Journal of Firewall Software‎ and Networking最新文献

{"title":"Smart Footstep Energy Harvesting System","authors":"P. Shevantikar, A. Udata, Vinayak Ravindra Jadhav, Om Ramesh Dixit, A. Korachagao","doi":"10.48001/jofsn.2024.2117-19","DOIUrl":"https://doi.org/10.48001/jofsn.2024.2117-19","url":null,"abstract":"This research paper introduces a novel approach to footstep energy harvesting by integrating piezoelectric sensors into flooring surfaces. The project aims to generate sustainable electrical power from footsteps, contributing to the development of energy-efficient systems. The proposed system incorporates a unique feature that enables users to easily identify and address sensor failures or damages. The core components of the system include piezoelectric sensors strategically placed in high-traffic areas, an Arduino microcontroller for signal processing and energy management, and a rechargeable battery for energy storage. A rectifier circuit is employed to convert AC signals generated by the sensors to DC, while a voltage regulator ensures a stable output voltage. The energy harvested is stored in the battery, which is connected to a USB charging module capable of charging electronic devices. To enhance the system's usability and maintenance, LEDs are integrated with each piezoelectric sensor. These LEDs act as indicators for sensor status, allowing users to visually identify any damaged or failed sensors. The Arduino code is designed to continuously monitor sensor values and activate the corresponding LED when a sensor falls below a predefined threshold, signaling a potential issue. This unique feature enhances user awareness, simplifies troubleshooting, and aids in the efficient maintenance of the energy harvesting infrastructure. The research focuses on the practical implementation of the system, exploring its effectiveness in real-world scenarios. Various aspects, such as energy generation efficiency, adaptability to different environments, and the ease of sensor failure identification, are evaluated through extensive experimentation. The paper discusses the system's potential applications, including sustainable power generation for small electronic devices and its integration into smart infrastructure. The proposed Smart Footstep Energy Harvesting System with Sensor Failure Identification addresses a crucial aspect of sustainability and smart infrastructure management. The findings from this research contribute to the growing field of energy harvesting technologies and pave the way for future developments in efficient, user-friendly, and maintainable footstep energy harvesting systems.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140739248","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":"Revolutionizing Customer Interaction: NLP-Powered Virtual Shopping Assistants and Sentiment Analysis in E-commerce","authors":"Dattatray G. Takale","doi":"10.48001/jofsn.2024.221-5","DOIUrl":"https://doi.org/10.48001/jofsn.2024.221-5","url":null,"abstract":"The title of the research article investigates the revolutionary influence that Natural Language Processing (NLP) and sentiment analysis have had on the interactions that customers have with businesses that are involved in e-commerce. Among the primary goals are the investigation of the applications of natural language processing (NLP) in the powering of virtual shopping assistants and the evaluation of the relevance of sentiment analysis in comprehending the level of customer pleasure. An in-depth assessment of the current literature, case studies of successful implementations, and an analysis of customer evaluations and attitudes are all components of the technique. The results highlight the critical role that natural language processing plays in the enhancement of virtual shopping assistants, the improvement of product suggestions, and the simplification of the process of making a purchase choice. Additionally, sentiment analysis is essential for determining the feelings and feedback of customers, which in turn influences the strategic decisions that are made by organisations that deal in e-commerce enterprises.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"36 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368422","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":"Smart Irrigation with the Use of Machine Learning","authors":"Dattatray G. Takale, S. Telang, Shubhangi Topale, Kanishk Suryawanshi, Parth Samgir","doi":"10.48001/jofsn.2024.2111-16","DOIUrl":"https://doi.org/10.48001/jofsn.2024.2111-16","url":null,"abstract":"A comprehensive review of recent challenges faced by farmers and how Machine learning technology can be used to overcome these challenges in this paper. In this paper, we explore various machine-learning technologies and methodologies that contribute to the development of agriculture systems. When the appropriate amount of water is given to the crop it will give us the best results but the problem is we are not measuring how much amount of water we gave to the crop, and that’s why we could not get satisfactory results in our research paper we found a Support Vector Machine (SVM) algorithm that can be very useful in this case along with temperature and sensor soil moisture sensors. However, these technologies are still facing many challenges. To improve existing techniques further research is required.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"32 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139962218","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":"Comparative Analysis of LSTM, RNN, CNN and MLP Machine Learning Algorithms for Stock Value Prediction","authors":"Dattatray G. Takale, Aditya A. Wattamwar, Saksham S. Saipatwar, Harshwardhan V. Saindane, Tushar B. Patil","doi":"10.48001/jofsn.2024.211-10","DOIUrl":"https://doi.org/10.48001/jofsn.2024.211-10","url":null,"abstract":"In the context of the increasingly globalized financial markets, this review paper compares four prominent ML algorithms for stock value prediction: LSTM, CNN, RNN, and MLP. Through rigorous evaluations using diverse historical stock price data and key metrics like MAE, RMSE, and precision, the study reveals unique strengths and weaknesses in each algorithm's ability to capture stock price dynamics. LSTM emerges as the top performer, followed by CNN, RNN, and MLP. However, the authors emphasize that the selection of the best algorithm relies on certain data characteristics and desired accuracy levels.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"63 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140486901","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":"Scheduling Algorithm in Infrastructure Less Network for PQ – A Study","authors":"S. Priyadharsini, K. Thamizhmaran","doi":"10.48001/jofsn.2023.114-6","DOIUrl":"https://doi.org/10.48001/jofsn.2023.114-6","url":null,"abstract":"Due to its inherent resource limitations, wireless based dipole networks are increasingly being used in security-critical applications. As a result, they are more vulnerable to different security assaults, including the Grey-hole attack, which has a significant impact on data collecting. For WSNs, the Active Trust security and trust routing technique is suggested as a means of overcoming that difficulty. By using the NS2 Simulator to define the path for security in the priority queuing in scheduling algorithm, hostile nodes are reduced, speed is further improved, and at the same time, excellent security is created with minimal routing overhead. In order to fulfil security targets for the following metrics packet delivery ratio, routing overhead, and throughput, we are implementing a secure dynamic on-demand routing protocol. The performance of the priority queuing algorithm is assessed in this proposed scheduling task in order to achieve Quality of Service (QoS). The HHLS-MARS4 method improves the effectiveness of the network topology, allowing the proposed protocol to provide secure transmission while also reducing routing overhead, delay, and packet loss.