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Video Conferencing and Live Streaming Platform Using Web RTC
People must communicate with one another for a variety of reasons, including personal and professional ones. Depending on the circumstances, an in-person meeting might not be feasible, which is more costly and logistically demanding. Additionally, the demand for long-distance and remote communication has increased as a result of the global epidemic. To address these issues, a web application for video conferencing is being implemented. Web RTC is used in the construction of the backend architecture. Web RTC is an open source, free framework that gives web browsers Real-Time Communications (RTC) features using straight forward Java script APIs. High-quality real-time communication applications for your webpage are the goal of Web RTC. The real-time web dynamically makes it possible to establish connections between browsers with ease. This makes it possible for our web application to offer a variety of functions, including audio calls, video chat, file sharing, screen sharing, and text chat. React JS is used to design the front end. Our signaling server is Firebase.
Machine Learning and Secure Image Transmission for Disease Forecasting
In today’s era of information technology, the rapid expansion of the Internet, especially in the realm of electronic healthcare, has become both feasible and widespread. Internet-enabled electronic healthcare systems allow patients to consult with specialist doctors remotely for diagnoses. Medical images are frequently stored, processed, and transmitted online, and during this process, sensitive data may be involved, requiring strict privacy measures. Given the importance of protecting patient information, encrypting this data is crucial to ensure that only authorized individuals can access it. Encrypting medical images presents unique challenges, particularly because of the need for fast and accurate data extraction. Traditional encryption methods may not always be suitable for securing large medical images effectively. Therefore, it's essential to safeguard the algorithms used for processing these images from potential attacks. One commonly used encryption tool is a random number generator, which produces sequences of numbers that are not predictable. The more unpredictable the number generation, the stronger the encryption, which makes it more effective. Chaos systems are often employed to generate pseudo-random numbers, providing a robust encryption key.
Network Security Intrusion Detection Methods Combining Optimization Algorithms and Neural Networks
Cyber-attacks are becoming increasingly common and sophisticated, and the security of net-works is a pressing concern for companies everywhere. To spot and stop attempts to access network resources without authorization, intrusion. There is, however, a problem that many traditional IDS methods can generate a lot of false alarms. This means that it misidentifies safe activity as a threat and is not readily accepted by security teams for which real issues. Scalability and resistance to evolving threats a hybrid approach of a neural network combined with an adaptive algorithm was used in this paper focuses on improvement the performance and accuracy of the intrusion detection system proposed by us. Combining genetic algorithms and particle swarm optimization with neural networks using a framework optimized for comprehensive performance is present in this tutorial. More to the point, parameters and training procedures for Deep learning models in intrusion detection tasks. The objective of this research paper model is to make a false alarm the rate should be decreased, search accuracy increased and in real-time network environments, the system should adapt easily. Experimental results show that the combination of optimization algorithms and neural networks outperforms traditional IDS methods.
Analysis and Comparison of Communication Strategy of Guzhengart Based on Artificial Intelligence
The integration of Artificial Intelligence (AI) into traditional art forms has opened new frontiers for both the creation and communication of artistic expressions. This paper explores the communication strategies of Guzhengart, a traditional Chinese string instrument, by employing AI-driven technologies to analyze, enhance, and compare communication processes in different contexts. Through a comparative analysis, we examine how AI tools such as machine learning, natural language processing, and audio analysis software can model and interpret the emotional, cultural, and technical dimensions of Guzheng performances. By contrasting traditional methods of conveying Guzheng music with AI enhanced strategies, we assess the potential of AI to expand the artistic reach of Guzheng performances globally while preserving its cultural heritage. The findings reveal both challenges and opportunities in blending ancient musical traditions with modern AI capabilities, ultimately contributing to a deeper understanding of cross cultural communication in the context of performing arts.
