2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)最新文献

{"title":"Maneuvering Machine Learning Algorithms to Presage the Attacks of Fusarium oxysporum on Cotton Leaves","authors":"Anurag Dutta, Pijush Kanti Kumar, Ankita De, Padmanavan Kumar, J. Harshith, Yash Soni","doi":"10.1109/DELCON57910.2023.10127436","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127436","url":null,"abstract":"Web technologies have reached unprecedented levels during this time of modernization. Significant and relevant technological stacks like IoT (Internet of Things), ML (Machine Learning), and AI-influenced crawling and cradling (Artificial Intelligence). These categories are beneficial. In this work, we would try to make use of the notion of Machine Learning Algorithms to predict the attack of Fusarium oxysporum on the leaves of the Cotton plant. It’s a type of ascomycete fungi that forms an infrageneric grouping called a section. All of the species, variations, and forms discovered by Wollenweber and Reinking are Elegans. It belongs to the Nectriaceae family. Many strains of the F. oxysporum complex are soil-borne plant pathogens, especially in agricultural settings, although their primary function in native soils may be as benign or even advantageous as plant endophytes or soil saprophytes. Many textile products are made from cotton. Cotton is used in a variety of products besides the textile industry, including gill nets, coffee filters, tarpaulins, cotton paper, and bookbinding. The cotton used to be used to make fire hoses. India and China are the major cotton producers in 2017, with an annual production of approximately 18.53 million tonnes and 17.14 million tonnes, respectively. The vast majority of this output is used by their textile businesses. This contributes a major portion of the economy. To strengthen the same, we can make use of certain prediction techniques that could foresee if the leaves of cotton suffering from the attack by the pathogens, making use of algorithms like ’Support Vector Machine’, ’Random Forest’, ’k - Nearest Neighbours’, and many more. Further, this work would also compare the efficacy of these algorithms in predicting the damage in the Cotton Leaves. All Codes, Data, and Supplementary Material are made available at https://github.com/Anurag-Dutta/Maneuvering-Machine-Learning-Algorithms-to-presage-the-attacks-of-Fusarium-oxysporum-on-Cotton-Leave","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128820058","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}

{"title":"Malware Classification using Deep Learning Techniques","authors":"Bhavya Dawra, Ananya Navneet Chauhan, Ritu Rani, A. Dev, Poonam Bansal, Arun Sharma","doi":"10.1109/DELCON57910.2023.10127303","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127303","url":null,"abstract":"Over 2.8 billion malware attacks struck in first six months of 2022, affecting everything from small businesses to large-scale corporations. The threat landscape has evolved from mischief to severe cybercrimes and espionage. Therefore, a defence for malware detection and classification is required. Portable Executable (PE) files or malware binaries were collected from dataset comprising of 9339 files of 25 different malware families, which were visualized into gray-scale images. On visualizing, we observed that texture and layout of images of same malware families emerged similar. In this paper, we compare the accuracies of our CNN-LSTM model with 3 pre-trained CNN (Convolutional Neural Network) models- ResNet50, VGG19 and Xception and a CNN model, by classifying the malware images into 25 different families. We transform the binary malware files to grayscale images and run them through a deep learning framework for malware detection and classification. The ability of CNNs to learn the features of these images may lead to the timely and accurate detection of malware. Results show that the CNN-LSTM model predicts classes with a training accuracy of 98.04 %.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114557094","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}

{"title":"Evidence based Employee Analytics using Taxonomy Enabled Knowledge Graph","authors":"Nitin Aggarwal, Sandeep Varma, Alina Rizvi, S. Shivam, Apuroop Bhushanam, Rishu Jamaiyar","doi":"10.1109/DELCON57910.2023.10127301","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127301","url":null,"abstract":"Every organization produces a lot of data, analyzing this data, to gain insights and to make amendments is vital for running any sector. There are many documents that can be used to analyze the company’s staffing distribution. One such document is statement of work, this includes the staffing details, the client names, capabilities focused, assets and tools used in that specific project. In this paper we create a pipeline, by taking dataset comprised of all the sow documents generated by an organization and then generating insights from it. We created a utility to identify the expertise of every employee, this information can be used to further predict the employee similarity or to create an expertise-based search engine for employees. These tools can assist the management and staffing authorities at higher level. Hence, we can create a solution and save an enormous amount of human efforts and time.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"2014 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114444109","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}

