Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072751
Pronaya Bhattacharya, Mohd. Zuhair, Debanjana Roy, V. Prasad, Darshan Savaliya
The success of human resource management (HRM) closely synchronizes with the success of the prospective candidate (PCs) recruitment cycle (i.e. from job application to joining process of employee). However, finding the right PC according to the job description (JDs) is a complex task owing to manual background checks, and maintaining the auditability of the recruitment process by third-party recruitment (TPR) services. Recent studies have suggested the introduction of the blockchain (BC) and artificial intelligence (AI) in HRM processes to assure chronology, auditability, and automation, but limited approaches have discussed the use of explainable AI (xAI) for model interpretability. To address the issues, we propose a fusion scheme, AaJeeViKa, which integrates BC and explainable AI (xAI) to integrate trusted analytics in staffing and recruitment processes. The scheme generates a job suitability score (JSS), on which an interview call is sent to PC (cutoff threshold). The interview score and JSS score are added to form the employee reputation score (ERS), and the output prediction significance is computed by Shapley additive explanations (SHAP) explainers. The xAI result along with other information is meta-recorded and updated on BC ledgers. The results indicate that the scheme is highly beneficial for modern organizations to renovate their staffing and recruitment policies.
{"title":"AaJeeViKa: Trusted Explainable AI Based Recruitment Scheme in Smart Organizations","authors":"Pronaya Bhattacharya, Mohd. Zuhair, Debanjana Roy, V. Prasad, Darshan Savaliya","doi":"10.1109/IC3I56241.2022.10072751","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072751","url":null,"abstract":"The success of human resource management (HRM) closely synchronizes with the success of the prospective candidate (PCs) recruitment cycle (i.e. from job application to joining process of employee). However, finding the right PC according to the job description (JDs) is a complex task owing to manual background checks, and maintaining the auditability of the recruitment process by third-party recruitment (TPR) services. Recent studies have suggested the introduction of the blockchain (BC) and artificial intelligence (AI) in HRM processes to assure chronology, auditability, and automation, but limited approaches have discussed the use of explainable AI (xAI) for model interpretability. To address the issues, we propose a fusion scheme, AaJeeViKa, which integrates BC and explainable AI (xAI) to integrate trusted analytics in staffing and recruitment processes. The scheme generates a job suitability score (JSS), on which an interview call is sent to PC (cutoff threshold). The interview score and JSS score are added to form the employee reputation score (ERS), and the output prediction significance is computed by Shapley additive explanations (SHAP) explainers. The xAI result along with other information is meta-recorded and updated on BC ledgers. The results indicate that the scheme is highly beneficial for modern organizations to renovate their staffing and recruitment policies.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130083594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073042
S. V. Prasath, N. Pushpalatha, D. Gunapriya, P. M. Kumar, R. T. Santhosh, S. Srinivasan
Agriculture's productivity has a big impact on the Indian economy. Plant disease identification is key to agricultural output. Early detection of sick plants reduces productivity and volume losses. Plant diseases are studied by examining the plant's apparent characteristics. Long-term farming requires monitoring crop health. Handling plant disease outbreaks is tough. Huge effort, plant disease knowledge, and processing time are needed. Early identification is crucial since it can affect output quantity and quality. When crops on large farms become apparent on the plant's leaves, an automated method will be useful. Image processing is used to identify plant diseases. Disease detection involves picture capture, pre-processing, segmentation, feature extraction, and classification. This study looked for plant illnesses using leaf pictures. In this work, leaf pictures were analysed to diagnose plant illnesses. Some strategies for recognising plant diseases were also addressed. Neural Networks were used to classify leaf diseases in this article. AGRI ROBOT helped with this.