Development and Application of Artificial Intelligence Multimedia Technology Based on Big Data
Artificial intelligence is an innovative enterprise theme that combines computer science, physiology, science, and other disciplines, including expert programs, technical experiments, games, native language understanding, cognitive rehabilitation, robotics, engineering, recognition, and other fields. The breakthrough growth made the arrival of the era of big data have a fundamental impact on many traditional industries and has also promoted the transformation of teaching methods and methods in the field of education. In the traditional classroom, due to the limitations of teaching resources, teaching time, and location, students are basically passively accepted by the teacher, and the teaching form is single. This will improve in the age of big data with well-developed network message. Modern distance education uses advanced technology such as computer networks, multimedia, and artificial intelligence to build a virtual teaching environment based on the network. It breaks through the limitations of teachers, teaching materials, experimental equipment, and other resources that exist under the traditional teaching model. Using educational resources of the same scale can expand educational capabilities exponentially and at the same time overcomes the unified progress of traditional teaching methods, single teaching methods, and students’ dissatisfaction. In this research we developed a system based on intelligent multimedia method and have the characteristics of security and stability and have done some help for network teaching of educational institutions.
Analysis of Computer Aided Interaction Design Based on Virtual Reality Technology
The conventional method of computer-aided interaction design faces challenges due to ambiguous user experience characteristics, resulting in a reduced number of identifiable frames. To address this issue, a novel computer-aided interaction design approach utilizing virtual reality technology has been developed. This method facilitates communication through visual computing, enabling the acquisition of feedback structures from computer vision. It allows for the mapping of human body contours and the extraction of user experience characteristics via virtual reality technology. Additionally, it measures the time required for reading and generating pertinent information, thereby constructing an effective computer-aided interaction mode. Experimental findings reveal that the average frame identification rates achieved by the proposed method are approximately 81(fps), 74(fps), and 72(fps), respectively. These results, when we will compare with two other computer-aided interaction methods, demonstrate that the integration of virtual reality technology significantly enhances performance. This review revealed that the majority of studies are centered on practical case analyses within specific application scenarios, employing empirical evaluation methods to assess objective or subjective metrics. We then concentrated on elucidating the foundational principles of interface design and their evaluation methodologies, providing a reference for future research endeavors. Additionally, the limitations, challenges, and future directions in VR interaction interface design research were discussed, highlighting the need for further research in design evaluation to continuously refine the development of standards and guidelines for VR interactive interface design.
User Authentication System Using Python
User authentication is a critical component in ensuring the security and privacy of digital systems. This paper User authentication is a critical component in ensuring the security and privacy of digital systems. This paper explores the implementation of user authentication explores the implementation of user authentication systems using Python, a versatile and widely-used systems using Python, a versatile and widely used programming language. With its robust libraries and frame works, Python provides a comprehensive eco system for designing, developing, and deploying secure authentication mechanisms. The study focuses on the practical implementation of authentication methods, including password-based systems, two-factor authentication (2FA), and biometrics integration. Python libraries such as Flask and Django are employed to create backend systems, while libraries liked crypt and pass lib are utilized for hashing and securing passwords. The integration of 2FA is demonstrated using PyOTP, which facilitates one-time password generation to enhance security. This paper also highlights the role of JSON Web Tokens (JWT) in enabling secure, stateless user sessions, making them suitable for modern web and mobile applications. Python's inter operability with biometric authentication systems is explored through the use of APIs and machine learning libraries, providing a foundation for advanced authentication mechanisms. The proposed authentication system is evaluated for its security, scalability, and ease of implementation, offering insights into best practices for minimizing vulnerabilities such as brute force attacks and data breaches. Additionally, the paper emphasizes the importance of encrypting sensitive user data during transmission using libraries like cryptography and SSL/TLS protocols. In conclusion, the paper demonstrates Python's potential to implement secure, flexible, and scalable user authentication systems suitable for diverse applications. It serves as a guide for developers and researchers aiming to enhance the security of their systems through robust authentication techniques. Programming language with its robust libraries and frameworks, Python provides a comprehensive eco system for designing, developing, and deploying secure authentication mechanisms. The study focuses on the practical implementation of authentication methods, including password-based systems, two-factor authentication (2FA), and biometrics integration. Python libraries such as Flask and Django are employed to create backend systems, while libraries like bcrypt and passlib are utilized for hashing and securing passwords. The integration of 2FA is demonstrated using PyOTP, which facilitates one-time password generation to enhance security.
