Pub Date : 2023-02-18DOI: 10.52783/cienceng.v11i1.282
Kamel Alikhan Siddiqui, K. Fatima, Ali Hasan Khan, Mr. Sibghatullah
This abstract describes a machine learning model for predicting the prognosis of cancer patients with leukemia. The model is based on analyzing patient data such as age, gender, diagnosis, lab results, treatment history, and prior hospitalizations. The model employs supervised learning techniques such as random forests and gradient boosting to generate predictions of survival probabilities. Additionally, the model uses feature engineering to identify important features and reduce noise in the data. The model is evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics. The results indicate that the model has good predictive accuracy and can be used to identify high-risk patients and guide clinical decisions. The goal of this project is to develop a machine learning model for the diagnosis and prognosis of leukemia in Indian patients. The model will use a progressive analysis of patient data to identify the characteristics associated with leukemia, including genetic markers, environmental exposures, lifestyle factors, and demographic information. Using this information, the model will be used to accurately predict the risk of developing leukemia in Indian patients. Additionally, the model will be used to identify the most effective treatments for those diagnosed with leukemia and to monitor the disease progression. Finally, the model will be evaluated for its accuracy and effectiveness in a clinical setting.
{"title":"Progressive Analysis and Predictions of Leukemia (Cancer) Patients on a Machine Learning Model- The APPOLLO Hospitals Accredited","authors":"Kamel Alikhan Siddiqui, K. Fatima, Ali Hasan Khan, Mr. Sibghatullah","doi":"10.52783/cienceng.v11i1.282","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.282","url":null,"abstract":"This abstract describes a machine learning model for predicting the prognosis of cancer patients with leukemia. The model is based on analyzing patient data such as age, gender, diagnosis, lab results, treatment history, and prior hospitalizations. The model employs supervised learning techniques such as random forests and gradient boosting to generate predictions of survival probabilities. Additionally, the model uses feature engineering to identify important features and reduce noise in the data. The model is evaluated using the area under the receiver operating characteristic curve (AUC) and other metrics. The results indicate that the model has good predictive accuracy and can be used to identify high-risk patients and guide clinical decisions. The goal of this project is to develop a machine learning model for the diagnosis and prognosis of leukemia in Indian patients. The model will use a progressive analysis of patient data to identify the characteristics associated with leukemia, including genetic markers, environmental exposures, lifestyle factors, and demographic information. Using this information, the model will be used to accurately predict the risk of developing leukemia in Indian patients. Additionally, the model will be used to identify the most effective treatments for those diagnosed with leukemia and to monitor the disease progression. Finally, the model will be evaluated for its accuracy and effectiveness in a clinical setting.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131317400","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.187
Kolipaka Srinivas, Manisha Dixit, Mamidala Kondal
The period from starting of the 11th to early part of the 14th century saw the emergence of the Kakatiyas dynasty that opened a fresh chapter in the annals of medieval South India. During the 12th and 13th centuries a number of developments contributed to a steady increase in over – all economic activity in Kakatiya kingdom. In fact, agriculture was the main occupation during the Kakatiya period, yet, a large number of industries and handicrafts flourished both in rural and urban areas. Textile industry, the leather industry Diamond Industry, Iron Industry etc. played a prominent role in the economic sphere of the Kakatiya kingdom. Among the industries of the period, textile, metal, jewellery and leather industries were the most flourished.
{"title":"Growth of Industry during Kakatiyas Period (1000 - 1323 AD)","authors":"Kolipaka Srinivas, Manisha Dixit, Mamidala Kondal ","doi":"10.52783/cienceng.v11i1.187","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.187","url":null,"abstract":"The period from starting of the 11th to early part of the 14th century saw the emergence of the Kakatiyas dynasty that opened a fresh chapter in the annals of medieval South India. During the 12th and 13th centuries a number of developments contributed to a steady increase in over – all economic activity in Kakatiya kingdom. In fact, agriculture was the main occupation during the Kakatiya period, yet, a large number of industries and handicrafts flourished both in rural and urban areas. Textile industry, the leather industry Diamond Industry, Iron Industry etc. played a prominent role in the economic sphere of the Kakatiya kingdom. Among the industries of the period, textile, metal, jewellery and leather industries were the most flourished.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125527673","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.389
Prof. A. P. Gaigol, Dr. V. S. Wadne, Prof. S. R. Bhandari, Dr. R. S. Deshpande, Dr. T. R. Sangole
“Data” is the trending word since last few decades. When it comes to any type of data its security is important. Relating the same, maintaining the medical health data is also very significant. When the medical health data is stored or retrieved from the internet is called as Electronic Health Record (EHR). Many times an EHR can be accessed in an unethical way when it is maintaining through conventional approach. Misuse of the same data can lead to many unavoidable problems. Use of Blockchain Technology plays pivotal role in dealing with such problems. An already proven and completely functional Blockchain Technology based Crypto-currencies are the best examples of how efficient and capable Blockchain Technology is. Peer to peer network is used in Blockchain Technology to store the data. Key features of Blockchain-based network are Public Distributed Ledger or non-centralization, Hashing Concept, PoW which makes Blockchain Technology one of most robust technology and highly recommended to use to store and retrieve the data safely. Goal of stated work is to study various benefits of Blockchain Technlogy, how an Electronic Health Record (EHR) can be access in a predefined way where the medical health data/an Electronic Health Record (EHR) will remain immutable throughout its existence.
