Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach’s feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively.
{"title":"Classification and recognition of soybean leaf diseases in Madhya Pradesh and Chhattisgarh using Deep learning methods","authors":"Shriniket Dixit, Anant Kumar, Akash Haripriya, Khitij Bohre, Kathiravan Srinivasan","doi":"10.1109/PCEMS58491.2023.10136030","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136030","url":null,"abstract":"Soybean is a major economic crop worldwide. So proper disease control measures must be implemented to reduce losses. These diseases can significantly affect the yield and quality of soybeans. Machine vision and pattern recognition technologies can help accurately diagnose crop diseases and minimize financial losses for soybean farmers. Many research papers discuss the use of deep learning algorithms for imagebased disease detection, including for soybean crops based on CNN, SVM, KNN, etc. However, lacking a well-curated dataset for soybean diseases is a challenge. Additionally, many existing research papers focus more on demonstrating the approach’s feasibility rather than providing solutions to the specific problems faced in a particular region. The proposed deep learning-based classification system for soybean leaf diseases can help identify Angular Leaf spots, Bacterial blight, Soybean Rust, and Downy mildew. An image dataset was created, and image-enhancing techniques were applied during pre-processing. The proposed classifier system achieved an efficiency of 83.9%, 93.01%, and 71.98% in classifying diseases using CNN, Resnet-V2, and KNN classifiers, respectively.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130369351","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}
This paper proposes an autonomously assessing system for the verbal examination of candidates. The system uses audio-video inputs and processes them to detect the candidate’s spoken answer, and compares it to the model answer in the dataset with the corresponding question. The semantic similarity score will be calculated and used to recommend the next question from the database using various types of recommendation systems discussed in the paper. Additionally, the system employs video analysis techniques to detect and prevent modern malpractices like multiple faces and reading from notes during the examination process. The proposed system aims to improve the efficiency and fairness of verbal examinations by eliminating human bias and accurately evaluating the candidate’s understanding of the subject. The system performance will be evaluated using a dataset of spoken answers and the results will demonstrate its effectiveness in improving the efficiency and fairness of the verbal examination process.
{"title":"Multimodal Autonomous Verbal Assessment With Visual Inspection","authors":"Meet Agrawal, Atharva Kathale, Sahil Purohit, Kalyani Sainis, Praveen Kumar, Mansi A. Radke","doi":"10.1109/PCEMS58491.2023.10136061","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136061","url":null,"abstract":"This paper proposes an autonomously assessing system for the verbal examination of candidates. The system uses audio-video inputs and processes them to detect the candidate’s spoken answer, and compares it to the model answer in the dataset with the corresponding question. The semantic similarity score will be calculated and used to recommend the next question from the database using various types of recommendation systems discussed in the paper. Additionally, the system employs video analysis techniques to detect and prevent modern malpractices like multiple faces and reading from notes during the examination process. The proposed system aims to improve the efficiency and fairness of verbal examinations by eliminating human bias and accurately evaluating the candidate’s understanding of the subject. The system performance will be evaluated using a dataset of spoken answers and the results will demonstrate its effectiveness in improving the efficiency and fairness of the verbal examination process.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130459447","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136065
N. Sakthipriya, V. Govindasamy, V. Akila
Internet of Things plays a vital role in our everyday lives in terms of economic, social, and commercial aspects. The widespread use of IoT devices has made them a prime target for cyber-attacks. IoT botnet attacks usually have a greater sensitivity to the consequences that might result from launching other attacks such as DDoS attacks and dissemination of sensitive information. For botnet detection in the IoT environment, deep learning mechanisms have emerged. But processing high-dimensional data is difficult, and it adversely affects DL-based botnet detection systems. Various dimensionality reduction methods have been proposed by researchers to address this concern. The purpose of this study is to examine and compare current mainstream dimensionality reduction methods. This will enable us to understand how reducing the number of features may lead to higher classification accuracy. Extensive tests are conducted on the NBaIoT dataset to verify the viability of PCA and auto encoder dimensionality reduction strategies. Results show that Auto encoder algorithm outperform PCA dimensionality reduction methods by the accuracy of 95.02%.
