in traffic and anomaly behaviour. Such issues become critical in case of large-scale infrastructure networks like WSN’s. As such circumstances, to perform efficient health and environment monitoring, the need for a next generation networked system raises. As the traditional anomaly detection schemes doesn’t work out for delay-sensitive environments due to increased latency, we propose a scalable, hybrid spatiotemporal anomaly detection approach that can effectively detect potential anomalies in the network. With the use of real-time stream processing, and other methodologies like Software-Defined Networking (SDN), a Fog Computing-based 5G low-power Wide Area Network (LPWAN) solution is developed and tested on a Antwerp’s City of Things testbed. The proposed approach is found to be beneficial when deployed in a real network environment with nearly 1800 sensor nodes.
{"title":"Fog Computing-Based 5G LPWAN Anomaly Detection for Smart Cities","authors":"R. Krishnan","doi":"10.36548/jucct.2022.3.003","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.003","url":null,"abstract":"Known for excellent convenience and abundant facilities, smart cities offer CCTV, delivery robots, security robots, and so on to its residents. Along with the collaboration of IoT (Internet Of Things), the innovation of smart city has gained immense attraction at present. Besides, the risks and challenging in the field of telecommunication still persists as the implemented wireless networks results in traffic and anomaly behaviour. Such issues become critical in case of large-scale infrastructure networks like WSN’s. As such circumstances, to perform efficient health and environment monitoring, the need for a next generation networked system raises. As the traditional anomaly detection schemes doesn’t work out for delay-sensitive environments due to increased latency, we propose a scalable, hybrid spatiotemporal anomaly detection approach that can effectively detect potential anomalies in the network. With the use of real-time stream processing, and other methodologies like Software-Defined Networking (SDN), a Fog Computing-based 5G low-power Wide Area Network (LPWAN) solution is developed and tested on a Antwerp’s City of Things testbed. The proposed approach is found to be beneficial when deployed in a real network environment with nearly 1800 sensor nodes.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130547502","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 : 2022-09-03DOI: 10.36548/jucct.2022.3.002
Vibhuti Gupta, Megha Gupta
A chatbot is a work of artificial intelligence technology that simulates a conversation (or chat) in natural language with a user via messaging applications, internet sites, smartphone apps, or the telephone. Chatbots are used in a range of conversation systems for a variety of purposes, including customer assistance, request processing, and information acquisition. Chatbots have been around for quite some time, but it has only been in the recent past few years that they have seen a significant uptick in popularity among consumers and companies. This change in the perspective of chatbots and conversational interfaces was heavily impacted by the advancements in artificial intelligence and machine learning, as well as by the expanding usage of messaging app technologies. This study offers a comprehensive analysis of the conversational tool known as chatbots, which emerged in the contemporary era. This paper also discusses how this tool is expanding its root in the life of human beings as well as the pros-cons that will be generated by the chatbots.
{"title":"A Modern Tool of Conversation: Chatbot","authors":"Vibhuti Gupta, Megha Gupta","doi":"10.36548/jucct.2022.3.002","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.002","url":null,"abstract":"A chatbot is a work of artificial intelligence technology that simulates a conversation (or chat) in natural language with a user via messaging applications, internet sites, smartphone apps, or the telephone. Chatbots are used in a range of conversation systems for a variety of purposes, including customer assistance, request processing, and information acquisition. Chatbots have been around for quite some time, but it has only been in the recent past few years that they have seen a significant uptick in popularity among consumers and companies. This change in the perspective of chatbots and conversational interfaces was heavily impacted by the advancements in artificial intelligence and machine learning, as well as by the expanding usage of messaging app technologies. This study offers a comprehensive analysis of the conversational tool known as chatbots, which emerged in the contemporary era. This paper also discusses how this tool is expanding its root in the life of human beings as well as the pros-cons that will be generated by the chatbots.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134388495","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 : 2022-08-24DOI: 10.36548/jucct.2022.3.001
S. Deepika, B. Kaviyadharshini, S. Sharmila, S. N. Sangeethaa, S. Jothimani
The detection and resolution of any crime is made possible with the use of modern technology. The crime is discovered, the suspect is named, and then the court is presented with sufficient proof to show that the crime was committed by the named suspect. The proofs could be mental or physical. The best lie detector now in existence, according to this invention, is reported to be able to catch even sneaky crooks who successfully pass the standard polygraph test. Criminal investigators gather physical evidence, which can be destroyed, while mental evidence is preserved in the brain and cannot be erased. The brain wave reaction of an individual to crime-related images or phrases displayed on a computer screen can be used to analyze those evidences, using Electroencephalography (EEG). This novel Brain Fingerprinting technique uses brainwaves, which can be used to determine if the test participant remembers the specifics of the incident. The brain wave issuer will trap him even if they are consciously hiding the required information. Over 120 studies, including testing on Federal agents, testing for the United States intelligence agency and the US Navy, tests on actual cases, including felony crimes, have demonstrated that brain fingerprinting is 100 percent accurate.
{"title":"A Study on Brain Fingerprinting Technology","authors":"S. Deepika, B. Kaviyadharshini, S. Sharmila, S. N. Sangeethaa, S. Jothimani","doi":"10.36548/jucct.2022.3.001","DOIUrl":"https://doi.org/10.36548/jucct.2022.3.001","url":null,"abstract":"The detection and resolution of any crime is made possible with the use of modern technology. The crime is discovered, the suspect is named, and then the court is presented with sufficient proof to show that the crime was committed by the named suspect. The proofs could be mental or physical. The best lie detector now in existence, according to this invention, is reported to be able to catch even sneaky crooks who successfully pass the standard polygraph test. Criminal investigators gather physical evidence, which can be destroyed, while mental evidence is preserved in the brain and cannot be erased. The brain wave reaction of an individual to crime-related images or phrases displayed on a computer screen can be used to analyze those evidences, using Electroencephalography (EEG). This novel Brain Fingerprinting technique uses brainwaves, which can be used to determine if the test participant remembers the specifics of the incident. The brain wave issuer will trap him even if they are consciously hiding the required information. Over 120 studies, including testing on Federal agents, testing for the United States intelligence agency and the US Navy, tests on actual cases, including felony crimes, have demonstrated that brain fingerprinting is 100 percent accurate.","PeriodicalId":443052,"journal":{"name":"Journal of Ubiquitous Computing and Communication Technologies","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116095865","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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