2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)最新文献

{"title":"Securing the Cyber Supply Chain: A Risk-based Approach to Threat Assessment and Mitigation","authors":"L. Prathyusha, Vadlamudi Jhansi, A. Madhuri, E. Jyothi, Sampreeth Chowdary, S. Sindhura","doi":"10.1109/ICESC57686.2023.10193255","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193255","url":null,"abstract":"The system of Cyber Supply Chain (CSC) is characterized by its complexity, consisting of several subsystems, each responsible for a distinct set of responsibilities. Securing the supply chain presents a challenge due to the presence of vulnerabilities and threats throughout the system that has the potential to be taken advantage of at any time, considering that any component of the system is susceptible to such attacks. As a result, supply chain security is difficult to achieve. This has the potential to create a significant interruption to the overall continuity of the company. Therefore, it is of the utmost importance to identify the hazards and make educated guesses about their likely outcomes so that organizations can take the appropriate precautions to ensure the safety of their supply chains. By leveraging a range of factors, such as the expertise and incentives of threat actors, Tactics, Techniques, and Procedures (TT and P), as well as Indicators of Compromise (IoC), the analysis of Cyber Threat Intelligence (CTI) offers valuable information on both identified ansignd unidentified cybersecurity threats. In order to increase the safety of the cyber supply chain, the purpose of this article is to investigate and speculate on potential dangers. The CTI and Machine Learning (ML) approaches have been employed by us in order to study and forecast the risks based on the CTI attributes. This makes it possible to detect the inherent CSC vulnerabilities, which enables suitable control. To enhance the overall security of computer systems, it is imperative to implement specific actions, including the collection of CTI data and the adoption of various machine learning techniques. These techniques encompass Logistic Regression (LG), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Cat Boost, and Gradient Boost, which are employed in analyzing the Microsoft Malware Prediction dataset to create predictive analytics. This is done in order to illustrate that the technique can be applied to a variety of situations.As input parameters, the experiment takes into account the assault and the TTP, while as output parameters, it takes into account vulnerabilities and indicators of compromise (IoC). According to the findings of the investigation, the most foreseen dangers in CSC are spyware and ransomware, as well as spear phishing. When it came to forecasting vulnerabilities, the predictive models that were produced using the Random Forest algorithm obtained the best accuracy rate of 91%, while the predictive models that were developed using the LR method earned the highest accuracy rate of 86%. In light of the results, the paper strongly advise putting appropriate controls into place in order to combat these dangers. The paper strongly recommend that the ML predicate model make use of CTI data in order to improve the CSC’s cyber security on the whole.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125293284","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":"Different Respiratory Lung Sounds Prediction using Deep Learning","authors":"Rajeshree Parsingbhai Vasava, Hetal A. Joshiara","doi":"10.1109/ICESC57686.2023.10193040","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193040","url":null,"abstract":"The sounds produced by the lungs when breathing might provide important information to physicians. Based on the findings, a deep learning-based approach is recommended for the prediction of breathing-related lung sounds. The Proposed model was trained in lung sounds collected from people suffering from a broad variety of respiratory conditions. The research improves classifying lung sounds, by audio to image spectrogram features is taken and used to train a deep convolutional neural network. The proposed technique accurately predicts many different types of respiratory lung sounds, demonstrating the promise of deep learning in this domain. This research results have important implications for the development of automated diagnostic tools that might help doctors make correct diagnoses of respiratory disorders more quickly and accurately.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130118758","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":"Development of Recommender Systems for Better Services and Products using Data Science","authors":"S. Padmapriya, Thamizhamuthu R, S. Jagadeesh, D. M. Kalai Selvi, M. Shariff","doi":"10.1109/ICESC57686.2023.10193328","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193328","url":null,"abstract":"Online social networking and e-commerce are becoming increasingly popular. Recommender Systems (RS) let users find relevant information from several possibilities. Internet applications currently need RS. This technology uses huge data to provide customized suggestions to improve customer happiness. Concerns and ideas help customers choose items. Sentiment Analysis (SA) may increase RS recommendation accuracy by improving user behaviour, views, and responses. RS solves information overload in information retrieval, but data sparsity remains a big problem. SA is notable for reading text and expressing user preferences. It helps E-Commerce to monitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recommendation approach to increase RS accuracy and correctness. The hybrid approach beats standard models in several assessment criteria. Modern retailing businesses’ e-commerce operations are impossible without RSs. The content-based and context-aware techniques are hybridized for providing promising results. Content-based approaches connect consumers to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithms propose customized items with minimal use history. These systems use case-based recommendations or limitations to make recommendations. Finally, ensemble recommender systems combine data source prediction power.