{"title":"A Comparative Analysis of various Dimensionality Reduction Techniques on N-BaIoT Dataset for IoT Botnet Detection","authors":"N. Sakthipriya, V. Govindasamy, V. Akila","doi":"10.1109/PCEMS58491.2023.10136065","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136065","url":null,"abstract":"Internet of Things plays a vital role in our everyday lives in terms of economic, social, and commercial aspects. The widespread use of IoT devices has made them a prime target for cyber-attacks. IoT botnet attacks usually have a greater sensitivity to the consequences that might result from launching other attacks such as DDoS attacks and dissemination of sensitive information. For botnet detection in the IoT environment, deep learning mechanisms have emerged. But processing high-dimensional data is difficult, and it adversely affects DL-based botnet detection systems. Various dimensionality reduction methods have been proposed by researchers to address this concern. The purpose of this study is to examine and compare current mainstream dimensionality reduction methods. This will enable us to understand how reducing the number of features may lead to higher classification accuracy. Extensive tests are conducted on the NBaIoT dataset to verify the viability of PCA and auto encoder dimensionality reduction strategies. Results show that Auto encoder algorithm outperform PCA dimensionality reduction methods by the accuracy of 95.02%.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132519857","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136114
Nimish Samant, Heramba Limaye, Anurag Bapat, Shraddha S. Shinde, Amit K. Nerurkar
This research paper aims to investigate the use of text classification for automatic issue tagging in issue-tracking systems. The study focuses on the current state of issue-tracking systems and their limitations in terms of issue tagging, specifically the manual effort required to tag and categorize issues. The research describes the implementation of a text classification model for automatic issue tagging and evaluates its performance in terms of accuracy and loss. The results of this study show that the use of text classification can significantly improve the efficiency and accuracy of issue tagging in issue-tracking systems, while also providing a more efficient and user-friendly experience. The results also provide valuable insights into the design and implementation of issue-tracking systems and demonstrates the potential of deep learning to enhance the accuracy and efficiency of issue-tracking. This research also provides insights for software development teams and managers on how to use text classification techniques to improve the efficiency and effectiveness of their issue-tracking systems.
{"title":"Optimizing Issue Tracking Systems using Deep Learning-based Issue Classification","authors":"Nimish Samant, Heramba Limaye, Anurag Bapat, Shraddha S. Shinde, Amit K. Nerurkar","doi":"10.1109/PCEMS58491.2023.10136114","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136114","url":null,"abstract":"This research paper aims to investigate the use of text classification for automatic issue tagging in issue-tracking systems. The study focuses on the current state of issue-tracking systems and their limitations in terms of issue tagging, specifically the manual effort required to tag and categorize issues. The research describes the implementation of a text classification model for automatic issue tagging and evaluates its performance in terms of accuracy and loss. The results of this study show that the use of text classification can significantly improve the efficiency and accuracy of issue tagging in issue-tracking systems, while also providing a more efficient and user-friendly experience. The results also provide valuable insights into the design and implementation of issue-tracking systems and demonstrates the potential of deep learning to enhance the accuracy and efficiency of issue-tracking. This research also provides insights for software development teams and managers on how to use text classification techniques to improve the efficiency and effectiveness of their issue-tracking systems.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128445288","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136086
Chithraja Rajan, Priya Suman, B. Neole, Jyoti Patel
In today’s scenario, a versatile device to minimise power consumption in resource constraint IoT applications are on high demand. Focusing this, we demonstrate a nanowire TFET device comprising of a gate all around structure for better gate controllability and hetero dielectric as gate oxide. Presented high-k oxide at source side provides a high ON-current i.e. 4.28× 1$0^{-5}$A/$mu$m and threshold voltage i.e. 0.3 V, which follows ITRS norms for low power devices. Additionally, instead of fundamental doped device, polarity-based concept is incorporated to provide immunity against RDFs and RF analysis is performed to judge its capability for wireless communication and RFIC applications; in which high cutoff frequency of 0.6 PHz and GBP of 60 THz are effectively obtained. Along with this, a high switching speed is also obtained, which is very much preferable for digital as well as analog applications.
