Pub Date : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200446
D. Kumar, Y. Kumar, V. Kukreja, Ankit Bansal, Abhishek Bhattacherjee
A worldwide industry that provides food, business, and employment opportunities, agriculture is a key component of human life. Despite this, wheat is one of the most common armed crops and the production rate harms wheat yield every year. In this paper, a prediction method for wheat yield has been calculated with different environmental impact assessment parameters. Predictors of data are a predictive approach that helps to categorize the data based on the different grouping patterns. Exploratory data analysis (EDA) and Linear discriminant analysis (LDA) are very effective approaches for grouping the data. The main aim of this paper is to predict the wheat yield prediction through EDA, decision tree, random forest regressor, ensemble learning, and LDA to maximize accuracy. Different environmental impacts parameters such as average rainfall, average temperature, and pesticides have been used to predict the wheat yield. Also, ensemble learning has been used for the prediction and analysis of the model through the decision tree and random forest regressor. Moreover, the LDA has been used to classify the wheat yield dataset by applying a reduction approach of LDA. During wheat yield prediction, the decision tree achieves 0.025 losses in training time. Also, the performance of LDA and EDA has been calculated through squared error functions. During wheat yield prediction through EDA with environmental impact parameters, the Root means squared error (RMSE) is 18245.27 while the value of Mean absolute error (MAE) is 12334.75. Furthermore, the work of LDA has presented by supporting the data visualization through different graphs using pandas and Matplotlib library. This study provides the data reduction predictors approach to the wheat yield and explains the data-preprocessing technique used along with EDA and LDA for wheat yield prediction in different environmental impact parameters.
{"title":"High Performance EDA and LDA Analysis: An Application for Wheat Yield Estimation","authors":"D. Kumar, Y. Kumar, V. Kukreja, Ankit Bansal, Abhishek Bhattacherjee","doi":"10.1109/ACCESS57397.2023.10200446","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200446","url":null,"abstract":"A worldwide industry that provides food, business, and employment opportunities, agriculture is a key component of human life. Despite this, wheat is one of the most common armed crops and the production rate harms wheat yield every year. In this paper, a prediction method for wheat yield has been calculated with different environmental impact assessment parameters. Predictors of data are a predictive approach that helps to categorize the data based on the different grouping patterns. Exploratory data analysis (EDA) and Linear discriminant analysis (LDA) are very effective approaches for grouping the data. The main aim of this paper is to predict the wheat yield prediction through EDA, decision tree, random forest regressor, ensemble learning, and LDA to maximize accuracy. Different environmental impacts parameters such as average rainfall, average temperature, and pesticides have been used to predict the wheat yield. Also, ensemble learning has been used for the prediction and analysis of the model through the decision tree and random forest regressor. Moreover, the LDA has been used to classify the wheat yield dataset by applying a reduction approach of LDA. During wheat yield prediction, the decision tree achieves 0.025 losses in training time. Also, the performance of LDA and EDA has been calculated through squared error functions. During wheat yield prediction through EDA with environmental impact parameters, the Root means squared error (RMSE) is 18245.27 while the value of Mean absolute error (MAE) is 12334.75. Furthermore, the work of LDA has presented by supporting the data visualization through different graphs using pandas and Matplotlib library. This study provides the data reduction predictors approach to the wheat yield and explains the data-preprocessing technique used along with EDA and LDA for wheat yield prediction in different environmental impact parameters.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130832066","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10201037
Diwaker, Kriti, Jyoti Rawat
Over past few decades Breast cancer (BC) has become more common and affecting females in early age, which is an alarming and challenging situation for researchers to provide methods to identify the disease in their early stage. This is the deadliest cancer among women and is alarming female fraternity becoming second leading cause of deaths. If the disease gets identified in their early stage it may leads to reduction in mortality rate. It may occur in cells that produce milk (lobules) or in the passages responsible for carrying milk (milk ducts). This paper presents the performance comparison of various pre-processing techniques based on the BreakHis dataset. The dataset used contains 1980 breast histopathological images including 625 benign and 1355 malignant cases. Initially the histopathological images have been pre-processed using techniques including contrast limited adaptive histogram equalization (CLAHE), contrast stretching (CS), histogram equalization (HE), and unsharp masking (UM) followed by feature extraction using 2D Gabor Wavelet Transform to obtain texture feature from both the categories like original and preprocessed images. Finally, support vector machine (SVM) classifies the images in two categories namely benign and malignant. The experiments results show that texture features computed using UM as pre-processing tool outperformed for making difference between benign and malignant images using breast histopathological images with a classification accuracy of 84.1 %.
