Pub Date : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076124
Vikas Yadav, M. Kumar
Cloud computing is simply exchanging data over a digital network to a remote location. In spite of security risks, cloud computing has become a necessity of human digital life therefore the data must be safeguard from exploiters. There are numerous algorithms widely known to secure intelligence along the new branch of data encryption evolving and forming in parallel. Presented paper combines three such branches of cryptography i.e. symmetric, asymmetric, and biological DNA cryptography. The goal is to find a balance between increasing complexity and the time taken for encryption. This paper is an attempt to unlock the future of cryptography where an amalgam of traditional ciphers used with modernera advanced biological ciphers and check their cross-functional compatibility with each other. The resulting algorithm lies between the symmetric and asymmetric cryptography on both parameters i.e. complexity and processing time. The resulting hybrid algorithm found at least 20.7398% faster than traditional RSA algorithm with bio complexity superior to AES algorithm for package greater than 200 kilobytes.
{"title":"A Hybrid Cryptography Approach Using Symmetric, Asymmetric and DNA Based Encryption","authors":"Vikas Yadav, M. Kumar","doi":"10.1109/ICCT56969.2023.10076124","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076124","url":null,"abstract":"Cloud computing is simply exchanging data over a digital network to a remote location. In spite of security risks, cloud computing has become a necessity of human digital life therefore the data must be safeguard from exploiters. There are numerous algorithms widely known to secure intelligence along the new branch of data encryption evolving and forming in parallel. Presented paper combines three such branches of cryptography i.e. symmetric, asymmetric, and biological DNA cryptography. The goal is to find a balance between increasing complexity and the time taken for encryption. This paper is an attempt to unlock the future of cryptography where an amalgam of traditional ciphers used with modernera advanced biological ciphers and check their cross-functional compatibility with each other. The resulting algorithm lies between the symmetric and asymmetric cryptography on both parameters i.e. complexity and processing time. The resulting hybrid algorithm found at least 20.7398% faster than traditional RSA algorithm with bio complexity superior to AES algorithm for package greater than 200 kilobytes.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125593326","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076043
Ram Krishna Jha, Parvez Alam, Nitin Priyadarshi, Mohammad Asad Ghazi, M. Bhargavi
Fake drugs are a growing serious issue related to the healthcare industry which causes extraordinary dangers to the society and the environment. Tracing the drugs at every step of the pharma supply chain is a difficult task. The goal of the research is to create a blockchain-based model that can forestall drug forging in drug store networks. The scope of the research lies where irregularities in the pharmaceutical supply chain affect public health and economy. The advanced elements of Blockchain makes it fit for complete recognizability of medications from producer to end patients, and the capacity to prevent fake prescriptions or medications. Drugs change proprietorship from producers to distributors and afterward retailers before it arrives at the client. Through the proposed model, the producer would have the option to transfer the subtleties relating to a medication, after which it will be sent for endorsement to the Government. From there on, distributors and retailers, in view of their prerequisites, can demand the endorsed drugs. Later on, in the event that a patient needs some drug, the person should demand it on the blockchain network. The solicitation will be shipped off the closest retailer/drug store and from that point, the patient can gather their medicine. Hyperledger fabric is utilized because they are permissioned and have an open source blockchain framework, meaning all the organization and pears are well known and confirmed. Our execution of the proposed blockchain based model features that the model can effectively forestall any medication being fake. This will be advantageous for the clients getting impacted with fake medications.