{"title":"Automated Agronomic Bot for Green Ailment Scanner","authors":"S. V. Prasath, N. Pushpalatha, D. Gunapriya, P. M. Kumar, R. T. Santhosh, S. Srinivasan","doi":"10.1109/IC3I56241.2022.10073042","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073042","url":null,"abstract":"Agriculture's productivity has a big impact on the Indian economy. Plant disease identification is key to agricultural output. Early detection of sick plants reduces productivity and volume losses. Plant diseases are studied by examining the plant's apparent characteristics. Long-term farming requires monitoring crop health. Handling plant disease outbreaks is tough. Huge effort, plant disease knowledge, and processing time are needed. Early identification is crucial since it can affect output quantity and quality. When crops on large farms become apparent on the plant's leaves, an automated method will be useful. Image processing is used to identify plant diseases. Disease detection involves picture capture, pre-processing, segmentation, feature extraction, and classification. This study looked for plant illnesses using leaf pictures. In this work, leaf pictures were analysed to diagnose plant illnesses. Some strategies for recognising plant diseases were also addressed. Neural Networks were used to classify leaf diseases in this article. AGRI ROBOT helped with this.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072518
Mg Balasubramanian, B.T. Geetha
The main aim of this research is to improve the efficiency in a multilevel inverter by reducing the harmonics to get a clear sinusoidal waveform by using novel phase disposition pulse width modulation and compared with the Multilevel inverter with the conventional pulse width modulation. Materials and Methods: A total number of 14 samples are collected by varying the frequencies of the input pulses. These samples are divided into two groups each of 7 samples. Group 1 is novel phase disposition pulse width modulation and group 2 is conventional pulse width modulation. The harmonics were calculated to quantify the performance of the novel Phase Disposition Pulse Width Modulation and conventional pulse width modulation.The G power is taken as 0.8. Results: Multilevel Inverter using the novel Phase Disposition Pulse width modulation has harmonics of 27.47%, and the same for Conventional Pulse Width Modulation is 52.59%. The significance value is 0.235 ($p gt 0.05$, statistically insignificant). Conclusion: It is observed that novel Phase disposition Pulse width modulation performs better than the Conventional Pulse width modulation in a multilevel inverter for the production of good sinusoidal and reduction of the harmonics based on efficiency.
{"title":"Reduction of Harmonics in Multilevel Inverter using Phase Disposition PWM compared with Conventional PWM based on Efficiency","authors":"Mg Balasubramanian, B.T. Geetha","doi":"10.1109/IC3I56241.2022.10072518","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072518","url":null,"abstract":"The main aim of this research is to improve the efficiency in a multilevel inverter by reducing the harmonics to get a clear sinusoidal waveform by using novel phase disposition pulse width modulation and compared with the Multilevel inverter with the conventional pulse width modulation. Materials and Methods: A total number of 14 samples are collected by varying the frequencies of the input pulses. These samples are divided into two groups each of 7 samples. Group 1 is novel phase disposition pulse width modulation and group 2 is conventional pulse width modulation. The harmonics were calculated to quantify the performance of the novel Phase Disposition Pulse Width Modulation and conventional pulse width modulation.The G power is taken as 0.8. Results: Multilevel Inverter using the novel Phase Disposition Pulse width modulation has harmonics of 27.47%, and the same for Conventional Pulse Width Modulation is 52.59%. The significance value is 0.235 ($p gt 0.05$, statistically insignificant). Conclusion: It is observed that novel Phase disposition Pulse width modulation performs better than the Conventional Pulse width modulation in a multilevel inverter for the production of good sinusoidal and reduction of the harmonics based on efficiency.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134286551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073430
K. Durgalakshmi, P. Anbarasu, V. Karpagam, A. Venkatesh, B. Kannapiran, Vandana Sharma
Multilevel inverters become more popular and attracted many in research and industrial applications. The MLI's were widely chosen for their higher and medium power/ voltage applications which were very helpful for interfacing non-conventional energy sources. Also for the past decade the research under development of reduced switch MLI topologies rapidly increased by making appropriate combination of switch connections. It will provide multi-level outputs with less harmonic distortions. Due to this, reduced switch MLI is most popularly used for power converter topologies. The various traditional multi-level inverter topologies and hybrid MLI schemes which are applied for non-conventional energy sources are discussed in this research paper and also it includes distinct modulation schemes to increase the performance of MLI.
{"title":"Utilization Of Reduced Switch Components With Different Topologies In Multi-Level Inverter For Renewable Energy Applications-A Detailed Review","authors":"K. Durgalakshmi, P. Anbarasu, V. Karpagam, A. Venkatesh, B. Kannapiran, Vandana Sharma","doi":"10.1109/IC3I56241.2022.10073430","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073430","url":null,"abstract":"Multilevel inverters become more popular and attracted many in research and industrial applications. The MLI's were widely chosen for their higher and medium power/ voltage applications which were very helpful for interfacing non-conventional energy sources. Also for the past decade the research under development of reduced switch MLI topologies rapidly increased by making appropriate combination of switch connections. It will provide multi-level outputs with less harmonic distortions. Due to this, reduced switch MLI is most popularly used for power converter topologies. The various traditional multi-level inverter topologies and hybrid MLI schemes which are applied for non-conventional energy sources are discussed in this research paper and also it includes distinct modulation schemes to increase the performance of MLI.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"178 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132788985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073224
Varun Malik, R. Mittal, S. V. Singh
Using tags and other forms of textual information, online retailers can create their product listings, descriptions, and categories. E-commerce information services, such as search and product recommendation, depend significantly on textual features to assist buyers in finding the items they want. This research focuses on “tags,” which often use textual descriptions of items. We assume that merchants are not always the “best” suppliers of item tag information, either because they are ill-equipped to do so (since they have not been “trained”) or because they are purposefully attempting to rig the system by using misleading or erroneous tags to sell their commodities (tag spam). To address these concerns, we may use automated tag recommendation techniques to enhance the precision with which we suggest tags for every specific product. We proposed EPR-ML for E-commerce product recommendation using NLP and ML algorithms. This research employed a product sentiment dataset normalized using NLP; the best features were selected using Logistic regression (LR). The classification was performed using various machine learning algorithms, including Linear support vector machine (L- SVM) and Gaussian nave Bayes (GNB), to determine which model is most accurate at predicting the number of days it will take a video to trend from the time it was uploaded and the number of days it will trend on the trending list. Using LSVM, the research achieved a maximum accuracy of 96%.