在当今的场景中，在资源受限的物联网应用中，一种能够最大限度地减少功耗的多功能设备需求很高。针对这一点，我们展示了一种纳米线ttfet器件，该器件由栅极全绕结构组成，具有更好的栅极可控性和异质介质作为栅极氧化物。源侧高k氧化物提供高导通电流，即4.28× 1$0^{-5}$ a /$mu$m，阈值电压为0.3 V，符合低功率器件的ITRS规范。此外，采用基于极性的概念代替基元掺杂器件来提供对rdf的免疫，并进行RF分析以判断其在无线通信和RFIC应用中的能力;有效地获得了0.6 PHz的高截止频率和60 THz的GBP。与此同时，还获得了高开关速度，这对于数字和模拟应用都是非常可取的。
{"title":"Impact of Gate All Around Architecture in Polarity Based TFET with RF/Analog Analysis","authors":"Chithraja Rajan, Priya Suman, B. Neole, Jyoti Patel","doi":"10.1109/PCEMS58491.2023.10136086","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136086","url":null,"abstract":"In today’s scenario, a versatile device to minimise power consumption in resource constraint IoT applications are on high demand. Focusing this, we demonstrate a nanowire TFET device comprising of a gate all around structure for better gate controllability and hetero dielectric as gate oxide. Presented high-k oxide at source side provides a high ON-current i.e. 4.28× 1$0^{-5}$A/$mu$m and threshold voltage i.e. 0.3 V, which follows ITRS norms for low power devices. Additionally, instead of fundamental doped device, polarity-based concept is incorporated to provide immunity against RDFs and RF analysis is performed to judge its capability for wireless communication and RFIC applications; in which high cutoff frequency of 0.6 PHz and GBP of 60 THz are effectively obtained. Along with this, a high switching speed is also obtained, which is very much preferable for digital as well as analog applications.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"2010 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131878416","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136071
Rishi Rakesh Shrivastava, G. Deepak
There is a need for Ontology modelling and automatic generation of Ontologies in order to assimilate knowledge World Wide Web knowledge as a strategic model. Ontologies are the best knowledge descriptor model as they have some amount of human cognition associated with them because either humans are major contributors when they are generated manually or are indirect contributors when they are semi automatically generated. Internet of Things is a domain which has strategically evolved in the last few years, and there is a need for integrating several facets of Internet of Things Ontology. In this paper a strategic scheme for Internet of Things Ontology integration for Internet of Things domain with different perspective are proposed wherein the dataset are subjected to tag generation which is further classified using the AdaBoost classifier which are aligned with the random core classes of the existing variational Ontologies in the Internet of Things domain using Shannon’s entropy and the pointwise mutual information measure with differential step deviation measure. Which yields average precision and recall of 96.83 and 97.95 respectively.