Tsunami Alert and Detection System Using IoT
Internet of Things (IoT) is the connectivity between the numbers of devices in the network. In which different types of devices, sensors, software, embedded devices are connected to each other for information sharing and exchange. In different applications, IoT technology is used for communication and data exchange between the two or more stations or devices. IoT technology is used in various application and fields like Transport, business, home automation and education. Tsunami detection is also one of the major problems, which is solved by using IoT. Tsunami alert and detection are important for avoiding the human death. So to solve these problems different techniques, algorithms, protocols are proposed by authors using IoT. In this paper, we have studied different papers on Tsunami detection and alert system for the survey. This paper is very helpful for new researchers and IoT learners.
An IOT Embedded Pollution Monitoring Mobile App for Smart Cities
Over the past fifty years, there has been a significant migration of people from rural areas to urban centers due to a lack of facilities, educational institutions, healthcare services, and employment opportunities in remote regions. This mass movement has led to increased pressure on large cities, which now face challenges in maintaining a livable and healthy environment as their populations grow rapidly. The demand for civic amenities has surged, particularly impacting road traffic, which has become a major contributor to air pollution and has negatively affected urban environmental health. In today's world, it is crucial to continuously monitor environmental pollution in cities to ensure they remain healthy and livable. One effective solution is the use of the Internet of Things (IoT) combined with smart sensor systems. These technologies can be deployed for various monitoring purposes, including tracking pollution levels in large urban areas. By integrating IoT with a network of sensors, a centralized city office can efficiently manage and oversee the system. This research proposes a framework for air quality monitoring in smart cities utilizing IoT and advanced sensor technology. The system is designed to measure parameters such as humidity, carbon emissions, temperature, smoke, noise, and other hazardous particles present in the atmosphere. The collected data is then transmitted to a central office for analysis, enabling timely actions to improve the city's environmental conditions. Additionally, the gathered information is stored in a database for future reference and can be shared with research institutions and environmental agencies for further studies.
Hand Sign Detection for the hearing and speech Impaired using Convolutional Neural Network
Learning and utilizing sign languages to communicate with deaf and mute persons has been easier because to developments in computer vision technology. The development of a global platform for communication in a variety of sign languages is an attractive area of research. In this research, we introduce a Deep Learning-based method that uses an image as input to recognize special sign in Indian Sign Language. When the user enters digits 0 through 9, the system can guess their signs. The Convolutional Neural Network's training time and storage needs are effectively decreased by using image processing to transform RGB data to grayscale images. The experiment's goal is to identify a less sophisticated deep learning architecture and image processing is used to implement the system on embedded single-board computers or mobile applications. The database is trained from scratch using small networks like LeNet-5 and AlexNet, as well as deeper networks like Vgg16 and MobileNet v2. Comparing the accuracy of recognition is covered in the study. A dropout layer that improved training and testing accuracy to 91.37% and 87.5%, respectively, is one of just 10 layers that comprise the final design that was selected.
AI Based Course Suggestion Portfolio
The rapid expansion of online education platforms has fundamentally changed the way students engage with learning opportunities. Students now have access to a vast array of courses, offered by different institutions, in various fields of study, and at different levels. [1] However, with such an overwhelming selection, choosing the right courses that align with a student's academic path, interests, and career aspirations can be a daunting task. Traditional course recommendation systems, which have relied on simple rule-based or manual methods, often fall short of addressing the diverse and evolving needs of modern learners. [5] To solve these challenges, this paper proposes the development of an AI-powered course suggestion system that adapts to the individual needs of each student by using advanced machine learning techniques. Unlike traditional systems, which are confined to static rules or rigid structures, the AI-based system is designed to learn from each student's behaviour, interactions, and academic profile to offer personalized course recommendations that evolve over time.