{"title":"Driver Inattentiveness Detection Techniques for Intelligent Transportation Systems: A Review","authors":"Anwesha Patel, Rishu Chhabra, C. Krishna","doi":"10.1109/DELCON57910.2023.10127504","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127504","url":null,"abstract":"Due to technical advancement, Intelligent Transportation System (ITS) aims to maximize driver safety and security. ITS helps us increase the safety and convenience of the overall transportation system. It aims to incorporate new technology into an already existing traditional transportation system to create a more efficient traffic system that both drivers and others in-charge of managing the traffic can use conveniently. ITS plays a crucial role in development of future smart cities. The core of any transportation system is its drivers. Multiple factors, including distracted driving while using a smartphone, driving while intoxicated, driving while talking on the phone, and many more, have dramatically increased the number of traffic accidents. Driver fatigue is another factor that negatively affects driving attention. Hence, detecting the driver's inattentiveness is an integral part of the ITS as it heavily ensures the safety of both drivers and passengers on the road. In this paper, we present a survey of various driver inattentiveness detection techniques using IoT, Machine Learning, or Deep Learning detection techniques. We initially require input before we can implement any of the detection strategies. Specific wearables, bio-signal sensors, cameras, and smartphone sensors, which include the magnetometer, gyroscope, GPS, and accelerometer, which are embedded into a smartphone, can be used to collect the required input. A comparative analysis has been carried out based on the benefits, drawbacks, and methods used in various techniques. Furthermore, new research directions for driver inattentiveness detection on the road have been discussed.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125669259","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}

{"title":"Study of Relation of Human Factors on Operational Modes and Display Attributes on See-through Displays under Low Visibility Conditions","authors":"Jhulan Kumar, S. Saini, Divya Agrawal, Aman Kataria, V. Karar","doi":"10.1109/DELCON57910.2023.10127384","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127384","url":null,"abstract":"The take-off and landing of an aircraft is a complex and challenging task for the pilot. Most accidents occur in this phase of flight while flying duration is only 4% of total flying time. Implementation of Head-up displays (HUDs) in the cockpit enhances the pilot’s SA and reduces workload. But presenting large amounts of data on HUD with different display attributes degrades pilot performance, leading to an accident in high workload conditions. This study examines the effect of the sudden change in Symbology and presents large data at a time on pilot performance. It also discussed a model to reduce human errors during take-off and landing modes of operation in low visibility conditions. The work discussed in this manuscript is novel as no experiment has been conducted to observe the human factor’s relation to operational modes and display attributes on see-through displays under low visibility conditions.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129220577","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}

{"title":"Control System Engineering through MATLAB-A Case Study on Project based Learning","authors":"Satyabrata Sahoo, V. Mahesh, Bharath Kumar Narukullapati, I. Kasireddy, D. G. Padhan, D. S. Naga Malleswara Rao","doi":"10.1109/DELCON57910.2023.10127243","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127243","url":null,"abstract":"Now a day, teacher centric approach in engineering education is gradually replaced with the new student centric pedagogy through active learning technique. This paper presents an effective and wide spread active learning process i.e. Project Based Learning (PBL) for control system engineering subject of electrical and electronics engineering discipline through MATLAB. Presently it is very difficult to attract the student’s attention towards the core theory subject. Here through Project Based Learning with MATLAB implementation we want to enhance the students’ performance overall. In this learning methodology, students are able to learn by implementing and interacting with each other. The objective of this paper is to enable the students for team work, problem solving ability, and leadership quality. Finally the student’s outcome of this project based learning is implemented through student’s feedback and internal assessment through presentation and mid examination.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134335405","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}