{"title":"Assessing the Effect of Pre-processing Techniques on Classification of Breast Cancer using Histopathological Images","authors":"Diwaker, Kriti, Jyoti Rawat","doi":"10.1109/ACCESS57397.2023.10201037","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10201037","url":null,"abstract":"Over past few decades Breast cancer (BC) has become more common and affecting females in early age, which is an alarming and challenging situation for researchers to provide methods to identify the disease in their early stage. This is the deadliest cancer among women and is alarming female fraternity becoming second leading cause of deaths. If the disease gets identified in their early stage it may leads to reduction in mortality rate. It may occur in cells that produce milk (lobules) or in the passages responsible for carrying milk (milk ducts). This paper presents the performance comparison of various pre-processing techniques based on the BreakHis dataset. The dataset used contains 1980 breast histopathological images including 625 benign and 1355 malignant cases. Initially the histopathological images have been pre-processed using techniques including contrast limited adaptive histogram equalization (CLAHE), contrast stretching (CS), histogram equalization (HE), and unsharp masking (UM) followed by feature extraction using 2D Gabor Wavelet Transform to obtain texture feature from both the categories like original and preprocessed images. Finally, support vector machine (SVM) classifies the images in two categories namely benign and malignant. The experiments results show that texture features computed using UM as pre-processing tool outperformed for making difference between benign and malignant images using breast histopathological images with a classification accuracy of 84.1 %.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126764603","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10201006
S. Natarajan, V. Govindaraj, Pallikonda Rajasekaran Murugan, Yudong Zhang, Arunprasath Thiyagarajan, Kiruthika Uma
Many medical image-based diagnostics, particularly the diagnosis of brain tumours in Magnetic Resonance Imaging (MRI), heavily rely on multi-region segmentation. This work's major objective is to improve the multi-region detection performance by combining a modified Fuzzy C-Means (FCM) with a self-accommodative JAYA (SAJAYA) algorithm. Due to its capacity to choose the number of cluster heads in the FCM stage and population suitability in the optimization stage, this technique is more successful and considerably facilitates the precise MR brain image segmentation. To achieve the best performance, SAJAYA is employed to optimize segmentation variables and reduce the overall computation time and complexity. The proposed algorithm segments the different informative sections, such as cerebrospinal fluid, grey matter, and white matter, which will be most helpful to investigate and characterize the tumour. The experiment's findings show that the suggested algorithm is successful in terms of sensitivity, specificity, accuracy and other benchmark metrics.
{"title":"Tumour region detection in MR brain images using MFCM based segmentation and Self Accommodative JAYA based optimization","authors":"S. Natarajan, V. Govindaraj, Pallikonda Rajasekaran Murugan, Yudong Zhang, Arunprasath Thiyagarajan, Kiruthika Uma","doi":"10.1109/ACCESS57397.2023.10201006","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10201006","url":null,"abstract":"Many medical image-based diagnostics, particularly the diagnosis of brain tumours in Magnetic Resonance Imaging (MRI), heavily rely on multi-region segmentation. This work's major objective is to improve the multi-region detection performance by combining a modified Fuzzy C-Means (FCM) with a self-accommodative JAYA (SAJAYA) algorithm. Due to its capacity to choose the number of cluster heads in the FCM stage and population suitability in the optimization stage, this technique is more successful and considerably facilitates the precise MR brain image segmentation. To achieve the best performance, SAJAYA is employed to optimize segmentation variables and reduce the overall computation time and complexity. The proposed algorithm segments the different informative sections, such as cerebrospinal fluid, grey matter, and white matter, which will be most helpful to investigate and characterize the tumour. The experiment's findings show that the suggested algorithm is successful in terms of sensitivity, specificity, accuracy and other benchmark metrics.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114425525","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10201167
K. Prasad, V. Chandratre, M. Sukhwani, R. Shinde
this paper describes the aspects of the multi-channel, compact data acquisition systems (DAQs), developed to characterize the front-end electronics (FEE) of the resistive plate chamber detectors (RPCs) being used in India-based neutrino observatory (INO) experiment. The number of RPCs and FEE boards required in the experiment are 29000 and 464000 respectively. It is required to characterize the FEEs along with RPC detectors before the actual deployment in the experiment. The detector parameters that are to be measured for this characterization are strip (noise) rate, muon detection efficiency, and time resolution. Usually, commercial, rack mount level translators, scalars and time-to-digital converters (TDC) are being used to measure these parameters. In the DAQ system presented here, all these functionalities have been integrated in a single compact module thereby resulting in a low cost, multichannel, and compact DAQ system. This paper describes in detail the developmental aspects of 128-channel DAQ system built using Xilinx Spartan-6 FPGAs, ARM Cortex-M4 microcontroller and in-house developed ASIC and FPGA based TDCs. The DAQ system has Ethernet interface and USB interface for data transfer. The system is supported by detailed data analysis software to monitor and measure the parameters in real time. The DAQ is tested with different configurations of RPCs and FEEs. The strip rates of the order of 30 - 200 counts per seconds (CPS), detector efficiency of greater than 90% and timing resolution of 2.4 ns to 2.7 ns are measured using these DAQs.