{"title":"A Systematic Review on Medical Health Data Management Using Blockchain Technology","authors":"Prof. A. P. Gaigol, Dr. V. S. Wadne, Prof. S. R. Bhandari, Dr. R. S. Deshpande, Dr. T. R. Sangole","doi":"10.52783/cienceng.v11i1.389","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.389","url":null,"abstract":"“Data” is the trending word since last few decades. When it comes to any type of data its security is important. Relating the same, maintaining the medical health data is also very significant. When the medical health data is stored or retrieved from the internet is called as Electronic Health Record (EHR). Many times an EHR can be accessed in an unethical way when it is maintaining through conventional approach. Misuse of the same data can lead to many unavoidable problems. Use of Blockchain Technology plays pivotal role in dealing with such problems. An already proven and completely functional Blockchain Technology based Crypto-currencies are the best examples of how efficient and capable Blockchain Technology is. Peer to peer network is used in Blockchain Technology to store the data. Key features of Blockchain-based network are Public Distributed Ledger or non-centralization, Hashing Concept, PoW which makes Blockchain Technology one of most robust technology and highly recommended to use to store and retrieve the data safely. Goal of stated work is to study various benefits of Blockchain Technlogy, how an Electronic Health Record (EHR) can be access in a predefined way where the medical health data/an Electronic Health Record (EHR) will remain immutable throughout its existence. ","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126515706","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.303
Shyamali Banerjee, Sanju Xavier
Mass Media, especially print media since its inception, if we look back, during Industrial Revolution and its concomitant Mass Production, has always been dwindled in between two binary epithets: Political Allegiance and Media Democracy. The duality lies in the fact that when the media has to work at the behest of political parties, ruling parties at the helm of body politique of the state, for its survival and sustenance, of course the other epithet which is ‘Media democracy’ or democratic rights of the media is questionable. However, the compulsion to show political allegiance to the ruling party at the governance is never accepted by the media without resistance and therefore any attempt of imposing censorship on media has been vehemently protested. Examples can be multiplied by drawing instances from history. Political censorship in Indian Media has never been such a recurrent phenomenon as it has become in the recent past. Also, ‘political allegiance’ when turned to propaganda tactics is more vulnerable than that of securing unflinching faith in the government. The number of defamation cases clamped against media houses, reporters and photo journalists have gone up from 13 to 167 as per a recent survey made by an independent journalist. This research study explores the fact that digital propaganda and political allegiance has been used by the ruling government to ensure a mass consensus over a number of socio-political issues, starting from Farmers’ Bill to Abrogation of Article 370 of Indian Constitution or for that matter Warfare Propaganda. The methodology that is used for this study is content analysis of media representation of information and speech analysis of PM Narendra Modi using a unique coding method. The samples are chosen on the basis of how media has represented announcement of different developmental schemes by the Modi Government and what they represent in reality. Most of the news reports that are analysed in this study between 2016-2022 are sourced from different newspapers and internet sources.