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133768708","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":"Scheduling Algorithm for Wireless Network Using FIFO – A Study","authors":"K. Prabu, K. Thamizhmaran","doi":"10.48001/jofsn.2023.111-3","DOIUrl":"https://doi.org/10.48001/jofsn.2023.111-3","url":null,"abstract":"Since the network is managed by no fixed infrastructure, the MANET does indeed have a number of issues, including frequent link failures, hidden nodes, a changing topology because of mobility, increased interference and constrained bandwidth because of the shared medium, and rapid power consumption on the mobile nodes. Due to these and other factors, it has developed into a difficult study subject. The Internet Engineering Task Force (IETF) MANET group has suggested several design principles for MANET routing protocols. Many different technologies, including satellite systems, television remote controls, computer interface devices, and GPS systems, can use wireless networks. \"Faulty nodes\" are nodes that act improperly as a result of an error or accident. The other category is referred to as \"malicious nodes,\" and it includes those that have been taken advantage of by an attacker. FIFO queuing is used in the NS2 simulator's scheduling method. In order to achieve Quality of Service (QoS), the performance of the First in First out (FIFO) scheduling method is assessed in this paper.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133075271","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":"Identification of Plant Leaf Disease Using a Novel Convolutional Neural Network","authors":"V. R, R. Mohanabharathi","doi":"10.48001/jofsn.2023.1111-15","DOIUrl":"https://doi.org/10.48001/jofsn.2023.1111-15","url":null,"abstract":"A critical element in preventing a major outbreak is the detection of plant leaves. An important research issue is the automatic detection of plant diseases. For both human life and condition, a plant's dedication is essential. Like humans and other animals, plants do suffer the negative impacts of illnesses. A plant's normal development is influenced by the frequency of plant diseases that occur. The entire plant, including the leaf, stem, organic material, root, and flower, is affected by these diseases. Most of the time, if a plant's ailment is not treated, it dies or may cause leaves, blooms, organic products, and so forth to fall off. For accurate identification and treatment of plant diseases, appropriate determination of these disorders is necessary. Plant pathology is the study of plant infections, their causes, and methods for preventing, managing, and eradicating them. However, the current approach includes human inclusion for structure and identifying disease proof. This tactic is time-consuming and expensive. Instead of using the current method, a programmed division of diseases from plant leaf images utilising a delicate registration methodology may be more beneficial. In this study, we describe a method for identifying and characterising plant leaf diseases naturally called Bacterial Searching Improvement Based Radial Basis Function Neural Network (BRBFNN). We use bacterial search streamlining (BFO), which increases the speed and accuracy of the system to recognise and organise the regions contaminated by diverse illnesses on the plant leaves, to assign Radial Basis Function Neural Network (RBFNN) the proper weight. The location development calculation increases the system's efficiency by searching for and gathering seed focuses on typical traits for the highlighted extraction operation. To make progress against parasite diseases including early curse, leaf twist, leaf spot, late scourge, and basic, cedar apple, and leaf rust. The suggested approach achieves more accuracy in identifying evidence and characterizing infections.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116295444","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":"Automated Water Billing and Leakage Detection System","authors":"Puneeth P N, Sanjay S D, Chethan C N, Bhargvram B L, Nataraju A B","doi":"10.48001/jofsn.2023.117-10","DOIUrl":"https://doi.org/10.48001/jofsn.2023.117-10","url":null,"abstract":"This project deals with automatically collecting a customer’s water consumption and detecting leakages in the water distribution system. For effective water monitoring, metering, data analysis and communication technology are combined in an integrated water bill and leak detection system. Real-time supply and demand data is collected using water meters and smart sensors. The data is analyzed using sophisticated algorithms and machine learning, which can detect patterns and anomalies that could indicate violations. When potential issues are detected, automated notifications are sent for immediate action. By consolidating consumption information with billing software, this solution automates payments while maintaining accuracy and accessibility. Operators can make smart decisions and optimize maintenance with real-time data visualization and reporting. The system encourages water savings, reduces water loss, and increases operational efficiency.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115069346","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":"Automatic Pothole and Humps Detection System of Roads","authors":"Nataraju A B, Akarsh C J, Lakkam Arun Kumar, H. B. Sanjay, Harshit H V","doi":"10.48001/jofsn.2023.1116-20","DOIUrl":"https://doi.org/10.48001/jofsn.2023.1116-20","url":null,"abstract":"One of the largest difficulties in developing countries is road maintenance. The nation's economy benefits greatly from well-maintained roads. Finding pavement issues like potholes and humps not only assists in preventing crashes and vehicle damage but also helps with road upkeep. In order to identify potholes and speed humps on roads and inform drivers in a timely manner so they can avoid collisions or serious injuries, this report highlights the pothole detection methods that have been created. Potholes and humps are detected using ultrasonic sensors. The suggested system uses a GPS receiver to record the geographic coordinates of potholes and humps. This pothole detection system’s idea is to alert the drivers if the pothole is detected on the way to reduce the speed of the vehicle or choose another path.","PeriodicalId":282937,"journal":{"name":"Journal of Firewall Software‎ and Networking","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115817431","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}