Pub Date : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169588
Vrutik Patel, P. Mehta, Kruti Lavingia
Every software contains numerous processes for building a program, and each step is significant for software requirements. As the globe expands and develops quickly, so does the demand for software. Categorization of requirements can be done manually however doing so requires a lot of human effort, time, money, and risk of inaccurate results. As a result, numerous earlier studies have suggested automating the classification process but consumes lot of time. Here several ways are introduced such that this time taking process can be automated and software requirements can be classified using several machine learning algorithms into various categories. In the process of achieving this there were several algorithms that were taken into consideration which includes KNN, SVM, DT, Naïve Bayes to train dataset and their evaluation metrics were studied.
{"title":"Software Requirement Classification Using Machine Learning Algorithms","authors":"Vrutik Patel, P. Mehta, Kruti Lavingia","doi":"10.1109/ICAIA57370.2023.10169588","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169588","url":null,"abstract":"Every software contains numerous processes for building a program, and each step is significant for software requirements. As the globe expands and develops quickly, so does the demand for software. Categorization of requirements can be done manually however doing so requires a lot of human effort, time, money, and risk of inaccurate results. As a result, numerous earlier studies have suggested automating the classification process but consumes lot of time. Here several ways are introduced such that this time taking process can be automated and software requirements can be classified using several machine learning algorithms into various categories. In the process of achieving this there were several algorithms that were taken into consideration which includes KNN, SVM, DT, Naïve Bayes to train dataset and their evaluation metrics were studied.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114768816","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169644
H. Bedi, Shakti Raj Chopra
The two most critical challenges in developing network protocols for utilizing real-time applications are high throughput and low delay. WLANs enable broadband multimedia communication, thus fulfilling QoS requirements such as these might be problematic. For packet transmission, IEEE 802.11 uses the (DCF) protocol and the BEB algorithm. In WLANs, the packet transmission technique has a major impact on the performance parameters like throughput and delay. So far, various Markov models have been made to check and improve the quality. However, recent models fail with significant packet collisions that decrease throughput and delay, crowded surroundings, and they are still unable to predict the network performance accurately This paper uses, a protocol and algorithm to reduce collisions and prevent the channel capture effect. The performance of the modified protocol, known as the CA-DCF (CAD) protocol, is assessed in terms of throughput and delay.
{"title":"Design and Development of a System for the Improvement of Network Efficiency in IEEE 802.11E","authors":"H. Bedi, Shakti Raj Chopra","doi":"10.1109/ICAIA57370.2023.10169644","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169644","url":null,"abstract":"The two most critical challenges in developing network protocols for utilizing real-time applications are high throughput and low delay. WLANs enable broadband multimedia communication, thus fulfilling QoS requirements such as these might be problematic. For packet transmission, IEEE 802.11 uses the (DCF) protocol and the BEB algorithm. In WLANs, the packet transmission technique has a major impact on the performance parameters like throughput and delay. So far, various Markov models have been made to check and improve the quality. However, recent models fail with significant packet collisions that decrease throughput and delay, crowded surroundings, and they are still unable to predict the network performance accurately This paper uses, a protocol and algorithm to reduce collisions and prevent the channel capture effect. The performance of the modified protocol, known as the CA-DCF (CAD) protocol, is assessed in terms of throughput and delay.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123196378","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169796
B. Rajanarayan Prusty, S. Mohan Krishna, Kishore Bingi, Neeraj Gupta
Risk-based reliability assessment is prevalent for modern power systems under higher penetration of renewable generations. This paper highlights the importance of machine learning and probabilistic approaches for risk-based reliability assessment during power system operation and planning. A set of metrics for realistic risk-based reliability assessment considering over-limit probabilities and corresponding severities is suggested. Probabilistic load flow using Monte-Carlo simulation is used to estimate the over-limit probabilities of power system variables. A detailed presentation of steps for the generation of random samples of a set of correlated random variables, development of realistic risk metrics, and portrayal of their significances via critical result analyses for different cases is expected to serve as a reference text for novice researchers in the field of risk-based reliability assessment of modern power systems integrated with photovoltaic generations.