{"title":"Counterfeit Drug Prevention in Pharma Supply Chain using Blockchain Technology","authors":"Ram Krishna Jha, Parvez Alam, Nitin Priyadarshi, Mohammad Asad Ghazi, M. Bhargavi","doi":"10.1109/ICCT56969.2023.10076043","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076043","url":null,"abstract":"Fake drugs are a growing serious issue related to the healthcare industry which causes extraordinary dangers to the society and the environment. Tracing the drugs at every step of the pharma supply chain is a difficult task. The goal of the research is to create a blockchain-based model that can forestall drug forging in drug store networks. The scope of the research lies where irregularities in the pharmaceutical supply chain affect public health and economy. The advanced elements of Blockchain makes it fit for complete recognizability of medications from producer to end patients, and the capacity to prevent fake prescriptions or medications. Drugs change proprietorship from producers to distributors and afterward retailers before it arrives at the client. Through the proposed model, the producer would have the option to transfer the subtleties relating to a medication, after which it will be sent for endorsement to the Government. From there on, distributors and retailers, in view of their prerequisites, can demand the endorsed drugs. Later on, in the event that a patient needs some drug, the person should demand it on the blockchain network. The solicitation will be shipped off the closest retailer/drug store and from that point, the patient can gather their medicine. Hyperledger fabric is utilized because they are permissioned and have an open source blockchain framework, meaning all the organization and pears are well known and confirmed. Our execution of the proposed blockchain based model features that the model can effectively forestall any medication being fake. This will be advantageous for the clients getting impacted with fake medications.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129655111","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075912
Adwitiya Pratap Singh, Nisarg Upadhyay, V. G. Shankar, B. Devi
It has been ascertained in recent studies that AD (Alzheimer's disease, a neurodegenerative disorder) and its earlier stages can be detected by neuroimaging biomarkers. To the prevention, previous studies have focused on volumetric asymmetry and brain atrophy. The identification of AD in its early stages has proven to be imperative, as Alzheimer's disease cannot be cured and can only slow its progression. Developing on that idea, this study aims to use the discriminative powers of a Siamese-inspired identical hybrid neural network for the task of classification between multiple stages. The proposed method uses a homemade pipeline for preprocessing and removing other unwanted components from the MRIs that might disturb the model. Registering all the MRI images to MNI space and resampling the slices helped in normalizing the whole dataset. This study uses feature-based methods to work with low-dimensional characteristics rather than high-dimensional voxel data can lessen computing cost and time spent. We used VGG-16 style net with image-net weights for the purpose of automatic feature extraction. T1-weighted MRIs were used for the research, which were accessed from the ADNI datasets ADNI2 and ADNI3. When compared to a normal DNN, our proposed identical hybrid neural network achieved better precision and F1-score.
{"title":"IHDNA: Identical Hybrid Deep Neural Networks for Alzheimer's Detection using MRI Dataset","authors":"Adwitiya Pratap Singh, Nisarg Upadhyay, V. G. Shankar, B. Devi","doi":"10.1109/ICCT56969.2023.10075912","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075912","url":null,"abstract":"It has been ascertained in recent studies that AD (Alzheimer's disease, a neurodegenerative disorder) and its earlier stages can be detected by neuroimaging biomarkers. To the prevention, previous studies have focused on volumetric asymmetry and brain atrophy. The identification of AD in its early stages has proven to be imperative, as Alzheimer's disease cannot be cured and can only slow its progression. Developing on that idea, this study aims to use the discriminative powers of a Siamese-inspired identical hybrid neural network for the task of classification between multiple stages. The proposed method uses a homemade pipeline for preprocessing and removing other unwanted components from the MRIs that might disturb the model. Registering all the MRI images to MNI space and resampling the slices helped in normalizing the whole dataset. This study uses feature-based methods to work with low-dimensional characteristics rather than high-dimensional voxel data can lessen computing cost and time spent. We used VGG-16 style net with image-net weights for the purpose of automatic feature extraction. T1-weighted MRIs were used for the research, which were accessed from the ADNI datasets ADNI2 and ADNI3. When compared to a normal DNN, our proposed identical hybrid neural network achieved better precision and F1-score.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131630017","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076117
Shreya Gupta, Amit Kumar Bairwa, Satpal Singh Kushwaha, Sandeep Joshi
In the recent past, with the advancement of tech-nologies, many changes have been made in Identity Management Systems across the globe. Identity cards have become an inextri-cable part of our lives. With time, authentication of a person's identity and data breaching has led to privacy concerns. The rapid growth of the population raises the challenge of storing large data securely. A new approach to data encryption and hashing can resolve concerns of privacy, security, data handling, and more. Blockchain Technology and the Internet Of Things will improve the current ecosystem. The devised model will be using the AES algorithm for the encryption of biometrics and demographic data. Additionally, the symmetrically distributed storage system, the InterPlanetary File System(IPFS) will be used and data will be hashed by SHA-256. Furthermore, key pairs will be assigned to IoT devices to store the data on a distributed ledger system. This paper will also introduce the merging of Blockchain and IoT, thus ensuring better security and connectivity in a decentralized manner.