{"title":"Mass Media, Propaganda & Ethics: An Overview of a New Upsurge of Fascism in India","authors":"Shyamali Banerjee, Sanju Xavier","doi":"10.52783/cienceng.v11i1.303","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.303","url":null,"abstract":"Mass Media, especially print media since its inception, if we look back, during Industrial Revolution and its concomitant Mass Production, has always been dwindled in between two binary epithets: Political Allegiance and Media Democracy. The duality lies in the fact that when the media has to work at the behest of political parties, ruling parties at the helm of body politique of the state, for its survival and sustenance, of course the other epithet which is ‘Media democracy’ or democratic rights of the media is questionable. However, the compulsion to show political allegiance to the ruling party at the governance is never accepted by the media without resistance and therefore any attempt of imposing censorship on media has been vehemently protested. Examples can be multiplied by drawing instances from history. Political censorship in Indian Media has never been such a recurrent phenomenon as it has become in the recent past. Also, ‘political allegiance’ when turned to propaganda tactics is more vulnerable than that of securing unflinching faith in the government. The number of defamation cases clamped against media houses, reporters and photo journalists have gone up from 13 to 167 as per a recent survey made by an independent journalist. This research study explores the fact that digital propaganda and political allegiance has been used by the ruling government to ensure a mass consensus over a number of socio-political issues, starting from Farmers’ Bill to Abrogation of Article 370 of Indian Constitution or for that matter Warfare Propaganda. The methodology that is used for this study is content analysis of media representation of information and speech analysis of PM Narendra Modi using a unique coding method. The samples are chosen on the basis of how media has represented announcement of different developmental schemes by the Modi Government and what they represent in reality. Most of the news reports that are analysed in this study between 2016-2022 are sourced from different newspapers and internet sources.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126592879","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.222
M. Banu
Plant pathogenic fungi belong to the Kingdom Fungi comprising of over 100,000 registered species grouped into about 4,300 genera. Many of these fungi infect a variety of cereals particularly stored condition. A study was conducted to evaluate the occurrence of Plant pathogenic fungi associated with stored Sorghum vulgare grains. The fungal was subjected to the fungal load for exact identification using the molecular biology technique. The fungal isolates were isolated from the collected grains. Results revealed that Genus Aspergillus fumigatus was the most frequent isolate which are known to produce Aflatoxins. A combination of many different isolates was present on the grains. The molecular identification method using Polymerase Chain Reaction (PCR) helped in the identification of fungi at the species level with precision and in the least possible time. In conclusion, the isolated species were identified morphologically as Aspergillus fumigatus. The fungal samples were then identified through molecular techniques by DNA sequencing which was identified as Aspergillus fumigatus. The molecular techniques used in this study, has added great benefits to the process of distinguishing between similar species of fungi in comparison with the classical techniques. Furthermore, there is a need for having a National Level, Germplasm collection centre and detailed database of all the naturally occurring post-harvest Plant pathogenic fungi for easy intervention and timely overcoming of situations in case of any emergency.
{"title":"Identification of Aspergillus Fumigatus using Molecular Techniques","authors":"M. Banu","doi":"10.52783/cienceng.v11i1.222","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.222","url":null,"abstract":"Plant pathogenic fungi belong to the Kingdom Fungi comprising of over 100,000 registered species grouped into about 4,300 genera. Many of these fungi infect a variety of cereals particularly stored condition. A study was conducted to evaluate the occurrence of Plant pathogenic fungi associated with stored Sorghum vulgare grains. The fungal was subjected to the fungal load for exact identification using the molecular biology technique. The fungal isolates were isolated from the collected grains. Results revealed that Genus Aspergillus fumigatus was the most frequent isolate which are known to produce Aflatoxins. A combination of many different isolates was present on the grains. The molecular identification method using Polymerase Chain Reaction (PCR) helped in the identification of fungi at the species level with precision and in the least possible time. In conclusion, the isolated species were identified morphologically as Aspergillus fumigatus. The fungal samples were then identified through molecular techniques by DNA sequencing which was identified as Aspergillus fumigatus. The molecular techniques used in this study, has added great benefits to the process of distinguishing between similar species of fungi in comparison with the classical techniques. Furthermore, there is a need for having a National Level, Germplasm collection centre and detailed database of all the naturally occurring post-harvest Plant pathogenic fungi for easy intervention and timely overcoming of situations in case of any emergency.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120959990","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.176
Dr. G. V. Uma
Cloud storage is a virtual setting that includes an amazing amount of processing power. The resources are in a state where they can serve users. Cloud data centres are the locations where all of this computing is maintained. In order to produce high-performance computing, the data centre houses a sizable number of powerful computers and servers connected to one another. The physical computer resources housed in the cloud data centre are accessible to the user via virtualization. To provide a convergent encryption method that will enhance data security while reducing deduplication's impact on security, encryption, and decryption times. Convergent encryption is used to prevent duplicating data, but the key used to encrypt data for the first time is kept up to date and distributed to all users who have same or closely related content of data to upload to the cloud storage. In an open cloud environment, it's crucial to distribute the key to all users. When using cloud storage services, users encountered numerous issues.