{"title":"EPR-ML: E-Commerce Product Recommendation Using NLP and Machine Learning Algorithm","authors":"Varun Malik, R. Mittal, S. V. Singh","doi":"10.1109/IC3I56241.2022.10073224","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073224","url":null,"abstract":"Using tags and other forms of textual information, online retailers can create their product listings, descriptions, and categories. E-commerce information services, such as search and product recommendation, depend significantly on textual features to assist buyers in finding the items they want. This research focuses on “tags,” which often use textual descriptions of items. We assume that merchants are not always the “best” suppliers of item tag information, either because they are ill-equipped to do so (since they have not been “trained”) or because they are purposefully attempting to rig the system by using misleading or erroneous tags to sell their commodities (tag spam). To address these concerns, we may use automated tag recommendation techniques to enhance the precision with which we suggest tags for every specific product. We proposed EPR-ML for E-commerce product recommendation using NLP and ML algorithms. This research employed a product sentiment dataset normalized using NLP; the best features were selected using Logistic regression (LR). The classification was performed using various machine learning algorithms, including Linear support vector machine (L- SVM) and Gaussian nave Bayes (GNB), to determine which model is most accurate at predicting the number of days it will take a video to trend from the time it was uploaded and the number of days it will trend on the trending list. Using LSVM, the research achieved a maximum accuracy of 96%.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130872450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073124
Archana Shahi, Sukhpreet Kaur, A. Mittal, S. V. Singh
Understanding the role of technology and its effect on the success of the women in healthcare sector is crucial to study and shall be extensive. In any case, a precise survey that offers broad comprehension into what influences medical care innovations and administrations and covers differentiated trends in enormous scope research stays back on the position. Consequently, this audit intends to survey deliberately the articles distributed on innovation acknowledgment in medical care. There are a very few research that study about the technology and innovation playing a role in the success of the women in the healthcare sector. The aim of the paper is to study the intention of technology and its driving position about the niche sector of women in the healthcare segment. UTAUT model will be discussed in the paper for giving the structural balance to the test. The data collection and systematic analysis approach will also be used to deliver the outcomes of the technology adoption in this segment.
{"title":"Building Technology adoption model for the success of Women Healthcare Workers","authors":"Archana Shahi, Sukhpreet Kaur, A. Mittal, S. V. Singh","doi":"10.1109/IC3I56241.2022.10073124","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073124","url":null,"abstract":"Understanding the role of technology and its effect on the success of the women in healthcare sector is crucial to study and shall be extensive. In any case, a precise survey that offers broad comprehension into what influences medical care innovations and administrations and covers differentiated trends in enormous scope research stays back on the position. Consequently, this audit intends to survey deliberately the articles distributed on innovation acknowledgment in medical care. There are a very few research that study about the technology and innovation playing a role in the success of the women in the healthcare sector. The aim of the paper is to study the intention of technology and its driving position about the niche sector of women in the healthcare segment. UTAUT model will be discussed in the paper for giving the structural balance to the test. The data collection and systematic analysis approach will also be used to deliver the outcomes of the technology adoption in this segment.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132840784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072879
Mohammed E. Seno, Areej Adnan Abed, Y. Hamad, U. M. Bhatt, B. Babu, Shomil Bansal
In order to simplify our day-to-day lives, a combination of technical and imaginative skills is needed. Leveraging the Internet and its various technology enhancements, everyday objects can now be networked and identified uniquely. Increasing efficiency, accuracy, comfort, and economic benefits are the main goals of the Internet of Things. It allows our lives to be easier by automating every task around us. IoT has revolutionized human life. In both commercial and residential kitchens, incidents involving the kitchen have increased in recent years. Frequently, people cook food in the kitchen. But if the gas cylinder leaks, it may be dangerous. Using IoT technologies, we try to lower these risks. Our solution has merged Node MCUs with gas sensors, temperature sensors, MQ3 sensors, alarm systems, exhaust fans, and load cells on the hardware side. Mobile apps and integrated Node MCUs have been utilized on the software side. The gas sensor will transmit a warning message to the user if it discovers a gas leak, and the stove's knob will switch off automatically as a result. A gas leakage monitoring system is created to increase home security. When a gas leak is discovered, the system notifies the user through SMS, and as a safety precaution, it shuts off the electricity while sounding an alarm. This sensor-based IoT-based system aims to monitor the quality and freshness of food. In addition to detecting the freshness of household food items like dairy items, fruits, and food items, this smart device prevents suffocation and explosion caused by gas leaks. The temperature of the smart kitchen is monitored, and if it exceeds a certain threshold, the exhaust fan is activated. It also monitors all software's functionality.
