There is a damage to the health of civil structures over the time due to aging and loading effects i.e., wind,earthquakes etc. To identify these hidden damages at the earliest, some reliable damage detection techniques are required. In addition, it becomes essential to monitor the performance of structural integrity and it results in the increased life span of the structures. In this paper, the average energy entropy scheme based on the discrete wavelet transform is proposed to detect incipient damage in the structures. The measured response is first decomposed into a set of wavelet components and average energy entropy is computed. The proposed method is applied to the simulated response of the beam obtained with different crack levels, and performance is compared to the approximate entropy. The results obtained from the proposed method illustrate a consistent and reliable damage indicator in comparison with the existing method.
{"title":"Damage Identification of Beam Structure Using Discrete wavelet transform","authors":"Manish Kumar, Sandeep Dhariwal, Roshan Kumar, Arvind R. Yadav, Evans Amponsah, Prasanna K.Singh","doi":"10.1109/AISP53593.2022.9760606","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760606","url":null,"abstract":"There is a damage to the health of civil structures over the time due to aging and loading effects i.e., wind,earthquakes etc. To identify these hidden damages at the earliest, some reliable damage detection techniques are required. In addition, it becomes essential to monitor the performance of structural integrity and it results in the increased life span of the structures. In this paper, the average energy entropy scheme based on the discrete wavelet transform is proposed to detect incipient damage in the structures. The measured response is first decomposed into a set of wavelet components and average energy entropy is computed. The proposed method is applied to the simulated response of the beam obtained with different crack levels, and performance is compared to the approximate entropy. The results obtained from the proposed method illustrate a consistent and reliable damage indicator in comparison with the existing method.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86414430","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760638
VishnuPriya Thotakura, Purnachand Nalluri
Activity recognition in videos using deep learning has shown phenomenal progress in the last decade. The community of computer vision has been working on video data for about a decade and solved many uncertainties. Various research groups have presented many convolutional neural network architectures to solve the issues related with classification, and many more computer vision tasks. All these networks were about two dimensional image data. Recently research community of Face book introduced a network architecture called C3D network which have three dimensional convolution layers. The C3D network is performing well in activity recognition from large-scale videos, video classification tasks. This article is focused on importance of C3D network by mentioning the drawbacks of hand crafted as well as deep learning methods, architecture of C3D network, contrasts in 2D and 3D CNNs, review on different deep learning models employed for activity detection from videos and compared the performance of various anomaly detection approaches with the proposed C3D network.
{"title":"Convolutional 3D in Activity Recognition -A Review","authors":"VishnuPriya Thotakura, Purnachand Nalluri","doi":"10.1109/AISP53593.2022.9760638","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760638","url":null,"abstract":"Activity recognition in videos using deep learning has shown phenomenal progress in the last decade. The community of computer vision has been working on video data for about a decade and solved many uncertainties. Various research groups have presented many convolutional neural network architectures to solve the issues related with classification, and many more computer vision tasks. All these networks were about two dimensional image data. Recently research community of Face book introduced a network architecture called C3D network which have three dimensional convolution layers. The C3D network is performing well in activity recognition from large-scale videos, video classification tasks. This article is focused on importance of C3D network by mentioning the drawbacks of hand crafted as well as deep learning methods, architecture of C3D network, contrasts in 2D and 3D CNNs, review on different deep learning models employed for activity detection from videos and compared the performance of various anomaly detection approaches with the proposed C3D network.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84146234","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760653
Puja Acharya, J. Kumar, Vineet Dahiya
The main motive of this paper is to design an antenna using metamaterial that provides lower return losses, high gain and good performance in X and Ku Bands. The antenna is a violin shaped structure and resonates at 9.4 GHz and 14.2 GHz. The center frequency Fr is 10 GHz. The design is finalized by employing metamaterial based split circular ring resonator. RT Duroid 5880 is used as substrate and patch is design on 20 x 20x 1.588 mm substrate body. Comparison is also studied by employing (CSSR) Complementary Split Rectangular structure and different results have been simulated on HFSS software showcasing the return loss and VSWR. The E and H patterns lies on same plane and good gain is achieved up to 16dB. The simulated results show –15 dB and –21 dB impedance bandwidth for X-band applications which is in range of 9.42 GHz and for Ku-band applications which is in range of 14.2 GHz. Circular notch is also inserted to have good bandwidth. The radiation efficiency is found to be approximately 98% for the bandwidth. The VSWR is also less than 2 for both bands which makes it suitable for applications in satellite and radar communications.