{"title":"Risk-Based Reliability Assessment of Modern Power Systems using Machine Learning and Probability Theory","authors":"B. Rajanarayan Prusty, S. Mohan Krishna, Kishore Bingi, Neeraj Gupta","doi":"10.1109/ICAIA57370.2023.10169796","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169796","url":null,"abstract":"Risk-based reliability assessment is prevalent for modern power systems under higher penetration of renewable generations. This paper highlights the importance of machine learning and probabilistic approaches for risk-based reliability assessment during power system operation and planning. A set of metrics for realistic risk-based reliability assessment considering over-limit probabilities and corresponding severities is suggested. Probabilistic load flow using Monte-Carlo simulation is used to estimate the over-limit probabilities of power system variables. A detailed presentation of steps for the generation of random samples of a set of correlated random variables, development of realistic risk metrics, and portrayal of their significances via critical result analyses for different cases is expected to serve as a reference text for novice researchers in the field of risk-based reliability assessment of modern power systems integrated with photovoltaic generations.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125777393","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169568
K. Lalli, V. K. Shrivastava, R. Shekhar
The digital images can easily be manipulated using Software tool or mobile application these days. Dispersal of forgery images in social media is one of the prime threats and it has a prodigious impact. Most shared tampered images are based on duplicating some part of the image (copy move image forgery) and merging some portion of two different images (image splicing). Hence, trust in a digital image on social media is becoming extremely hard nowadays. The researchers are highly active in finding a solution for this challenge and there are several papers proposed with different approaches to solve this issue. Most of the suggestions revolve around deep learning models that are efficient and suitable to detect copy move images. This paper focusses on reviewing various Deep Convolution Neural Network (DCNN) approaches and hybrid Deep learning models in copy move image detection by comparative analysis of the experimental outcome of the different models presented for this issue. This research article compares various articles relating to our issue by means of a model, a dataset, and the characteristics of those articles.
{"title":"Detecting Copy Move Image Forgery using a Deep Learning Model: A Review","authors":"K. Lalli, V. K. Shrivastava, R. Shekhar","doi":"10.1109/ICAIA57370.2023.10169568","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169568","url":null,"abstract":"The digital images can easily be manipulated using Software tool or mobile application these days. Dispersal of forgery images in social media is one of the prime threats and it has a prodigious impact. Most shared tampered images are based on duplicating some part of the image (copy move image forgery) and merging some portion of two different images (image splicing). Hence, trust in a digital image on social media is becoming extremely hard nowadays. The researchers are highly active in finding a solution for this challenge and there are several papers proposed with different approaches to solve this issue. Most of the suggestions revolve around deep learning models that are efficient and suitable to detect copy move images. This paper focusses on reviewing various Deep Convolution Neural Network (DCNN) approaches and hybrid Deep learning models in copy move image detection by comparative analysis of the experimental outcome of the different models presented for this issue. This research article compares various articles relating to our issue by means of a model, a dataset, and the characteristics of those articles.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129787995","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169451
Vatsala Singh, G. Singh
The world is surrounded by enormous amount of data almost generated at an unpredictable velocity, in huge volume, variety and veracity. Our traditional systems, however have not yet reached a state where they can efficiently use every bit of this Big data and incorporate it to derive consistent information. Data Fusion is one such technique which can help us achieve this goal. It can be applied to various fields, however for the scope of this paper we have focused on its implementation in Precision Agriculture. While remote sensing has played a major role in precision agriculture by harnessing satellite data as a non-destructive way of information retrieval. The data collected from these satellite varies widely depending on the technique, spatial resolution, temporal resolution, spectral range, viewing geometry of the sensors, thus providing us different amounts of information for different use cases, some in which makes it quiet challenging for the researchers to harness all the information available to attain higher levels of precision, as high as to be able to classify at a sub pixel level while retaining the efficiency and feasibility of the solution. one of the major pain point in agriculture is monitoring large fields and gauging their crop density per square meter. While crop density of a field depends hugely of the soil quality, moisture, fertilizer percentage knowing crop density can have a great impact on yield prediction, sustainable fertilization and overall better through put of a field. Thus, in this paper we have explored the possibility of fusing data from different sensors using CNN based Data Fusion Algorithm to retrieve crop density and segregate patches of field as sparse or dense respectively. The results are quite encouraging.