{"title":"Cloud Based Smart Kitchen Automation and Monitoring","authors":"Mohammed E. Seno, Areej Adnan Abed, Y. Hamad, U. M. Bhatt, B. Babu, Shomil Bansal","doi":"10.1109/IC3I56241.2022.10072879","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072879","url":null,"abstract":"In order to simplify our day-to-day lives, a combination of technical and imaginative skills is needed. Leveraging the Internet and its various technology enhancements, everyday objects can now be networked and identified uniquely. Increasing efficiency, accuracy, comfort, and economic benefits are the main goals of the Internet of Things. It allows our lives to be easier by automating every task around us. IoT has revolutionized human life. In both commercial and residential kitchens, incidents involving the kitchen have increased in recent years. Frequently, people cook food in the kitchen. But if the gas cylinder leaks, it may be dangerous. Using IoT technologies, we try to lower these risks. Our solution has merged Node MCUs with gas sensors, temperature sensors, MQ3 sensors, alarm systems, exhaust fans, and load cells on the hardware side. Mobile apps and integrated Node MCUs have been utilized on the software side. The gas sensor will transmit a warning message to the user if it discovers a gas leak, and the stove's knob will switch off automatically as a result. A gas leakage monitoring system is created to increase home security. When a gas leak is discovered, the system notifies the user through SMS, and as a safety precaution, it shuts off the electricity while sounding an alarm. This sensor-based IoT-based system aims to monitor the quality and freshness of food. In addition to detecting the freshness of household food items like dairy items, fruits, and food items, this smart device prevents suffocation and explosion caused by gas leaks. The temperature of the smart kitchen is monitored, and if it exceeds a certain threshold, the exhaust fan is activated. It also monitors all software's functionality.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133623076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073385
P. Kshirsagar, D. H. Reddy, Mallika Dhingra, Dharmesh Dhabliya, Ankur Gupta
In the previous several years, wireless telecommunications technology has seen a significant change. The term “generation” in the context of wireless communication typically describes to a shift in the essential characteristics of the assistance being offered, such as transmission technologies, bit rates, frequency bands, channel frequency bandwidth, and data transfer capacity. One of the most active technological fields that is expanding too quickly is the wireless age. There is a need to implement such technologies that can be integrated and adjusted in order to produce a more advanced and unified system because there are numerous superior technologies available. In this work, we provide a summary of several wireless telecommunications technologies, in particular, 4G, 5G, and 6G Networks, and a detailed comparison between them.
{"title":"A Review on Comparative study of 4G, 5G and 6G Networks","authors":"P. Kshirsagar, D. H. Reddy, Mallika Dhingra, Dharmesh Dhabliya, Ankur Gupta","doi":"10.1109/IC3I56241.2022.10073385","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073385","url":null,"abstract":"In the previous several years, wireless telecommunications technology has seen a significant change. The term “generation” in the context of wireless communication typically describes to a shift in the essential characteristics of the assistance being offered, such as transmission technologies, bit rates, frequency bands, channel frequency bandwidth, and data transfer capacity. One of the most active technological fields that is expanding too quickly is the wireless age. There is a need to implement such technologies that can be integrated and adjusted in order to produce a more advanced and unified system because there are numerous superior technologies available. In this work, we provide a summary of several wireless telecommunications technologies, in particular, 4G, 5G, and 6G Networks, and a detailed comparison between them.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132688610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10073470
Akash Kumar Bhagat, Daxa Vekariya
One of the most dangerous diseases is a brain tumor that may affect children as well as adults both. Brain tumors are responsible for 80-90 percent of all primary Central Nervous System (CNS) cancers. Each year, around 12,000 A brain tumor is discovered in a person. When diagnosed with a malignant brain or CNS tumor, males and the five-year survival rate for women is approximately 33% and 37% respectively. There are several types of brain tumors, including pituitary tumors, malignant tumors, benign tumors and more. Increased life expectancy for patients should be achieved by appropriate therapy, planning, and precise diagnostics. Magnetic resonance imaging is the most effective method for identifying brain tumors (MRI). Scanners produce massive volumes of picture data. The radiologist examines these pictures. Automated classification technologies such as Artificial intelligence (AI) and machine learning (ML) have regularly outperformed manual categorization in terms of accuracy. As a consequence, offering a system that employs Deep Learning Techniques and Algorithms such as ANN (Artificial Neural Networks), CNN (Convolution Neural Networks), TL (Transfer Learning) and GLCM for recognizing and tracking it will benefit doctors worldwide.