{"title":"A Novel Violin Type UWB Patch Antenna for X & Ku Bands using metamaterial: A Review","authors":"Puja Acharya, J. Kumar, Vineet Dahiya","doi":"10.1109/AISP53593.2022.9760653","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760653","url":null,"abstract":"The main motive of this paper is to design an antenna using metamaterial that provides lower return losses, high gain and good performance in X and Ku Bands. The antenna is a violin shaped structure and resonates at 9.4 GHz and 14.2 GHz. The center frequency Fr is 10 GHz. The design is finalized by employing metamaterial based split circular ring resonator. RT Duroid 5880 is used as substrate and patch is design on 20 x 20x 1.588 mm substrate body. Comparison is also studied by employing (CSSR) Complementary Split Rectangular structure and different results have been simulated on HFSS software showcasing the return loss and VSWR. The E and H patterns lies on same plane and good gain is achieved up to 16dB. The simulated results show –15 dB and –21 dB impedance bandwidth for X-band applications which is in range of 9.42 GHz and for Ku-band applications which is in range of 14.2 GHz. Circular notch is also inserted to have good bandwidth. The radiation efficiency is found to be approximately 98% for the bandwidth. The VSWR is also less than 2 for both bands which makes it suitable for applications in satellite and radar communications.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77787721","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760622
J. K. Panigrahi, D. P. Acharya, U. Nanda
A Dual Threshold CMOS(DTCMOS) based Current Starved Voltage Controlled Oscillator (CSVCO) has been analysed. The performance of a DTCMOS besed CSVCO is evaluated and compared to its conventional counterpart using 90nm CMOS process. The simulation results indicate an improvement of Frequency Tuning Range by 14% from the conventional CSVCO. The phase noise performance also improves by about 2 dBc/Hz at the designed oscillation frequency of 2.4GHz. The Figure of Merit (FoM) of this VCO is found to be -155.8, which is better than its conventional counterpart. The thermal stability of performance parameters of the said CSVCO is also analysed. The performance improvement for DTCMOS based CSVCO is achieved at a cost of little more power consumption than conventional CSVCO.
{"title":"Performance Analysis of Dual Threshold CMOS based Current Starved Voltage Controlled Oscillator - A Review","authors":"J. K. Panigrahi, D. P. Acharya, U. Nanda","doi":"10.1109/AISP53593.2022.9760622","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760622","url":null,"abstract":"A Dual Threshold CMOS(DTCMOS) based Current Starved Voltage Controlled Oscillator (CSVCO) has been analysed. The performance of a DTCMOS besed CSVCO is evaluated and compared to its conventional counterpart using 90nm CMOS process. The simulation results indicate an improvement of Frequency Tuning Range by 14% from the conventional CSVCO. The phase noise performance also improves by about 2 dBc/Hz at the designed oscillation frequency of 2.4GHz. The Figure of Merit (FoM) of this VCO is found to be -155.8, which is better than its conventional counterpart. The thermal stability of performance parameters of the said CSVCO is also analysed. The performance improvement for DTCMOS based CSVCO is achieved at a cost of little more power consumption than conventional CSVCO.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82615870","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760580
Charmee Mehta, Purvi Harniya, Sagar Kamat
In today’s world, machine learning is an emerging technology which is being used extensively in different domains. In order to offer effective solutions in the broad area of computer security with the use of machine learning (ML) models, applications which identify and protect against potential adversarial attacks are employed. In the ever-growing field of adversarial machine learning, attackers with different extents of accessibility to a machine learning model can launch a number of attacks to achieve their goals. Concurrently, ML models and algorithms are quite susceptible to various cybersecurity threats. In this paper, an in-depth survey has been carried out on the impact of cybersecurity in machine learning and the adversarial attacks which can be encountered in a ML based system.
{"title":"Comprehending and Detecting Vulnerabilities using Adversarial Machine Learning Attacks","authors":"Charmee Mehta, Purvi Harniya, Sagar Kamat","doi":"10.1109/AISP53593.2022.9760580","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760580","url":null,"abstract":"In today’s world, machine learning is an emerging technology which is being used extensively in different domains. In order to offer effective solutions in the broad area of computer security with the use of machine learning (ML) models, applications which identify and protect against potential adversarial attacks are employed. In the ever-growing field of adversarial machine learning, attackers with different extents of accessibility to a machine learning model can launch a number of attacks to achieve their goals. Concurrently, ML models and algorithms are quite susceptible to various cybersecurity threats. In this paper, an in-depth survey has been carried out on the impact of cybersecurity in machine learning and the adversarial attacks which can be encountered in a ML based system.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82854765","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760644
Hao Zhang, Peng Fei
Lensfree on-chip imaging bares a low spatial resolution. We develop an adaptive super-resolution based contact imaging technique on a CMOS chip. Without using insitu labels for training, this method achieves subcellular spatial resolution across the entire active chip area.