{"title":"AIOIML: Automatic Integration of Ontologies for IoT Domain Using Hybridized Machine Learning Techniques","authors":"Rishi Rakesh Shrivastava, G. Deepak","doi":"10.1109/PCEMS58491.2023.10136071","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136071","url":null,"abstract":"There is a need for Ontology modelling and automatic generation of Ontologies in order to assimilate knowledge World Wide Web knowledge as a strategic model. Ontologies are the best knowledge descriptor model as they have some amount of human cognition associated with them because either humans are major contributors when they are generated manually or are indirect contributors when they are semi automatically generated. Internet of Things is a domain which has strategically evolved in the last few years, and there is a need for integrating several facets of Internet of Things Ontology. In this paper a strategic scheme for Internet of Things Ontology integration for Internet of Things domain with different perspective are proposed wherein the dataset are subjected to tag generation which is further classified using the AdaBoost classifier which are aligned with the random core classes of the existing variational Ontologies in the Internet of Things domain using Shannon’s entropy and the pointwise mutual information measure with differential step deviation measure. Which yields average precision and recall of 96.83 and 97.95 respectively.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124569097","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136101
Poongkuyil Muse, M. S., Hamil Stanly
Efficient log analysis involves collecting, evaluating, and managing raw data from computer-generated records. As security vulnerabilities increase, the analysis of logs has become vital and crucial in multidisciplinary domains. Maintaining and analyzing the log is a pivotal part of every organization as tons of logs are generated every millisecond. However, anomaly detection and log parsing addressed so far, rely on a time-consuming training algorithm based on a Machine Learning framework. The proposed method detects anomalies from real-time data generated from the data centre without the need for a training algorithm. Detection and visualization of malicious activities are done by Elasticsearch, Logstash, and Kibana (ELK) framework. The process of shipping, parsing, indexing, and anomaly detection is carried out using an unsupervised machine learning algorithm which gives a clear inference to detect bots and perform unique log session classification. A real-time Apache HTTP Server log is accessed and anomalous behavior is identified based on the incoming requests. Experiments on real-time data show that 13.76% of anomalies are detected on per weekly basis.
{"title":"Online Log Analysis(OLA) for Malicious User Activities","authors":"Poongkuyil Muse, M. S., Hamil Stanly","doi":"10.1109/PCEMS58491.2023.10136101","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136101","url":null,"abstract":"Efficient log analysis involves collecting, evaluating, and managing raw data from computer-generated records. As security vulnerabilities increase, the analysis of logs has become vital and crucial in multidisciplinary domains. Maintaining and analyzing the log is a pivotal part of every organization as tons of logs are generated every millisecond. However, anomaly detection and log parsing addressed so far, rely on a time-consuming training algorithm based on a Machine Learning framework. The proposed method detects anomalies from real-time data generated from the data centre without the need for a training algorithm. Detection and visualization of malicious activities are done by Elasticsearch, Logstash, and Kibana (ELK) framework. The process of shipping, parsing, indexing, and anomaly detection is carried out using an unsupervised machine learning algorithm which gives a clear inference to detect bots and perform unique log session classification. A real-time Apache HTTP Server log is accessed and anomalous behavior is identified based on the incoming requests. Experiments on real-time data show that 13.76% of anomalies are detected on per weekly basis.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128439337","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136095
Biby Joseph, Gopireddy Chaithanyakumar Reddy, R. Kavitha
Address decoders play a vital role in Static Random-Access Memory (SRAM) memory array architecture to fetch the data in less span of time. As compared to other memory devices, SRAM based Artificial Intelligence (AI) accelerator possess high speed in which memory array address decoder plays a vital role. As a result, the address decoder is the key element for SRAM performance. In this paper, an energy efficient address decoder with low power dissipation is proposed, which can be used for SRAM based AI accelerator. Major part of power consumption of memory chip depends on address decoders. As we go down from higher technology nodes to lower technology nodes, leakage power increases which results in total power consumption. Source biasing technique is used to reduce static power consumption. This paper compares the proposed Address decoder in UMC 65nm technology with existing architectures in terms of power, delay and energy. This decoder design has an improvement of 85.8% average power and 87.46% energy as compared with existing conventional 6-64 decoder circuit using pre-decoding methodology.
{"title":"Energy Efficient Memory Decoder for SRAM Based AI Accelerator","authors":"Biby Joseph, Gopireddy Chaithanyakumar Reddy, R. Kavitha","doi":"10.1109/PCEMS58491.2023.10136095","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136095","url":null,"abstract":"Address decoders play a vital role in Static Random-Access Memory (SRAM) memory array architecture to fetch the data in less span of time. As compared to other memory devices, SRAM based Artificial Intelligence (AI) accelerator possess high speed in which memory array address decoder plays a vital role. As a result, the address decoder is the key element for SRAM performance. In this paper, an energy efficient address decoder with low power dissipation is proposed, which can be used for SRAM based AI accelerator. Major part of power consumption of memory chip depends on address decoders. As we go down from higher technology nodes to lower technology nodes, leakage power increases which results in total power consumption. Source biasing technique is used to reduce static power consumption. This paper compares the proposed Address decoder in UMC 65nm technology with existing architectures in terms of power, delay and energy. This decoder design has an improvement of 85.8% average power and 87.46% energy as compared with existing conventional 6-64 decoder circuit using pre-decoding methodology.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"449 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115931360","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136117
Omkar Gupta, Irika Nikhita Kalapala, K. Bhurchandi
Images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image denoising.The efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising.