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131144871","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":"Sensor and Microcontroller based 360 Degree Car Assistance System","authors":"J. J. Elavarasi, Abilash Franklin, S. Nagarajan","doi":"10.1109/ICESC57686.2023.10193445","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193445","url":null,"abstract":"The 360 Degree Car Assistance System automatically applies emergency brakes when necessary and regulates a vehicle's speed based on the proximity of an object. Damages that may be made while driving will be avoided by this approach. The car's undercarriage is vulnerable to hit from the ground and vice versa, and the region is also hidden from the driver's view. Thus, in order to avoid this, a survey was conducted, and this project was built in such a manner that it would identify the item and alert the driver if there were any impediments beneath the car that are close to the Chassis and the Wheels. A prototype of the car is created and used in this project. The system uses an Ultrasonic Sensor and AT Mega to detect obstructions, and it allows the execution of emergency braking in accordance with pre-burnt code in the AT Mega. Consequently, a cost-effective automatic collision avoidance system is suggested. In order to avoid and prevent accidents all automobile models can use the 360 Degree Car Assistance System that has been designed.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132531430","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":"Recommendation of Contemporary Fashion Trends via AI-Enhanced Multimodal Search Engine and Blockchain Integration","authors":"Santosh Gore, Gagandeep Dhindsa, Sujata Gore, N. Jagtap, Umesh Nanavare","doi":"10.1109/ICESC57686.2023.10193587","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193587","url":null,"abstract":"The evolution of advanced technology paved way for various automations and innovations in the manufacturing and production sectors in clothing industry. The fashion is a form of expressing one expression in the form of color and designs. The interest of clothing for a particular model in a particular intervals of time are termed as fashion trend. It is formulated and evaluated through the sentiment analysis. This helps to extract the people’s interest towards the particular design and theme. The innovation of fashion trend is increasing rapidly due to the advancement of digital environment. This helps to access the information from anywhere through online platform. This is implemented through augmented and virtual reality environments through artificial intelligence. The influence of clothing and its brand are enhanced through the social media interconnections. Proposed model helps to easily access one’s interest through the search engine modalities. The entire functioning and transactions in the online platform are done through the block chain technology. This helps to obtain safe and secure transactions and storing of information without any external consequences.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132590594","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":"A New Topology DC-DC Bidirectional Converter for Hybrid Energy Integration System","authors":"B. Sridhar, V. Mokshita, S. Padmanabhan, A. Vidyasree, R. Santhosh Kumar, B. Marisekar","doi":"10.1109/ICESC57686.2023.10193360","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193360","url":null,"abstract":"The preponderance of four-port converters at present accomplish power flow through the battery port, which is positioned on the low-voltage side. In other words, the battery receives energy from input sources and disbands it to the load. The system is helpless to manage it at a power level when there is this sort of power flow. An innovative multiport bidirectional DC-DC converter is proposed. When compared to the current converters, this conception requires the fewest devices due to the usage of a single-tapped power conversion mechanism rather than the center-tapped conversion already in use. The converter is indeed a wiser choice for DC micro grid applications due to its dual-directional power flow. First, the converter’s operating mechanism is assessed, subsequently; the converter is developed to fulfill the specifications for gentle switching and power rating. The research plan illustrates the incorporation of both magnetic coupling and DC-link. The new approaches unite numerous input sources. A multiport bidirectional DC-DC converter with single-tapped conversion is proposed. This configuration does not necessitate any further switches. The resultant converter features a straight forward topology and the fewest possible power devices.