{"title":"Adaptive Super-resolution on-chip Imaging","authors":"Hao Zhang, Peng Fei","doi":"10.1109/AISP53593.2022.9760644","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760644","url":null,"abstract":"Lensfree on-chip imaging bares a low spatial resolution. We develop an adaptive super-resolution based contact imaging technique on a CMOS chip. Without using insitu labels for training, this method achieves subcellular spatial resolution across the entire active chip area.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83131189","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760664
Kamesh Sonti, R. Dhuli
Glaucoma is the leading eye disorder that may cause irreversible vision loss if not diagnosed quickly. Due to its invisible symptoms, it is very hard to detect glaucoma in the early stages hence increasing its impact and leads to blindness. Due to the limitations with the available medical tests, glaucoma diagnosis is preferred with computer-aided design (CAD) approach. Hence it is necessary to propose a model to diagnose glaucoma with retinal color fundus images. This paper proposed a new methodology based on local directional texture pattern (LDTP) descriptor and statistical texture features and classified using various machine learning schemes. The proposed method is validated on Drishti-GSI and ACRIMA datasets with 101 and 705 images respectively and evaluated performance with 10-fold cross validation and 70:30 split ratio approach and reported results with sufficient performance metric values. From the obtained simulation results and metrics, we state that our approach achieves good classification performance compared to other existing approaches.
{"title":"Pattern Based Glaucoma Classification Approach using Statistical Texture Features","authors":"Kamesh Sonti, R. Dhuli","doi":"10.1109/AISP53593.2022.9760664","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760664","url":null,"abstract":"Glaucoma is the leading eye disorder that may cause irreversible vision loss if not diagnosed quickly. Due to its invisible symptoms, it is very hard to detect glaucoma in the early stages hence increasing its impact and leads to blindness. Due to the limitations with the available medical tests, glaucoma diagnosis is preferred with computer-aided design (CAD) approach. Hence it is necessary to propose a model to diagnose glaucoma with retinal color fundus images. This paper proposed a new methodology based on local directional texture pattern (LDTP) descriptor and statistical texture features and classified using various machine learning schemes. The proposed method is validated on Drishti-GSI and ACRIMA datasets with 101 and 705 images respectively and evaluated performance with 10-fold cross validation and 70:30 split ratio approach and reported results with sufficient performance metric values. From the obtained simulation results and metrics, we state that our approach achieves good classification performance compared to other existing approaches.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77240883","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760551
Yerrababu Moukthika Reddy, Mounika Nadampalli, Asisa Kumar Panigrahy, Kunduri Surya Divyasree, A. Jahnavi, N. Vignesh
Living with the novel Coronavirus is becoming the new normal as nations around the globe resume. However, in order to stop the virus from spreading, we must isolate Covid-infected persons from the rest of the population.Fever is the most common symptom of coronavirus infection, according to the CDC [1], with up to 83 percent of symptomatic patients presenting indications of fever. Early symptom detection and good hygiene standards are therefore critical, particularly in situations where people come into random contact with one another. As a result, temperature checks and masks are now required in schools, colleges, offices, and other public spaces. However, manually monitoring each individual and measuring their respective body temperatures is a cumbersome task. Currently, most of the temperature checkups are done manually which can be inefficient, impractical, and riskybecause sometimes people checking manually may be reluctant to check every person’s temperature or sometimes allow people even if they violate the guidelines. Moreover, the person assigned to manually check will be at high risk as he is exposed to a lot of people. To solve these issues, we propose a project that reduces the growth of COVID-19 by monitoring the presence of a facial mask and measuring their temperature. The Face Mask Detection can be done using the TensorFlow software library, Mobilenet V2 architecture and OpenCV.A non-contact IR temperature sensor is used to monitor the individual’s body temperature. To avoid false positives, the system will be strengthened by training it with a variety of cases. Once the system detects a mask, it measures the body temperature of the person. If the temperature is within the normal range, sanitization is done,and the person is permitted entry through an IOT enabled smart door. However, if the system fails to detect a mask or the person’s temperature falls out of the predefined range, a buzzer rings and the door remains closed. Our model is intended to be effective in preventing the spread of this infectious disease.