A Comprehensive Study on Facial Expression Recognition Using Deep Learning Techniques
In the previous years, the landscape of computer vision has changed significantly with improved algorithms. One such area is face expression which owes its magnificence due to the rapid advancement in techniques of deep learning. This research paper gives exhaustive studies on face expression with their relate on various CNN deep learning models, evolution trends in methodologies and the state-of-the-art architectures. The challenges as well as possible applications of the same will also be addressed. The primary aim is thorough analysis on the effectiveness and efficiency that deep learning comes with for face expression tasks, and finally offers insight on current trends and future directions in this rapidly evolving domain.
Enhancing IOT Systems with Machine Learning: A Framework for Intelligent Decision Making
The convergence of the Internet of Things (IoT) with machine learning (ML) holds considerable promise for advancing green computing. By optimizing resource use, reducing energy consumption, and fostering sustainable practices, this integration has the potential to significantly contribute to environmental sustainability. This paper delves into how IoT and ML can work together in areas like energy management, predictive maintenance, HVAC optimization, smart agriculture, and waste management. It also examines the challenges in deploying these technologies, such as concerns around data privacy, scalability, resource constraints, and interoperability. Looking ahead, the paper highlights emerging trends like edge computing, federated learning, AI-driven sustainable solutions, and the development of smart cities, which offer exciting avenues for future research and development. Ultimately, the combination of IoT and ML is seen as a powerful tool to not only enhance operational efficiency but also to achieve broader sustainability goals.
IoT Driven Smart Lock System
The evolution of the Internet of Things (IoT) has led to the development of advanced security solutions for homes and businesses. One such solution is the IoT-based smart door lock system, which offers enhanced security features compared to traditional locking systems. This paper explores the design, implementation, and potential benefits of an IoT-based smart door lock system for home security [6]. It discusses the integration of wireless communication, mobile applications, and remote monitoring features, highlighting the security enhancements these systems provide. Additionally, the paper addresses the challenges and future potential of smart door locks in the context of IoT technologies.
Automatic Captcha Recognition Technology Based on AI
Captcha (Completely Automated Public Turing test to tell Computers and Humans Apart) is doing an phenomenal critical object for safeguarding online systems from automated bots and ensuring user authentication. With advancements in Artificial Intelligence (AI), captcha recognition technology has undergone significant transformation, allowing automated systems to decipher even highly complex captcha formats with remarkable accuracy. This paper dives onto application of AI methodologies, with addition of Convolutional Neural Networks (CNNs) for effective captcha solving. Through the use of various advanced algorithms, AI systems excel in tasks such as text recognition, segmentation, and noise reduction, addressing traditional challenges like distortion and visual clutter. The study also evaluates the associated security risks, ethical considerations, and potential counterstrategies to strengthen system defenses.