{"title":"Computational Intelligence approach to improve the Classification accuracy of Brain Tumor Detection","authors":"Akash Kumar Bhagat, Daxa Vekariya","doi":"10.1109/IC3I56241.2022.10073470","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10073470","url":null,"abstract":"One of the most dangerous diseases is a brain tumor that may affect children as well as adults both. Brain tumors are responsible for 80-90 percent of all primary Central Nervous System (CNS) cancers. Each year, around 12,000 A brain tumor is discovered in a person. When diagnosed with a malignant brain or CNS tumor, males and the five-year survival rate for women is approximately 33% and 37% respectively. There are several types of brain tumors, including pituitary tumors, malignant tumors, benign tumors and more. Increased life expectancy for patients should be achieved by appropriate therapy, planning, and precise diagnostics. Magnetic resonance imaging is the most effective method for identifying brain tumors (MRI). Scanners produce massive volumes of picture data. The radiologist examines these pictures. Automated classification technologies such as Artificial intelligence (AI) and machine learning (ML) have regularly outperformed manual categorization in terms of accuracy. As a consequence, offering a system that employs Deep Learning Techniques and Algorithms such as ANN (Artificial Neural Networks), CNN (Convolution Neural Networks), TL (Transfer Learning) and GLCM for recognizing and tracking it will benefit doctors worldwide.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134500876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.1109/IC3I56241.2022.10072565
Farhat Ali Syed, N. Bargavi, Abhishek Sharma, Abhishek Mishra, Pooja Nagpal, Aparna Srivastava
The development of smart manufacturing systems is being driven by a variety of diverse needs for the dependability of equipment and the prediction of quality. In order to accomplish this objective through the use of machine learning, a wide range of approaches are being investigated. The management and protection of one’s company’s data presents yet another challenging aspect of doing business. In order to cope with fraudulent datasets, machine learning and internet of things technologies were utilized. These technologies were used to protect system transactions and manage a dataset. Because of this, we were able to find solutions to the problems that we had previously discussed. The gathered information was organized and examined with the help of big data techniques. The Internet of Things system was constructed using the Hyperledger Fabric platform, which is a private computer network. In addition, a hybrid prediction strategy was utilized for the defect diagnostic as well as the defect forecasting. The latest machine learning techniques were utilized in order to model the complexity of the environment and estimate the genuine positive ratio of the quality control system. The quality control of the system was evaluated using these pieces of data.
{"title":"Recent Management Trends Involved With the Internet of Things in Indian Automotive Components Manufacturing Industries","authors":"Farhat Ali Syed, N. Bargavi, Abhishek Sharma, Abhishek Mishra, Pooja Nagpal, Aparna Srivastava","doi":"10.1109/IC3I56241.2022.10072565","DOIUrl":"https://doi.org/10.1109/IC3I56241.2022.10072565","url":null,"abstract":"The development of smart manufacturing systems is being driven by a variety of diverse needs for the dependability of equipment and the prediction of quality. In order to accomplish this objective through the use of machine learning, a wide range of approaches are being investigated. The management and protection of one’s company’s data presents yet another challenging aspect of doing business. In order to cope with fraudulent datasets, machine learning and internet of things technologies were utilized. These technologies were used to protect system transactions and manage a dataset. Because of this, we were able to find solutions to the problems that we had previously discussed. The gathered information was organized and examined with the help of big data techniques. The Internet of Things system was constructed using the Hyperledger Fabric platform, which is a private computer network. In addition, a hybrid prediction strategy was utilized for the defect diagnostic as well as the defect forecasting. The latest machine learning techniques were utilized in order to model the complexity of the environment and estimate the genuine positive ratio of the quality control system. The quality control of the system was evaluated using these pieces of data.","PeriodicalId":274660,"journal":{"name":"2022 5th International Conference on Contemporary Computing and Informatics (IC3I)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127462970","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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