{"title":"Decentralized Identity Management System using the amalgamation of Blockchain Technology","authors":"Shreya Gupta, Amit Kumar Bairwa, Satpal Singh Kushwaha, Sandeep Joshi","doi":"10.1109/ICCT56969.2023.10076117","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076117","url":null,"abstract":"In the recent past, with the advancement of tech-nologies, many changes have been made in Identity Management Systems across the globe. Identity cards have become an inextri-cable part of our lives. With time, authentication of a person's identity and data breaching has led to privacy concerns. The rapid growth of the population raises the challenge of storing large data securely. A new approach to data encryption and hashing can resolve concerns of privacy, security, data handling, and more. Blockchain Technology and the Internet Of Things will improve the current ecosystem. The devised model will be using the AES algorithm for the encryption of biometrics and demographic data. Additionally, the symmetrically distributed storage system, the InterPlanetary File System(IPFS) will be used and data will be hashed by SHA-256. Furthermore, key pairs will be assigned to IoT devices to store the data on a distributed ledger system. This paper will also introduce the merging of Blockchain and IoT, thus ensuring better security and connectivity in a decentralized manner.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"10 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132232741","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075749
Anushka Sharma, Rishabh Kaushal
Because of the rise in online hatred, the research communities of artificial intelligence, particularly natural language processing, have been developing models for identifying online hatred. Recently, code-mixing, or the usage of multiple languages in social media conversations, has made multilingual hatred a significant difficulty for automated detection. The crucial task involved in NLP is identifying inciting hatred in writings on social networking sites. This work has several relevant applications, including analysis of sentiments, cyberbullying in online world, and societal & political conflict studies. Using tweets that have been put online on Twitter, we analyze the issue of hatred detection in multilingual functionality in this paper. The tweets have the text annotations and the speech category (Normal speech or Hate speech) to which these belong. We, therefore, recommend a monitored method for detecting hatred. Additionally, the classification approach is provided, which uses certain characters level, words level, and lexicons-based features for identifying hate speech in the corpus. We obtain results of 96% accuracy in identifying posts across four classifiers. Index Terms—Hate speech, Multilingual, Code-mixing, NLP
{"title":"Detecting Hate Speech in Hindi in Online Social Media","authors":"Anushka Sharma, Rishabh Kaushal","doi":"10.1109/ICCT56969.2023.10075749","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075749","url":null,"abstract":"Because of the rise in online hatred, the research communities of artificial intelligence, particularly natural language processing, have been developing models for identifying online hatred. Recently, code-mixing, or the usage of multiple languages in social media conversations, has made multilingual hatred a significant difficulty for automated detection. The crucial task involved in NLP is identifying inciting hatred in writings on social networking sites. This work has several relevant applications, including analysis of sentiments, cyberbullying in online world, and societal & political conflict studies. Using tweets that have been put online on Twitter, we analyze the issue of hatred detection in multilingual functionality in this paper. The tweets have the text annotations and the speech category (Normal speech or Hate speech) to which these belong. We, therefore, recommend a monitored method for detecting hatred. Additionally, the classification approach is provided, which uses certain characters level, words level, and lexicons-based features for identifying hate speech in the corpus. We obtain results of 96% accuracy in identifying posts across four classifiers. Index Terms—Hate speech, Multilingual, Code-mixing, NLP","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"100 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134476507","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076194
Sagar Gupta, S. Vadlamudi
Cloud computing stimulated the development of agile software. The new oil is software. More than 70-90% of the software is open-source, and its usage is inevitable. Open source encourages innovation through collaboration, reduces Time-To-Market, and fuels breakthrough technologies from the past few decades. In a way, open source is eating software or driving the Software world. Open source communities involve more contributors /developers, sometimes posing substantial security challenges. Recently, we have witnessed SolarWinds compromising the entire supply chain, Log4j allowing access to execute code with critical zero-day vulnerability remotely. The digital universe paused because these zero-day vulnerabilities exploded as an outcome. In this work, we will highlight challenges and propose an approach to help organisations protect the code base by safely consuming the Open Source.