{"title":"Enhanced Symmetric Convergent Encryption for Secured Data Deduplication in Cloud","authors":"Dr. G. V. Uma","doi":"10.52783/cienceng.v11i1.176","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.176","url":null,"abstract":"Cloud storage is a virtual setting that includes an amazing amount of processing power. The resources are in a state where they can serve users. Cloud data centres are the locations where all of this computing is maintained. In order to produce high-performance computing, the data centre houses a sizable number of powerful computers and servers connected to one another. The physical computer resources housed in the cloud data centre are accessible to the user via virtualization. To provide a convergent encryption method that will enhance data security while reducing deduplication's impact on security, encryption, and decryption times. Convergent encryption is used to prevent duplicating data, but the key used to encrypt data for the first time is kept up to date and distributed to all users who have same or closely related content of data to upload to the cloud storage. In an open cloud environment, it's crucial to distribute the key to all users. When using cloud storage services, users encountered numerous issues. ","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116772031","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}
The scarcity of clean water resources around the globe has generated a need for their optimum utilization Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing are bridging the gaps between the cyber and physical world. IoT based smart irrigation systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture soil temperature, and environmental conditions along with the weather forecast data from the Internet. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information-insights based on the analysis of sensors data and weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.
{"title":"Smart Irrigation and Crop Health Prediction","authors":"Md Owais Quadri, Mahmood farmaan, Md Anas, Praveen Ku Reshma Ghani","doi":"10.52783/cienceng.v11i1.314","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.314","url":null,"abstract":"The scarcity of clean water resources around the globe has generated a need for their optimum utilization Internet of Things (IoT) solutions, based on the application specific sensors’ data acquisition and intelligent processing are bridging the gaps between the cyber and physical world. IoT based smart irrigation systems can help in achieving optimum water-resource utilization in the precision farming landscape. This paper presents an open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture soil temperature, and environmental conditions along with the weather forecast data from the Internet. The intelligence of the proposed system is based on a smart algorithm, which considers sensed data along with the weather forecast parameters like precipitation, air temperature, humidity, and UV for the near future. The complete system has been developed and deployed on a pilot scale, where the sensor node data is wirelessly collected over the cloud using web-services and a web-based information visualization and decision support system provides the real-time information-insights based on the analysis of sensors data and weather forecast data. The paper describes the system and discusses in detail the information processing results of three weeks data based on the proposed algorithm. The system is fully functional and the prediction results are very encouraging.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122751900","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.120
Rohit Khedkar et al.
Now a days cyber crime growing and has a big effect everywhere globally. ethical hackers are normally involved in identifying flaws and recommending mitigation measures. the cyber safety international, there's a pressing need for the improvement of powerful techniques. Because of the effectiveness of machine learning in cyber security issues, machine learning for cyber security has recently become a hot topic. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. Although ML cannot fully automate a cyber-security system, it can identify cyber-security threats more efficiently than other software-oriented approaches, relieving security analysts of their burden. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates, and cheaper computing and transmission costs. Our key goal is that the challenge of detecting attacks is fundamentally different from those of these other applications, making it substantially more difficult for the intrusion detection community to apply machine learning effectively. In this study, the CPS is modeled as a network of agents that move in unison with one another, with one agent acting as a leader and commanding the other agents. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack. The use of robust control algorithms in the network to isolate the misbehaving agent in the leader-follower mechanism has been researched. Following the attack detection phase with a deep neural network, the control system uses the reputation algorithm to isolate the misbehaving agent in the presented control method. Experiment results show that deep learning algorithms can detect attacks more effectively than traditional methods, making cyber security simpler, more proactive, and less expensive and more expensive.
{"title":"Detection of Cyber Attacks and Network Attacks Using Machine Learning Algorithms","authors":"Rohit Khedkar et al.","doi":"10.52783/cienceng.v11i1.120","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.120","url":null,"abstract":"Now a days cyber crime growing and has a big effect everywhere globally. ethical hackers are normally involved in identifying flaws and recommending mitigation measures. the cyber safety international, there's a pressing need for the improvement of powerful techniques. Because of the effectiveness of machine learning in cyber security issues, machine learning for cyber security has recently become a hot topic. In cyber security, machine learning approaches have been utilized to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. Although ML cannot fully automate a cyber-security system, it can identify cyber-security threats more efficiently than other software-oriented approaches, relieving security analysts of their burden. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates, and cheaper computing and transmission costs. Our key goal is that the challenge of detecting attacks is fundamentally different from those of these other applications, making it substantially more difficult for the intrusion detection community to apply machine learning effectively. In this study, the CPS is modeled as a network of agents that move in unison with one another, with one agent acting as a leader and commanding the other agents. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack. The use of robust control algorithms in the network to isolate the misbehaving agent in the leader-follower mechanism has been researched. Following the attack detection phase with a deep neural network, the control system uses the reputation algorithm to isolate the misbehaving agent in the presented control method. Experiment results show that deep learning algorithms can detect attacks more effectively than traditional methods, making cyber security simpler, more proactive, and less expensive and more expensive.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121853704","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.128
Prof. Ganga Yadawad, Yash Zope, Niraj Ohol, Tejas
Brain tumor can be divided into two types: benign and malignant. Brain tumors are the most common and aggressive disease, which in its highest stage leads to a very short life expectancy. Thus, treatment planning is a key phase for improving patients' quality of life. However, it has some limitations (i.e., accurate quantitative measurements are provided for a limited number of frames). A reliable and automatic classification system is therefore necessary to prevent human mortality. Block chain technology is an emerging field of science that plays a major role in every application field of science, including education, banking, and healthcare. . In healthcare, most health problems arise due to their neglect of correct diagnosis by the doctor and ignorance of the symptom by the patients. The most common disease today is called cancer. A brain tumor usually has symptoms such as frequent headaches, unexplained nausea or vomiting. Sometimes he may also have blurred vision, double vision and sometimes loss of peripheral vision. In this project we will diagnose tumor using Blockchain strategy.