{"title":"Development of AI Enabled Solution for Efficient Feature Enrichment from Multiple Data Sources: An Application in Precision Agriculture","authors":"Vatsala Singh, G. Singh","doi":"10.1109/ICAIA57370.2023.10169451","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169451","url":null,"abstract":"The world is surrounded by enormous amount of data almost generated at an unpredictable velocity, in huge volume, variety and veracity. Our traditional systems, however have not yet reached a state where they can efficiently use every bit of this Big data and incorporate it to derive consistent information. Data Fusion is one such technique which can help us achieve this goal. It can be applied to various fields, however for the scope of this paper we have focused on its implementation in Precision Agriculture. While remote sensing has played a major role in precision agriculture by harnessing satellite data as a non-destructive way of information retrieval. The data collected from these satellite varies widely depending on the technique, spatial resolution, temporal resolution, spectral range, viewing geometry of the sensors, thus providing us different amounts of information for different use cases, some in which makes it quiet challenging for the researchers to harness all the information available to attain higher levels of precision, as high as to be able to classify at a sub pixel level while retaining the efficiency and feasibility of the solution. one of the major pain point in agriculture is monitoring large fields and gauging their crop density per square meter. While crop density of a field depends hugely of the soil quality, moisture, fertilizer percentage knowing crop density can have a great impact on yield prediction, sustainable fertilization and overall better through put of a field. Thus, in this paper we have explored the possibility of fusing data from different sensors using CNN based Data Fusion Algorithm to retrieve crop density and segregate patches of field as sparse or dense respectively. The results are quite encouraging.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125852445","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169756
Ishika Giroti, J. Das, N. Harshith, Gousia Thahniyath
Diabetic retinopathy is an eye disease that progressively degrades a person’s vision. It affects 40-45% of people with diabetes and it is one of society’s leading causes of blindness. Diabetic retinopathy has 4 different phases and with each stage, the eyesight of a person degrades. If diabetic retinopathy is detected in its early phases its effects and progression can be slowed down and save the person from permanent blindness. We aim to utilize ML to detect the disease and classify diabetic retinopathy into its 4 classes based on severity, helping in early detection. This paper gives a glimpse into diabetic retinopathy and proposes a methodology to develop a machine-learning model along with deploying the developed model on an AWS based web-app.