{"title":"Open-Source Software Security Challenges and Policies for Cloud Enterprises","authors":"Sagar Gupta, S. Vadlamudi","doi":"10.1109/ICCT56969.2023.10076194","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076194","url":null,"abstract":"Cloud computing stimulated the development of agile software. The new oil is software. More than 70-90% of the software is open-source, and its usage is inevitable. Open source encourages innovation through collaboration, reduces Time-To-Market, and fuels breakthrough technologies from the past few decades. In a way, open source is eating software or driving the Software world. Open source communities involve more contributors /developers, sometimes posing substantial security challenges. Recently, we have witnessed SolarWinds compromising the entire supply chain, Log4j allowing access to execute code with critical zero-day vulnerability remotely. The digital universe paused because these zero-day vulnerabilities exploded as an outcome. In this work, we will highlight challenges and propose an approach to help organisations protect the code base by safely consuming the Open Source.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121200899","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075906
Harshal Shrimali, Rahul Saxena, Kavita
We launched a new method of recommending youTube videos. Our algorithm suggests videos based on their semantic similarity to the query using the transcripts of the videos and ranking them using natural language ranking algorithms. In terms of quality and information, our method has the potential to provide better-related videos than tag-based recommendation. This can be useful for recommending educational videos and can be used on ed-tech platforms and MOOC course platforms.
{"title":"Content based Video Recommendation System","authors":"Harshal Shrimali, Rahul Saxena, Kavita","doi":"10.1109/ICCT56969.2023.10075906","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075906","url":null,"abstract":"We launched a new method of recommending youTube videos. Our algorithm suggests videos based on their semantic similarity to the query using the transcripts of the videos and ranking them using natural language ranking algorithms. In terms of quality and information, our method has the potential to provide better-related videos than tag-based recommendation. This can be useful for recommending educational videos and can be used on ed-tech platforms and MOOC course platforms.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122736467","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075913
N. Gupta, Faizan Khan, Bhavna Saini
According to statistics, drowsy driving is the leading cause of accidents worldwide that result in the loss of precious lives and worsen public health. When a driver is fatigued, cameras can be employed to detect their drowsiness and inform them well before which can help in decreasing accidents. This work employes a transfer Learning model DenseNet to identify the driver drowsiness in real time. The MRL eye dataset of 84923 images has been used and the model works well with 91.56% accuracy.
{"title":"Real Time Driver Drowsiness Detecion using Transfer learning","authors":"N. Gupta, Faizan Khan, Bhavna Saini","doi":"10.1109/ICCT56969.2023.10075913","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075913","url":null,"abstract":"According to statistics, drowsy driving is the leading cause of accidents worldwide that result in the loss of precious lives and worsen public health. When a driver is fatigued, cameras can be employed to detect their drowsiness and inform them well before which can help in decreasing accidents. This work employes a transfer Learning model DenseNet to identify the driver drowsiness in real time. The MRL eye dataset of 84923 images has been used and the model works well with 91.56% accuracy.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132498530","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10075901
Samparthi V S Kumar, Hari Kishan Kondaveerti
Naturally, birds appear around us at different locations in a variety of sizes, shapes, and colors. Bird species recognition provides crucial information on the state of the environment. Manual collection and processing of bird species data for the identification of birds is a huge task for ornithologists. Automatic bird recognition systems reduce their burden to some extent by collecting, processing bird related information and identifying the birds automatically. In this view, this paper presents a comparative study of the performances of MobileNet, AlexNet, InceptionResNet V2, Inception V3, and EfficientNet on bird species recognition. We gathered 11488 images of 200 bird species from the Kaggle dataset and increased the number of images to 40000 using data augmentation techniques. The experiment results shows that MobileNet and EfficientNet are the quickest training models. EfficientNet is outperforming the other models with test accuracy of 87.13%.