{"title":"Multi-channel, compact DAQ system with inbuilt Time-to-digital converters","authors":"K. Prasad, V. Chandratre, M. Sukhwani, R. Shinde","doi":"10.1109/ACCESS57397.2023.10201167","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10201167","url":null,"abstract":"this paper describes the aspects of the multi-channel, compact data acquisition systems (DAQs), developed to characterize the front-end electronics (FEE) of the resistive plate chamber detectors (RPCs) being used in India-based neutrino observatory (INO) experiment. The number of RPCs and FEE boards required in the experiment are 29000 and 464000 respectively. It is required to characterize the FEEs along with RPC detectors before the actual deployment in the experiment. The detector parameters that are to be measured for this characterization are strip (noise) rate, muon detection efficiency, and time resolution. Usually, commercial, rack mount level translators, scalars and time-to-digital converters (TDC) are being used to measure these parameters. In the DAQ system presented here, all these functionalities have been integrated in a single compact module thereby resulting in a low cost, multichannel, and compact DAQ system. This paper describes in detail the developmental aspects of 128-channel DAQ system built using Xilinx Spartan-6 FPGAs, ARM Cortex-M4 microcontroller and in-house developed ASIC and FPGA based TDCs. The DAQ system has Ethernet interface and USB interface for data transfer. The system is supported by detailed data analysis software to monitor and measure the parameters in real time. The DAQ is tested with different configurations of RPCs and FEEs. The strip rates of the order of 30 - 200 counts per seconds (CPS), detector efficiency of greater than 90% and timing resolution of 2.4 ns to 2.7 ns are measured using these DAQs.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121391969","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10199720
Jowa Yangchin, N. Marchang
Mobile crowd sensing is a technique that allows collection of real-time data from a large number of mobile users who carry a mobile device with sensing capabilities. It is widely used for data sensing applications, such as traffic monitoring, environmental monitoring, health and fitness, retail marketing, and emergency response. It requires individual users to perform the sensing task based on the location of the task and the user. Ensuring privacy and security of individuals and accuracy and reliability of the data collected are primary challenges in a mobile crowd sensing system. To motivate more users to collect data, it is required for the system to be built in a manner that each user is rewarded for the task done while maintaining the budget balance. As users are of heterogeneous nature, they must be rewarded for the task done based on their own true valuation of the task. The reverse auction method for mobile crowdsensing is becoming one of the widely used incentive mechanism for its choice to the mobile users, who act as the participating workers, for fixing the price for which they want to sell the sensed data. For a reverse auction system to work, it is required that there are enough users who are willing to bid in an auction round. Maintaining a participant pool with enough competition while keeping the bid values near to true values is a key challenge to be addressed. Failing to maintain enough participants can result in higher bid prices with each round and hence increasing total reward value to be distributed. This may lead to incentive explosion where the bid price is too high for the available budget. In this work, we propose a novel approach of retaining users by considering the frequency of winning and participation of users. This mechanism is built on top of RADP-VPC which is reverse auction mechanism based on reverse- auction with dynamic pricing with virtual participation credit. The experimental results show that the proposed approach using participation history for each user performs better than RADP-VPC in terms of retaining users and incentive explosion.