{"title":"Diabetic Retinopathy Detection & Classification using Efficient Net Model","authors":"Ishika Giroti, J. Das, N. Harshith, Gousia Thahniyath","doi":"10.1109/ICAIA57370.2023.10169756","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169756","url":null,"abstract":"Diabetic retinopathy is an eye disease that progressively degrades a person’s vision. It affects 40-45% of people with diabetes and it is one of society’s leading causes of blindness. Diabetic retinopathy has 4 different phases and with each stage, the eyesight of a person degrades. If diabetic retinopathy is detected in its early phases its effects and progression can be slowed down and save the person from permanent blindness. We aim to utilize ML to detect the disease and classify diabetic retinopathy into its 4 classes based on severity, helping in early detection. This paper gives a glimpse into diabetic retinopathy and proposes a methodology to develop a machine-learning model along with deploying the developed model on an AWS based web-app.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134179580","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169591
K. Rai, N. Mishra, Arun Sharma, Sachin Mishra, Prasad Prakashrao Dahale
Natural disasters, over which humans have no control and which cause far more harm than man-made disasters, have posed the greatest challenge to humanity. Floods are a hydrological disaster that has occurred often in our country since its founding. Rivers like the Ganga and the Brahmaputra run through India. All of these rivers, as well as their tributaries, are involved in a variety of agricultural and human activities. Floods have claimed countless lives and wreaked havoc on the banks of our country’s rivers on numerous occasions. The major goal of our research is to evaluate the many aspects that influence flood risk zonation mapping of 2021 and, as a result, damage assessment. The study area will be the Nainital district of the state of Uttrakhand, with Sentinel 2A as the dataset. The flood mitigation indices, climatic factors, and shapefiles will be interpolated using the Analytical Hierarchical Process (AHP), which will aid in the construction of Flood risk zonation mapping. Flood research is essential in order to reduce the loss and destruction caused by this tragedy.
{"title":"Flood Risk Assessment Mapping of Nainital District Using GIS Tools","authors":"K. Rai, N. Mishra, Arun Sharma, Sachin Mishra, Prasad Prakashrao Dahale","doi":"10.1109/ICAIA57370.2023.10169591","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169591","url":null,"abstract":"Natural disasters, over which humans have no control and which cause far more harm than man-made disasters, have posed the greatest challenge to humanity. Floods are a hydrological disaster that has occurred often in our country since its founding. Rivers like the Ganga and the Brahmaputra run through India. All of these rivers, as well as their tributaries, are involved in a variety of agricultural and human activities. Floods have claimed countless lives and wreaked havoc on the banks of our country’s rivers on numerous occasions. The major goal of our research is to evaluate the many aspects that influence flood risk zonation mapping of 2021 and, as a result, damage assessment. The study area will be the Nainital district of the state of Uttrakhand, with Sentinel 2A as the dataset. The flood mitigation indices, climatic factors, and shapefiles will be interpolated using the Analytical Hierarchical Process (AHP), which will aid in the construction of Flood risk zonation mapping. Flood research is essential in order to reduce the loss and destruction caused by this tragedy.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114818961","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169547
Ashwini Kumar, Kunal P. Ghosh, Sumanto Kar, Rahul Paknikar
Analog-to-digital converters are extensively used for digital signal processing. In this paper, a 4-bit servo tracking type ADC is designed to convert the analog signal to a digital signal. The amplitude range of the analog input signal is 0 to 1 V. Verilog code is designed and simulated in Makerchip IDE for a 4-bit up-down counter circuit. The 4-bit up-down counter along with SKY130 PDK (Process Design Kit) components like resistors and op-amps are used in the circuit design. The 4-bit binary input is converted to analog output using an R-2R ladder type DAC (Digital to Analog Converter). The circuit is simulated in the eSim EDA tool developed by IIT Bombay.
{"title":"Design of 4-bit servo tracking type ADC using Sky-Water SKY130 PDK and eSim","authors":"Ashwini Kumar, Kunal P. Ghosh, Sumanto Kar, Rahul Paknikar","doi":"10.1109/ICAIA57370.2023.10169547","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169547","url":null,"abstract":"Analog-to-digital converters are extensively used for digital signal processing. In this paper, a 4-bit servo tracking type ADC is designed to convert the analog signal to a digital signal. The amplitude range of the analog input signal is 0 to 1 V. Verilog code is designed and simulated in Makerchip IDE for a 4-bit up-down counter circuit. The 4-bit up-down counter along with SKY130 PDK (Process Design Kit) components like resistors and op-amps are used in the circuit design. The 4-bit binary input is converted to analog output using an R-2R ladder type DAC (Digital to Analog Converter). The circuit is simulated in the eSim EDA tool developed by IIT Bombay.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131959503","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169757
Shaik Mahammad Aasheesh, K. N. S. R. Reddy, Manideep Yellani, D. N, Soumya Shridhar Hegde
Reinforcement Learning has performed remarkably well in various games and has the potential to surpass human level gameplay. Although it does good in turn-based games, complex game genres like fighting or much more complex 3D shooters are still a challenge. The created ML model can teach itself how to play a specific game so that it can be used to test the games for completeness, bugs, and irregularities. The model is also used to find out if there are any ways to speed run games by exploiting certain in-game mechanisms or to check if any characters or abilities are overpowered. Models were created for a few games to identify how well these AI models can perform and see what kind of differences were required to switch between the games.