{"title":"A Comparative Study on Deep Learning Techniques for Bird Species Recognition","authors":"Samparthi V S Kumar, Hari Kishan Kondaveerti","doi":"10.1109/ICCT56969.2023.10075901","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10075901","url":null,"abstract":"Naturally, birds appear around us at different locations in a variety of sizes, shapes, and colors. Bird species recognition provides crucial information on the state of the environment. Manual collection and processing of bird species data for the identification of birds is a huge task for ornithologists. Automatic bird recognition systems reduce their burden to some extent by collecting, processing bird related information and identifying the birds automatically. In this view, this paper presents a comparative study of the performances of MobileNet, AlexNet, InceptionResNet V2, Inception V3, and EfficientNet on bird species recognition. We gathered 11488 images of 200 bird species from the Kaggle dataset and increased the number of images to 40000 using data augmentation techniques. The experiment results shows that MobileNet and EfficientNet are the quickest training models. EfficientNet is outperforming the other models with test accuracy of 87.13%.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125884111","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 : 2023-01-19DOI: 10.1109/ICCT56969.2023.10076139
Khushbu Garg, N. N. Das, Gaurav Aggrawal
Autism spectrum disorder (ASD) is a mental ailment that can be diagnosed by the study of social media data and biopsy. Those with autism spectrum disorder (ASD), a neurodevelopment condition, may experience permanent changes to their facial appearance over time. The faces of children with ASD are easily identifiable from those of normally developing (TD) children. After the toddler years, specialists will typically look at a child's behaviour patterns to make a diagnosis of autism spectrum disorders (ASD). Quicker intervention and better long-term outcomes are possible after an early diagnosis of autism spectrum disorder. Machine learning uses data science to facilitate early autism diagnoses. This literature review aims to bridge a gap in understanding by bringing together the results of recent studies and technologies that use machine learning based approaches for ASD screening in infants and children younger than 18 months. Individuals on the autism spectrum have restricted interests and behaviors and struggle to communicate and interact socially with others. The prevalence of ASD has increased in recent years. The potential for innovative methods like machine learning to be integrated into established therapeutic practices is quite encouraging. How computers can be taught to recognize patterns in data is the focus of machine learning research. Artificially intelligent technologies can identify signs and symptoms, organize data, make diagnoses, and forecast outcomes. Machine learning is an area of artificial intelligence that focuses on teaching computers new behaviors by observing how they interact with the world. These days, there are a wide variety of machine learning methods available. In this piece, we look at the existing literature on the frequency of ASD in the general community. The main interest of this paper is the academic literature were sought by searching numerous databases.
{"title":"A Review On: Autism Spectrum Disorder Detection by Machine Learning Using Small Video","authors":"Khushbu Garg, N. N. Das, Gaurav Aggrawal","doi":"10.1109/ICCT56969.2023.10076139","DOIUrl":"https://doi.org/10.1109/ICCT56969.2023.10076139","url":null,"abstract":"Autism spectrum disorder (ASD) is a mental ailment that can be diagnosed by the study of social media data and biopsy. Those with autism spectrum disorder (ASD), a neurodevelopment condition, may experience permanent changes to their facial appearance over time. The faces of children with ASD are easily identifiable from those of normally developing (TD) children. After the toddler years, specialists will typically look at a child's behaviour patterns to make a diagnosis of autism spectrum disorders (ASD). Quicker intervention and better long-term outcomes are possible after an early diagnosis of autism spectrum disorder. Machine learning uses data science to facilitate early autism diagnoses. This literature review aims to bridge a gap in understanding by bringing together the results of recent studies and technologies that use machine learning based approaches for ASD screening in infants and children younger than 18 months. Individuals on the autism spectrum have restricted interests and behaviors and struggle to communicate and interact socially with others. The prevalence of ASD has increased in recent years. The potential for innovative methods like machine learning to be integrated into established therapeutic practices is quite encouraging. How computers can be taught to recognize patterns in data is the focus of machine learning research. Artificially intelligent technologies can identify signs and symptoms, organize data, make diagnoses, and forecast outcomes. Machine learning is an area of artificial intelligence that focuses on teaching computers new behaviors by observing how they interact with the world. These days, there are a wide variety of machine learning methods available. In this piece, we look at the existing literature on the frequency of ASD in the general community. The main interest of this paper is the academic literature were sought by searching numerous databases.","PeriodicalId":128100,"journal":{"name":"2023 3rd International Conference on Intelligent Communication and Computational Techniques (ICCT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129872817","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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