{"title":"Incentive Mechanism for Mobile Crowd Sensing Using Reverse Auction Dynamic Pricing and Recent History","authors":"Jowa Yangchin, N. Marchang","doi":"10.1109/ACCESS57397.2023.10199720","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199720","url":null,"abstract":"Mobile crowd sensing is a technique that allows collection of real-time data from a large number of mobile users who carry a mobile device with sensing capabilities. It is widely used for data sensing applications, such as traffic monitoring, environmental monitoring, health and fitness, retail marketing, and emergency response. It requires individual users to perform the sensing task based on the location of the task and the user. Ensuring privacy and security of individuals and accuracy and reliability of the data collected are primary challenges in a mobile crowd sensing system. To motivate more users to collect data, it is required for the system to be built in a manner that each user is rewarded for the task done while maintaining the budget balance. As users are of heterogeneous nature, they must be rewarded for the task done based on their own true valuation of the task. The reverse auction method for mobile crowdsensing is becoming one of the widely used incentive mechanism for its choice to the mobile users, who act as the participating workers, for fixing the price for which they want to sell the sensed data. For a reverse auction system to work, it is required that there are enough users who are willing to bid in an auction round. Maintaining a participant pool with enough competition while keeping the bid values near to true values is a key challenge to be addressed. Failing to maintain enough participants can result in higher bid prices with each round and hence increasing total reward value to be distributed. This may lead to incentive explosion where the bid price is too high for the available budget. In this work, we propose a novel approach of retaining users by considering the frequency of winning and participation of users. This mechanism is built on top of RADP-VPC which is reverse auction mechanism based on reverse- auction with dynamic pricing with virtual participation credit. The experimental results show that the proposed approach using participation history for each user performs better than RADP-VPC in terms of retaining users and incentive explosion.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124665850","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10201106
Asha J. Vithayathil, A. Sreekumar
In recent years, chaos based cryptography has become a prevalent and efficient way to secure digital images because of the similarities between chaotic properties and the traits needed for encryption. This paper proposes an image encryption algorithm combining a chaotic map, Josephus problem, cyclic shift operation, and XOR operation. The proposed encryption procedure follows the traditional permutation-substitution or confusion-diffusion structure. Here, the Henon map and the novel key generation phase generate image-sensitive chaotic streams, which are used for permutation and diffusion. The aperiodic Josephus permutation and bit-level chaotic cycle shift method accomplish the permutation stage by altering the position and value of each pixel. And this proposed permutation thwarts statistical cryptanalysis by dropping the correlation between neighboring pixels to approximately equal zero. The substitution stage is achieved with modulus and XOR operations on the scrambled image and the chaotic matrix. We compare the proposed method with other recent image encryption algorithms in the simulation experiment and security analysis, and the results confirm that the proposed method has better performance and higher security.
{"title":"Image Encryption Through Aperiodic Josephus Permutation And Novel Cyclic Shift Operation","authors":"Asha J. Vithayathil, A. Sreekumar","doi":"10.1109/ACCESS57397.2023.10201106","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10201106","url":null,"abstract":"In recent years, chaos based cryptography has become a prevalent and efficient way to secure digital images because of the similarities between chaotic properties and the traits needed for encryption. This paper proposes an image encryption algorithm combining a chaotic map, Josephus problem, cyclic shift operation, and XOR operation. The proposed encryption procedure follows the traditional permutation-substitution or confusion-diffusion structure. Here, the Henon map and the novel key generation phase generate image-sensitive chaotic streams, which are used for permutation and diffusion. The aperiodic Josephus permutation and bit-level chaotic cycle shift method accomplish the permutation stage by altering the position and value of each pixel. And this proposed permutation thwarts statistical cryptanalysis by dropping the correlation between neighboring pixels to approximately equal zero. The substitution stage is achieved with modulus and XOR operations on the scrambled image and the chaotic matrix. We compare the proposed method with other recent image encryption algorithms in the simulation experiment and security analysis, and the results confirm that the proposed method has better performance and higher security.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129827731","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200895
Biya Kurian, Jerom Regi, Dennis John, Hari P, Therese Yamuna Mahesh
In recent years, there has been an increase in the use of IoT devices for home automation, shopping malls, and other public places. However, for individuals who are mute or bedridden, accessing these devices can be difficult, especially when they are voice-activated. To address this issue, hand gesture recognition technology has been developed to allow individuals to control these devices through simple hand movements. Image processing and pattern recognition are crucial for accurately detecting these hand gestures, and platforms such as Open CV, Python, PyCharm, and Media Pipe are commonly used in software development to achieve this. This technology has the potential to help people with physical, sensory, or intellectual disabilities to participate fully in all activities in society and enjoy equal opportunities. By using hand gestures to communicate with IoT devices, individuals who are deaf can also benefit from this technology. Ultimately, this technology has the potential to create a human-computer interaction that is accessible to all, making it a valuable addition to the field of assistive technology. Furthermore, hand gesture recognition technology is an excellent example of the potential of IoT devices to facilitate a more connected and automated world. However, it is important to note that with any new technology, there are also concerns around data privacy and security. As such, it is essential that developers prioritize ethical considerations and robust security protocols when designing these systems. Moreover, hand gesture recognition technology can be further improved through the use of artificial intelligence and machine learning. These technologies can help improve the accuracy of the recognition system and provide a more personalized experience for users. This system is highly reliable and user-friendly, and does not require any physical contact, which makes it highly suitable for disabled people. Furthermore, the development of new sensor technologies can also help increase the reliability and efficiency of the hand gesture recognition system. Overall, the development of hand gesture recognition technology is an exciting and innovative area of research that has the potential to improve the lives of many individuals, particularly those with physical or sensory disabilities. With continued advancements in technology, it can expect to see more sophisticated and accessible hand gesture recognition systems that will help create a more inclusive and accessible society.