{"title":"Machine Learning Techniques for Real-Time Emotion Detection from Facial Expressions","authors":"Akshita Sharma, Vriddhi Bajaj, Jatin Arora","doi":"10.1109/DELCON57910.2023.10127369","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127369","url":null,"abstract":"Facial expressions recognition by emotion is a crucial component in many applications. This paper covers the recent trends in human emotion detection. An overview of various facial emotion recognition and its applications are presented. In the literature review, major machine-learning techniques used for facial emotion identification have been explored. Machine learning approaches are compared on the basis of their advantages, disadvantages, and their accuracy. Theoretical analysis of existing approaches shows that the algorithm providing the maximum accuracy should be used for facial emotion recognition. The existing approaches are also suffered from some challenges and those challenges should be addressed and considered for accurately predicting the users' emotional state. The application of emotion detection is also very vast and a few of the major applications are also discussed. Finally, a brief analysis of existing Machine learning approaches and their conclusion is given.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125192457","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}

{"title":"DC, RF and Noise Characterization of AlGaN/GaN HEMT","authors":"Shivansh Awasthi, Prof Vikas Kumar, P. Shrivastava, Pragyey Kumar Kaushik, Ankur Gupta","doi":"10.1109/DELCON57910.2023.10127539","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127539","url":null,"abstract":"In this work, DC and RF characterization of AlGaN/GaN HEMT on SiC substrate was carried out. It was found that the maximum drain on current (I<inf>ON</inf>) was 0.12 A/mm at a drain to source (V<inf>DS</inf>) bias of 9 V and at the gate to source bias (V<inf>GS</inf>) of 2 V. The maximum transconductance (g<inf>m</inf>) of the device was calculated to be 0.02 S. S21 parameter of the device was measured from 100 MHz to 50 GHz. The measurement of S parameters was done by the variation of V<inf>GS</inf> and V<inf>DS</inf>. The Noise Figure measurements of the device were done by varying the frequency from 2 GHz to 10 GHz using a Noise Figure Analyzer.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115452286","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}

{"title":"Design of a Low-power 4th order Composite Folded Flipped Source Follower Filter for Biological Applications","authors":"Diksha Thakur, K. Sharma","doi":"10.1109/DELCON57910.2023.10127389","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127389","url":null,"abstract":"The implementation of the biological healthcare system can greatly benefit from the use of a low-pass filter (LPF) architecture, which is considered to be a highly important architecture. This article, presents a composite folded flipped source follower (FFSF) LPF architecture designed for biomedical signals. The proposed LPF works in the sub-threshold region to attain low-noise and low-power operation. The composite FFSF filter design has been simulated in 0.18 µm complementary metal-oxide semiconductor (CMOS) technology. The proposed architecture delivers -4.2 dB of gain, 100 Hz of bandwidth, 0.79 nW of power, 51 µVrms of input-referred noise and figure of merit of 1.95×10-14 J. The proposed LPF architecture can be used to attain excellent power efficiency in upcoming low-voltage biomedical healthcare systems.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116020934","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}

{"title":"Crop Recommendation Application using Ensemble Classifiers","authors":"Belide Kusumasri, S. V, Sanjay Satyavada, G. Kiran","doi":"10.1109/DELCON57910.2023.10127576","DOIUrl":"https://doi.org/10.1109/DELCON57910.2023.10127576","url":null,"abstract":"The practice of raising cattle and plants is known as agriculture. It necessitates the preparation of plant and animal products and distribution of them to markets for human consumption. Agriculture plays a major role in the world’s food and textile production. Wool, cotton, and leather are products of agriculture. Paper and lumber for building are additional products of agriculture. The agricultural methods used and the commodities produced can vary between different locations. The challenge for farmers is make to right choice of the crop in light of the current weather and soil nutrient levels. The project’s primary goal is to develop a reliable model that provides precise predictions of crop sustainability for a specific soil type and set of weather circumstances. In an attempt to eliminate loss for the farmers, the model provides a model that recommends the best local crop. The following factors are taken into account when building the model: nitrogen, potassium, phosphorus, temperature, air humidity, soil pH, and annual rainfall. Considering the data gathered from previous years, the model assists in choosing the type of crop that must be grown. The model is trained using an ensemble learning strategy that incorporates Gaussian Naive Bayes, Logistic Regression, and Support Vector Machine (SVM), which has acquired an accuracy of 99.31 for the model. These algorithms are evaluated at various levels and used as comparative research and analysis to support the task.","PeriodicalId":193577,"journal":{"name":"2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129982403","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}