Automated Control and Monitoring in Greenhouses: An IoT-Driven Approach
This paper discusses the possibility of an IoT-driven approach toward the automation of control and monitoring in greenhouses, thereby establishing an efficient sustainable approach towards modern agricultural problems. The given system allows for real-time monitoring as well as for accurately controlling key environmental factors such as temperature, humidity, light, and soil moisture by using interconnected sensors, cloud-based data processing, and intelligent algorithms. The coupled use of various technologies fosters efficient resource exploitation, human interference reduction, and crop yield increase. The studies emphasize ethical implementation. The technological gains postured enable harmonization with sustainability, equity, and inclusiveness principles. Real-time data harvesting and in-time analysis allow for immediate reaction in line with dynamic changes associated with plant growth, minimizing waste. An experimental framework provides comprehensive testing for the system's performance in various conditions, demonstrating its adaptability and scalability for high-scale deployment. Importantly, the research points out the need for collaborative efforts in addressing global food security issues through innovative technologies. Moreover, it enhances productivity and energy efficiency sharply while pointing out positive environmental implications for the future of IoT systems in agriculture. Finally, the paper identifies areas of future directions on how to enhance such a system through predictive analytics and AI-driven optimization. This work constitutes a significant step toward more sustainable agriculture, providing further insights into a pathway to harnessing the potential of IoT in feeding soaring food demands while conserving nature and promoting benevolent technological development. Another aspect of this research is the potential to exploit renewable energy sources-solar power-in an effort towards more sustainable harvesting and possibly a lesser dependence on non-renewable resources. The proposed approach further stretches into how predictive analytics might work proactively to prevent the failure of system elements and ideal crop management through data-driven insights. The findings focus on the broader implications of incorporating IoT into agriculture, where it would eventually address two of the most pressing global issues currently being dealt with: climate change and food scarcity.Through the promotion of precision agriculture, the significance of adopting new technologies should therefore be felt to alleviate environmental impacts while meeting increasing demands for agricultural production.
Smart Campus: Based Automatic Attendance System Using Photo Face Recognition
This review examines a number of experiments that use the Smart Attendance system to record attendance using facial recognition. In many organizational and educational contexts, managing attendance effectively and accurately is essential. Manual attendance tracking using traditional methods is laborious and prone to mistakes. The suggested system recognizes and confirms the identity of people in a certain context by using computer vision and deep learning techniques. It enables smooth and effective attendance tracking by using facial recognition algorithms to capture and match facial features with records already in a database.
Digital Image Watermarking
In recent years, the field of digital image watermarking has garnered considerable attention due to its critical role in safeguarding broadcasting media. This technique serves as a powerful tool for ensuring the authenticity of content, monitoring media broadcasts, managing duplication control, and tackling a variety of related issues. Researchers have extensively examined digital image watermarking to address these challenges, making it a pivotal area of study. This paper delves into the concept of digital image watermarking, starting with an overview of its fundamental model. It then explores the primary requirements for effective watermarking systems and highlights their diverse applications. Additionally, the paper reviews key techniques and algorithms employed in watermarking, analyzes potential threats and attacks that such systems may face, and outlines the methods used to evaluate the performance and reliability of these systems.
Enhancement of Sales Forecasting and Prediction with Machine Learning Methods
Accurate sales forecasting is essential for building an effective business strategy. It empowers companies to make smarter decisions that keep inventory in check, manage resources wisely, and adapt to shifting customer demands. By predicting sales accurately, businesses can cut down on the costs of having too much or too little stock, manage cash flow more efficiently, and fine-tune their marketing and sales efforts. Traditional forecasting methods, like moving averages and autoregressive models, have been helpful but often fall short when it comes to the complex, fast-changing data that modern businesses deal with, influenced by seasonality, promotions, and consumer trends. These limitations have paved the way for machine learning (ML). In this research we can reveal hidden patterns in large datasets for much more precise predictions. With machine learning, companies can draw on diverse data sources, including historical sales, market trends, economic indicators, and even outside factors like weather or social media trends, to create more accurate and flexible forecasts.
A Water Pollution Monitoring System Based on IoT
In the 21st century, there were lots of inventions, but at the same time were pollutions, global warming and so on are being formed, because of this there is no safe drinking water for the world’s pollution. Nowadays, water quality monitoring in real time faces challenges because of global warming limited water resources, growing population, etc. Water pollution is one of the biggest fears for the green globalization. In order to ensure the safe supply of the drinking water the quality needs to be monitor in real time. In this paper we present a design and development of a low cost system for real time monitoring of the water quality in IOT(internet of things).The system consist of several sensors is used to measuring physical and chemical parameters of the water. The parameters such as temperature, PH, turbidity, flow sensor of the water can be measured. The measured values from the sensors can be processed by the core controller. The Arduino model can be used as a core controller. Finally, the sensor data can be viewed on internet using WI-FI system.