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132671552","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":"FSM based Coordinated Four-Way Intersection Specific Automated Traffic Light Controller","authors":"M. Sakthimohan, G. Elizabeth Rani, T. Radhika, P. Sneha, M. Gowthaman, S. Melvin","doi":"10.1109/ICESC57686.2023.10193513","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193513","url":null,"abstract":"Efficient traffic management is crucial for maintaining smooth and safe flow at busy intersections. The FSM controller employs a modular and deterministic approach to traffic signal control. It utilizes a set of well-defined states and transitions to adaptively respond to real-time traffic conditions. The system incorporates various sensors, including vehicle detectors, pedestrian sensors, and traffic flow monitors, to gather input data for accurate decision-making. The FSM model is designed to accommodate different traffic patterns and prioritize the movement of vehicles and pedestrians. It dynamically adjusts signal timings based on the detected demand, considering factors such as vehicle queues, waiting times, and pedestrian crossings. By intelligently coordinating traffic movements, the controller aims to minimize delays, maximize throughput, and improve overall intersection efficiency. To validate the effectiveness of the proposed controller, extensive simulations and comparative analyses are conducted using realistic traffic scenarios. Performance metrics, including average delay, queue length, and throughput, are measured and compared against traditional fixed-time signal control systems. The results demonstrate that the FSM-based automatic traffic light controller offers significant advantages in terms of responsiveness, adaptability, and efficiency. It effectively mitigates congestion, reduces travel times, and enhances the safety of both vehicles and pedestrians at the intersection.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132730776","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":"DDoS Detection using Multilayer Perceptron","authors":"D. Mary, Subaja Christo, Sai Varun Nuna, Ms.J. Josepha, Mikhale George","doi":"10.1109/ICESC57686.2023.10193406","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193406","url":null,"abstract":"In recent years, distributed denial of service (DDoS) attacks have grown to be a serious threat to network security, severely disrupting internet services and enterprises. Due to the dynamic and evolving nature of these attacks, detecting and mitigating them has become a difficult task. By examining the network traffic data, machine learning algorithms like Multilayer Perceptrons (MLPs) have demonstrated the potential in identifying DDoS attacks. This research study investigates the application of MLPs for DDoS detection and assess the model’s performance on a real-world dataset. We also examine how various hyperparameters affect the model’s performance and suggest an optimization technique to increase its accuracy. The outcomes of our research show that MLPs have the potential to be an effective tool for detecting and countering DDoS attacks, in addition to offering suggestions for future network security research","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133242630","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":"Optimization of Industrial Power Systems through Automated Humidity and Temperature Surveillance based on PLC and SCADA","authors":"L. Manoj Kumar Yadav, K. Dhineshkumar, R. Mythili, B. Arthi","doi":"10.1109/ICESC57686.2023.10193350","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193350","url":null,"abstract":"The typical textile industry ventilation system has drawbacks such manual humidity and temperature control, a limited level of automation, and information management that is comparatively error-prone. This study designs a novel humidity and heat surveillance system, which utilizes PLC and SCADA to meet information control requirements of contemporary textiles. Numerous businesses use sequential industrial processes that are unique in nature. Due to changes in the nature of manufacturing, industries must rely on the employment of relays, stepping drums, timers, and controls; nevertheless, reprogramming these devices can be quite challenging. A redesign is frequently necessary if the entire system needs to be abandoned. PLC control systems were introduced as a solution to these issues. Automation technology used nowadays typically uses computer assisted automation.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133612016","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 Classifiers to Decrease Diabetic Patients Probability of Hospital Readmission","authors":"S. Thenappan, N. Ramshankar, N. Hemavathy, V. Subashree, Ravi Ranjan Manjul, J. Rajeswari","doi":"10.1109/ICESC57686.2023.10193669","DOIUrl":"https://doi.org/10.1109/ICESC57686.2023.10193669","url":null,"abstract":"Diabetes is a chronic condition that affects millions of individuals around the globe. Unfortunately, patients with diabetes have a significant risk of hospital readmission following discharge. Using machine learning classifiers, proposed a strategy to reduce the risk of readmission for 768 diabetes patients. This utilized a dataset containing patient demographic information, medical history, and laboratory results which are collected from Pima India Dataset. To predict the risk of readmission, and train several Machine Learning models, including Logistic Regression, Random Forest, and Support Vector Machines. The research indicates that the Random Forest model has the maximum degree of precision, at 97%. Age, glucose level, Hba1c levels, and comorbidities have been identified as the most significant factors contributing to the risk of readmission. On the basis of these findings, this recommends a personalized approach to diabetes management that takes individual patient characteristics and risk factors into account. This strategy could reduce the likelihood of readmission for diabetic patients and enhance their overall health.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133725938","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}