{"title":"Gameplay Automation","authors":"Shaik Mahammad Aasheesh, K. N. S. R. Reddy, Manideep Yellani, D. N, Soumya Shridhar Hegde","doi":"10.1109/ICAIA57370.2023.10169757","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169757","url":null,"abstract":"Reinforcement Learning has performed remarkably well in various games and has the potential to surpass human level gameplay. Although it does good in turn-based games, complex game genres like fighting or much more complex 3D shooters are still a challenge. The created ML model can teach itself how to play a specific game so that it can be used to test the games for completeness, bugs, and irregularities. The model is also used to find out if there are any ways to speed run games by exploiting certain in-game mechanisms or to check if any characters or abilities are overpowered. Models were created for a few games to identify how well these AI models can perform and see what kind of differences were required to switch between the games.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116442559","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 : 2023-04-21DOI: 10.1109/ICAIA57370.2023.10169781
Jyotirmoy Pathak, S. Tripathi
The internationalisation of the semiconductor supply chain brings with it an increase in the hazards posed to both data security and physical security. Theft of intellectual property through reverse engineering and harmful design alterations are two primary threats. The latter is supported, in part, by the fruitful use of reverse engineering to the design. Two of the strategies that are currently under investigation to prevent reverse engineering by end users or foundries are known as IC stealth and logic blocking. Nonetheless, for a number of years, one of the most difficult challenges has been the creation of low-overload camouflage and blocking schemes that are resilient enough to endure the ever-changing condition of heart attacks. This article describes a unique design for a disguised multiplier as well as an implementation of that architecture. The structure that has been suggested is capable of being reorganised to compute an S-bit Baugh-Wooley multiplier. To conceal obfuscation modes, a fresh control flow method has been developed and implemented. When compared to the original design, it has been demonstrated that the suggested method results in space and power needs that are significantly reduced.
{"title":"Novel Obfuscated Secure Architecture for Baugh Wooley Multiplier","authors":"Jyotirmoy Pathak, S. Tripathi","doi":"10.1109/ICAIA57370.2023.10169781","DOIUrl":"https://doi.org/10.1109/ICAIA57370.2023.10169781","url":null,"abstract":"The internationalisation of the semiconductor supply chain brings with it an increase in the hazards posed to both data security and physical security. Theft of intellectual property through reverse engineering and harmful design alterations are two primary threats. The latter is supported, in part, by the fruitful use of reverse engineering to the design. Two of the strategies that are currently under investigation to prevent reverse engineering by end users or foundries are known as IC stealth and logic blocking. Nonetheless, for a number of years, one of the most difficult challenges has been the creation of low-overload camouflage and blocking schemes that are resilient enough to endure the ever-changing condition of heart attacks. This article describes a unique design for a disguised multiplier as well as an implementation of that architecture. The structure that has been suggested is capable of being reorganised to compute an S-bit Baugh-Wooley multiplier. To conceal obfuscation modes, a fresh control flow method has been developed and implemented. When compared to the original design, it has been demonstrated that the suggested method results in space and power needs that are significantly reduced.","PeriodicalId":196526,"journal":{"name":"2023 International Conference on Artificial Intelligence and Applications (ICAIA) Alliance Technology Conference (ATCON-1)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133790682","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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