{"title":"Automated Facemask Detection and Monitoring of Body Temperature using IoT Enabled Smart Door","authors":"Yerrababu Moukthika Reddy, Mounika Nadampalli, Asisa Kumar Panigrahy, Kunduri Surya Divyasree, A. Jahnavi, N. Vignesh","doi":"10.1109/AISP53593.2022.9760551","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760551","url":null,"abstract":"Living with the novel Coronavirus is becoming the new normal as nations around the globe resume. However, in order to stop the virus from spreading, we must isolate Covid-infected persons from the rest of the population.Fever is the most common symptom of coronavirus infection, according to the CDC [1], with up to 83 percent of symptomatic patients presenting indications of fever. Early symptom detection and good hygiene standards are therefore critical, particularly in situations where people come into random contact with one another. As a result, temperature checks and masks are now required in schools, colleges, offices, and other public spaces. However, manually monitoring each individual and measuring their respective body temperatures is a cumbersome task. Currently, most of the temperature checkups are done manually which can be inefficient, impractical, and riskybecause sometimes people checking manually may be reluctant to check every person’s temperature or sometimes allow people even if they violate the guidelines. Moreover, the person assigned to manually check will be at high risk as he is exposed to a lot of people. To solve these issues, we propose a project that reduces the growth of COVID-19 by monitoring the presence of a facial mask and measuring their temperature. The Face Mask Detection can be done using the TensorFlow software library, Mobilenet V2 architecture and OpenCV.A non-contact IR temperature sensor is used to monitor the individual’s body temperature. To avoid false positives, the system will be strengthened by training it with a variety of cases. Once the system detects a mask, it measures the body temperature of the person. If the temperature is within the normal range, sanitization is done,and the person is permitted entry through an IOT enabled smart door. However, if the system fails to detect a mask or the person’s temperature falls out of the predefined range, a buzzer rings and the door remains closed. Our model is intended to be effective in preventing the spread of this infectious disease.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89453451","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760570
Banhi Sanyal, Anshuman Padhy, R. Mohapatra, Ratnakar Dash
Traffic sign recognition (TSR) is an important aspect of Intelligent transport systems (ITS). The lack of architectures that are flexible across multiple datasets are rarely there. As such this work attempts to use an efficient Deep neural network (DNN) architecture and implement it on an Indian traffic sign dataset IRSDBv1.0. IRSDBv1.0 is the first publicly available Indian datset, to the best our knowledge. The performance of MDEffNet is studied and analyzed.
{"title":"Real-time Indian TSR using MDEffNet","authors":"Banhi Sanyal, Anshuman Padhy, R. Mohapatra, Ratnakar Dash","doi":"10.1109/AISP53593.2022.9760570","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760570","url":null,"abstract":"Traffic sign recognition (TSR) is an important aspect of Intelligent transport systems (ITS). The lack of architectures that are flexible across multiple datasets are rarely there. As such this work attempts to use an efficient Deep neural network (DNN) architecture and implement it on an Indian traffic sign dataset IRSDBv1.0. IRSDBv1.0 is the first publicly available Indian datset, to the best our knowledge. The performance of MDEffNet is studied and analyzed.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80233781","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 : 2022-02-12DOI: 10.1109/AISP53593.2022.9760593
M. Rahul Rajesh, B. P. Kumar, Ranjeet Kumar, Samineni Peddakrishna, F. J. Dian
The rise of wearable devices being used into our daily life have been observed the disputes, when it is utilized by the clients for durable is quiet an issue with growth of Internet of Things (IOT). In this paper we reviewed the dissipates in ethical, social and environmental associated to wearable technologies from client point of views. Research has been pointed numerous issues in the ecological, social and ethical values are jointly investigated in this research work, shown in the area of wearable Internet of Things. This study mainly focus on dispute of IOT, which are found to be important in wearable technologies. The disputes which have been mentioned are significant effects for reducing the negative adaptation sprints of wearables in our daily basis.
{"title":"Study on Recent disputed of Internet of Things (IoT) in Wearable Technologies","authors":"M. Rahul Rajesh, B. P. Kumar, Ranjeet Kumar, Samineni Peddakrishna, F. J. Dian","doi":"10.1109/AISP53593.2022.9760593","DOIUrl":"https://doi.org/10.1109/AISP53593.2022.9760593","url":null,"abstract":"The rise of wearable devices being used into our daily life have been observed the disputes, when it is utilized by the clients for durable is quiet an issue with growth of Internet of Things (IOT). In this paper we reviewed the dissipates in ethical, social and environmental associated to wearable technologies from client point of views. Research has been pointed numerous issues in the ecological, social and ethical values are jointly investigated in this research work, shown in the area of wearable Internet of Things. This study mainly focus on dispute of IOT, which are found to be important in wearable technologies. The disputes which have been mentioned are significant effects for reducing the negative adaptation sprints of wearables in our daily basis.","PeriodicalId":6793,"journal":{"name":"2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80585448","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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