{"title":"Enhancing Image Denoising Performance through a Family of Algorithms","authors":"Omkar Gupta, Irika Nikhita Kalapala, K. Bhurchandi","doi":"10.1109/PCEMS58491.2023.10136117","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136117","url":null,"abstract":"Images are an integral and indispensable aspect of various disciplines, such as medicine, surveillance, and the entertainment industry. However, the quality of images can be severely compromised by the presence of sensor noise, quantization errors, or transmission errors. This research proposes a novel approach that combines wavelet thresholding and BM3D (Block-Matching and 3D Filtering) techniques for effective image denoising.The efficacy of the methodologies is evaluated and compared to cutting-edge denoising techniques, demonstrating superior performance in both quantitative metrics and visual quality. Furthermore, the study delves into the intricate mechanisms underlying the denoising process and the impact of various parameters on the denoising performance, contributing significantly to the field of image denoising.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116415609","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 : 2023-04-05DOI: 10.1109/PCEMS58491.2023.10136033
Bhavesh Dhake, C. Shetye, Pratik Borhade, Devish Gawas, Amit K. Nerurkar
Cyber hazards have emerged as a significant cause of worry for society. Firms are beginning to invest heavily in developing Cyber Threat Intelligence in recent years in order to combat the rising threat of cyber-attacks (CTI). Predominantly, many businesses gathered and analyzed data from internal log files, leading in reactive CTI, which is essentially a data-driven process. The internet hacker community may provide significant proactive CTI value by alerting enterprises about risks that they were previously unaware of. Forums, more than any other platform, give the most metadata, data persistence, and tens of thousands of publicly available Tools, Techniques, and Procedures. Anticrawling techniques, including as authentication, throttling, and obfuscation, are commonly employed in forums. This study intends to create a unique web crawler, as well as use machine learning and deep learning approaches with neural networks to automatically categorize hacker forum data into predetermined categories and anticipate probable future cyber risks for proactive and timely CTI.
{"title":"Stratification of Hacker Forums and Predicting Cyber Assaults for Proactive Cyber Threat Intelligence","authors":"Bhavesh Dhake, C. Shetye, Pratik Borhade, Devish Gawas, Amit K. Nerurkar","doi":"10.1109/PCEMS58491.2023.10136033","DOIUrl":"https://doi.org/10.1109/PCEMS58491.2023.10136033","url":null,"abstract":"Cyber hazards have emerged as a significant cause of worry for society. Firms are beginning to invest heavily in developing Cyber Threat Intelligence in recent years in order to combat the rising threat of cyber-attacks (CTI). Predominantly, many businesses gathered and analyzed data from internal log files, leading in reactive CTI, which is essentially a data-driven process. The internet hacker community may provide significant proactive CTI value by alerting enterprises about risks that they were previously unaware of. Forums, more than any other platform, give the most metadata, data persistence, and tens of thousands of publicly available Tools, Techniques, and Procedures. Anticrawling techniques, including as authentication, throttling, and obfuscation, are commonly employed in forums. This study intends to create a unique web crawler, as well as use machine learning and deep learning approaches with neural networks to automatically categorize hacker forum data into predetermined categories and anticipate probable future cyber risks for proactive and timely CTI.","PeriodicalId":330870,"journal":{"name":"2023 2nd International Conference on Paradigm Shifts in Communications Embedded Systems, Machine Learning and Signal Processing (PCEMS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115589556","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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