{"title":"Visual Gesture-Based Home Automation","authors":"Biya Kurian, Jerom Regi, Dennis John, Hari P, Therese Yamuna Mahesh","doi":"10.1109/ACCESS57397.2023.10200895","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200895","url":null,"abstract":"In recent years, there has been an increase in the use of IoT devices for home automation, shopping malls, and other public places. However, for individuals who are mute or bedridden, accessing these devices can be difficult, especially when they are voice-activated. To address this issue, hand gesture recognition technology has been developed to allow individuals to control these devices through simple hand movements. Image processing and pattern recognition are crucial for accurately detecting these hand gestures, and platforms such as Open CV, Python, PyCharm, and Media Pipe are commonly used in software development to achieve this. This technology has the potential to help people with physical, sensory, or intellectual disabilities to participate fully in all activities in society and enjoy equal opportunities. By using hand gestures to communicate with IoT devices, individuals who are deaf can also benefit from this technology. Ultimately, this technology has the potential to create a human-computer interaction that is accessible to all, making it a valuable addition to the field of assistive technology. Furthermore, hand gesture recognition technology is an excellent example of the potential of IoT devices to facilitate a more connected and automated world. However, it is important to note that with any new technology, there are also concerns around data privacy and security. As such, it is essential that developers prioritize ethical considerations and robust security protocols when designing these systems. Moreover, hand gesture recognition technology can be further improved through the use of artificial intelligence and machine learning. These technologies can help improve the accuracy of the recognition system and provide a more personalized experience for users. This system is highly reliable and user-friendly, and does not require any physical contact, which makes it highly suitable for disabled people. Furthermore, the development of new sensor technologies can also help increase the reliability and efficiency of the hand gesture recognition system. Overall, the development of hand gesture recognition technology is an exciting and innovative area of research that has the potential to improve the lives of many individuals, particularly those with physical or sensory disabilities. With continued advancements in technology, it can expect to see more sophisticated and accessible hand gesture recognition systems that will help create a more inclusive and accessible society.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130034452","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10199858
J. Sravanthi, B. V. Subbayamma, Waseem Sultana, Sriram Parabrahmachari, V. G. Krishnan, Yadavalli S S Sriramam
The optimization of energy conservation and the span of the system are the important obstacles to establishing and managing the function of wireless sensor networks. Grouping is an efficient strategy for modifying the load, synchronizing the structure with the associated order, and lengthening the system's life. In a cluster-based system, hot spots arise because the group leader that is closer the sink soon runs out of energy. To resolve this challenge, numerous uneven grouping strategies have been suggested. These strategies have the limitation of overburdening the group leader with endpoints that enter the identical cluster. Hence, in order to strengthen the functionality of a group, we provide a strategy in this research called fuzzy logic - based imbalanced grouping. Generated statistics is used to examine the intended study. The recommended strategy is contrasted with two existing heuristics: LEACH, which employs an analogous grouping strategy, and EAUCF, which employs an asymmetrical grouping strategy. The MATLAB simulation findings illustrate that the recommended strategy performs superior than the other two strategies.