{"title":"Brain Tumor Detection Using Block Chain","authors":"Prof. Ganga Yadawad, Yash Zope, Niraj Ohol, Tejas ","doi":"10.52783/cienceng.v11i1.128","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.128","url":null,"abstract":"Brain tumor can be divided into two types: benign and malignant. Brain tumors are the most common and aggressive disease, which in its highest stage leads to a very short life expectancy. Thus, treatment planning is a key phase for improving patients' quality of life. However, it has some limitations (i.e., accurate quantitative measurements are provided for a limited number of frames). A reliable and automatic classification system is therefore necessary to prevent human mortality. Block chain technology is an emerging field of science that plays a major role in every application field of science, including education, banking, and healthcare. . In healthcare, most health problems arise due to their neglect of correct diagnosis by the doctor and ignorance of the symptom by the patients. The most common disease today is called cancer. A brain tumor usually has symptoms such as frequent headaches, unexplained nausea or vomiting. Sometimes he may also have blurred vision, double vision and sometimes loss of peripheral vision. In this project we will diagnose tumor using Blockchain strategy.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128040015","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 : 2023-02-18DOI: 10.52783/cienceng.v11i1.278
Dr. Sweta Rani
Non-store retailing is the selling of goods and services without ever establishing a physical store, as opposed to the traditional way – the very foundation of retail. In an ever-changing market of globalization era, where need for better efficiency is crucial, non-store retailing is patronized to time conscious consumers or compulsive buyers. Non-store retailing, that includes e-commerce, direct selling, telemarketing, etc., has evolved throughout the years and continues to offer consumers the convenience of buying 24 hours a day, 7 days a week and delivery at the location and time of their choice. Taking a leap in popularity in the pandemic era, the mere expectation that it will grow at a staggering rate over the next few years is no rocket science either. Although here the potential customer base can be limitless, it’s not devoid of its own demerits and negative aspects, of which unhealthy competition leading to customers having to pay twice-thrice the money they should pay, is the biggest. This study aims to thoroughly go through both the positive and negative aspects of non-store retailing, to seek out and strike a balance between store based retailing and non-store retailing, so that both continue to develop, evolve and thrive in a healthy manner and contribute to the nation’s GDP and towards customer satisfaction as well as retention.
{"title":"Impact of Non- Store Retailing in Indian Economy","authors":"Dr. Sweta Rani","doi":"10.52783/cienceng.v11i1.278","DOIUrl":"https://doi.org/10.52783/cienceng.v11i1.278","url":null,"abstract":"Non-store retailing is the selling of goods and services without ever establishing a physical store, as opposed to the traditional way – the very foundation of retail. In an ever-changing market of globalization era, where need for better efficiency is crucial, non-store retailing is patronized to time conscious consumers or compulsive buyers. Non-store retailing, that includes e-commerce, direct selling, telemarketing, etc., has evolved throughout the years and continues to offer consumers the convenience of buying 24 hours a day, 7 days a week and delivery at the location and time of their choice. Taking a leap in popularity in the pandemic era, the mere expectation that it will grow at a staggering rate over the next few years is no rocket science either. Although here the potential customer base can be limitless, it’s not devoid of its own demerits and negative aspects, of which unhealthy competition leading to customers having to pay twice-thrice the money they should pay, is the biggest. This study aims to thoroughly go through both the positive and negative aspects of non-store retailing, to seek out and strike a balance between store based retailing and non-store retailing, so that both continue to develop, evolve and thrive in a healthy manner and contribute to the nation’s GDP and towards customer satisfaction as well as retention.","PeriodicalId":214525,"journal":{"name":"Proceeding International Conference on Science and Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133435937","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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