{"title":"Enhancement of Energy Efficiency using Improved Energy Efficient Routing Protocol in Wireless Sensor Networks for IoT Applications","authors":"J. Sravanthi, B. V. Subbayamma, Waseem Sultana, Sriram Parabrahmachari, V. G. Krishnan, Yadavalli S S Sriramam","doi":"10.1109/ACCESS57397.2023.10199858","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199858","url":null,"abstract":"The optimization of energy conservation and the span of the system are the important obstacles to establishing and managing the function of wireless sensor networks. Grouping is an efficient strategy for modifying the load, synchronizing the structure with the associated order, and lengthening the system's life. In a cluster-based system, hot spots arise because the group leader that is closer the sink soon runs out of energy. To resolve this challenge, numerous uneven grouping strategies have been suggested. These strategies have the limitation of overburdening the group leader with endpoints that enter the identical cluster. Hence, in order to strengthen the functionality of a group, we provide a strategy in this research called fuzzy logic - based imbalanced grouping. Generated statistics is used to examine the intended study. The recommended strategy is contrasted with two existing heuristics: LEACH, which employs an analogous grouping strategy, and EAUCF, which employs an asymmetrical grouping strategy. The MATLAB simulation findings illustrate that the recommended strategy performs superior than the other two strategies.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123078096","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10199210
Vinay Venkatesh
An estimated 19,223 people lost their lives due to gun violence in 2020. ArmVision prevents arm related crimes before they occur and brings quicker attention to them if they do with its efficient notification system. Its wide range of applicability expresses a bright future for its development. ArmVision uses a complex machine learning (ML) algorithm called You Only Look Once (YOLO) and Convolutional Neural Networks (CNNs) to optimize its performance.
据估计，2020年有19223人因枪支暴力而丧生。ArmVision可以在武器犯罪发生之前进行预防，如果使用其高效的通知系统，可以更快地引起人们的注意。其广泛的适用性表明其发展前景广阔。ArmVision使用一种名为You Only Look Once (YOLO)的复杂机器学习(ML)算法和卷积神经网络(cnn)来优化其性能。
{"title":"ArmVision: An Approach to Improve Police Officers Response Times to Gun Violence and Put a Stop to Armed Crimes Before They Occur","authors":"Vinay Venkatesh","doi":"10.1109/ACCESS57397.2023.10199210","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10199210","url":null,"abstract":"An estimated 19,223 people lost their lives due to gun violence in 2020. ArmVision prevents arm related crimes before they occur and brings quicker attention to them if they do with its efficient notification system. Its wide range of applicability expresses a bright future for its development. ArmVision uses a complex machine learning (ML) algorithm called You Only Look Once (YOLO) and Convolutional Neural Networks (CNNs) to optimize its performance.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121833261","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 : 2023-05-18DOI: 10.1109/ACCESS57397.2023.10200199
Subha Thomas, R. Sudarmani
The leading cause of cancer related mortality in both kids and adults is brain tumor. Through the examination of the abnormalities of the tumor's tissues and cells, a tumor may be divided into many stages. The acute risk of tumor growth and spread is provided by this stage. Biopsy can be used to assess the tumor grade. It should be mentioned that tumor grading differs from cancer stage classification. Due to their high complexity and wide variation, brain tumor types are particularly difficult to diagnose with great accuracy. This survey starts with the review of 25 papers on brain tumor classification. The varied models used in the papers are analyzed, which includes methods for segmentation, classification and optimization. The analysis on varied metrics is analyzed and their maximal performances are also examined. Finally, chronological review is performed along with existing challenges.
{"title":"Brain Tumor Classification using MRI Image -A Survey","authors":"Subha Thomas, R. Sudarmani","doi":"10.1109/ACCESS57397.2023.10200199","DOIUrl":"https://doi.org/10.1109/ACCESS57397.2023.10200199","url":null,"abstract":"The leading cause of cancer related mortality in both kids and adults is brain tumor. Through the examination of the abnormalities of the tumor's tissues and cells, a tumor may be divided into many stages. The acute risk of tumor growth and spread is provided by this stage. Biopsy can be used to assess the tumor grade. It should be mentioned that tumor grading differs from cancer stage classification. Due to their high complexity and wide variation, brain tumor types are particularly difficult to diagnose with great accuracy. This survey starts with the review of 25 papers on brain tumor classification. The varied models used in the papers are analyzed, which includes methods for segmentation, classification and optimization. The analysis on varied metrics is analyzed and their maximal performances are also examined. Finally, chronological review is performed along with existing challenges.","PeriodicalId":345351,"journal":{"name":"2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS)","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132758375","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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