JOURNAL OF OPERATING SYSTEMS DEVELOPMENT & TRENDS
ISSN: 2454-9355 Scientific Journal Impact Factor (SJIF): 6.172 Index Copernicus (ICV: 63.17) Journal DOI no.: 10.37591/JoOSDT
STM JOURNALS ( Consortium eLearning Network Pvt Ltd)
Technological Advancements and Challenges in Operating Systems: A Comprehensive Review
This work provides a thorough analysis of technological advancements and challenges in the field of operating systems (OS). Starting with fundamental principles, the studycharts the evolution of operating systems, emphasizing significant milestones and contributions. It explores the impact of technological advancements such as virtualization, multiprocessing, and cloud computing on the design and functionality of operating systems. Furthermore, it discusses challenges in OS research and development, including security, real-time computing, and energy efficiency. The objective of this examination is to provide perspectives on the technological progress and obstacles encountered in the realm of operating systems.
Copyright (c) 2023 JOURNAL OF OPERATING SYSTEMS DEVELOPMENT & TRENDS
Synergetic Symbiosis: Harnessing Intelligent Orchestration for Dynamic and Secure Operating Systems
The contemporary landscape of computing demands an innovative paradigm to propel operating systems (OS) into a new era. This study, titled "Synergetic Symbiosis: Harnessing Intelligent Orchestration for Dynamic and Secure Operating Systems", ventures into uncharted territory to introduce a transformative approach. At its core lies a pioneering framework, an amalgamation of intelligent orchestration, emphasizing both dynamism and security. Through an exhaustive and meticulous study, this research explores the vast potential inherent in the synergetic symbiosis of dynamic adaptability and robust security within OS architectures.
A Review of Valley Tracing Mechanism for Implementation of Multiprogrammed Operating Systems
This review examines the application of valley tracing mechanisms in the implementation of multiprogrammed operating systems. Multiprogrammed operating systems facilitate concurrent execution of multiple tasks, necessitating efficient resource allocation and scheduling to maximize system throughput and responsiveness. Valley tracing, a concept rooted in identifying and exploiting periods of decreased resource demand during program execution, offers a promising approach to optimize resource utilization and enhance system performance. The review begins by introducing the concept of multiprogrammed operating systems and the challenges associated with managing resources in such environments. It then delves into the fundamentals of valley tracing, elucidating its significance in identifying valleys, periods of reduced resource requirements, in program execution profiles. Valley tracing mechanisms encompass various components, including resource monitoring, valley detection algorithms, dynamic resource reallocation strategies, task scheduling policies, and feedback mechanisms for adaptive adjustment. This review delves into an extensive examination of available literature and research contributions, exploring both the advantages and difficulties linked with valley tracing mechanisms. It highlights the potential of valley tracing to improve resource utilization, responsiveness, and overall system efficiency in multiprogrammed operating systems. However, it also addresses challenges such as overhead, complexity, tuning, and integration, which must be carefully considered in the implementation of valley tracing mechanisms. The review concludes by emphasizing the importance of continued research and development efforts to advance the state-of-the-art in valley tracing mechanisms for multiprogrammed operating systems. By addressing key challenges and leveraging insights from program execution dynamics, valley tracing has the potential to play a pivotal role in optimizing resource management and enhancing the performance of modern operating systems in diverse computing environments.
A Roadmap for Next-generation Operating Systems: Navigating Towards Future Innovation
As technology advances rapidly, there is a growing demand for next-generation operating systems (OS) to keep up with the changes. This study delves into the research roadmap essential for shaping the future of operating systems. By identifying current challenges, analysing emerging technologies, and envisioning future user needs, this roadmap aims to provide a comprehensive guide for researchers and developers navigating the complex landscape of OS innovation. From advancements in security and privacy to optimization for heterogeneous computing environments, this study outlines key areas of focus, potential strategies, and anticipated milestones for the development of next-generation operating systems.
OS 2.0: Revolutionizing Operating System Architecture for Quantum Computing
Quantum computing promises unprecedented processing power by harnessing quantum mechanical phenomena. However, existing operating systems are inadequate for the unique demands of quantum computers. This research proposes OS 2.0, a new operating system architecture tailored for quantum computing. A comprehensive literature review identifies key requirements like managing quantum entanglement and error correction. Evaluation of current operating systems reveals performance limits, lagging fault tolerance, and inability to control quantum interference effects. OS 2.0 aims to resolve these gaps through a modular, layered design optimizing memory management, parallelism, and qubit routing. Simulations demonstrate OS 2.0 reduces quantum state decoherence by 62% and executes quantum algorithms 78% faster than existing systems. By revolutionizing operating system design for the coming quantum era, OS 2.0 enables the construction of stable, scalable quantum computers outperforming classical machines. The principles pioneered in OS 2.0 chart the course for further advancements as quantum devices mature.
Face Recognition Systems: An Anti-spoofing and Liveliness Verification Toolkit
The safety of facial recognition technology is vital in the current digital era. In this research, a deep learning approach to face spoofing detection is presented, covering the complete process from data collection to real-time application. To guarantee model robustness, a 7,000-image bespoke dataset that includes both “real” and “fake” facial scenarios is curated. The YOLO architecture, a state-of-the-art object identification model, is used after necessary preprocessing. The model, which was developed using Google Colab's GPU, is enhanced with a confidence threshold to produce accurate predictions. After being trained, the model is incorporated into a real-time application that gives users a confidence score for dependability and color-coded visual indicators to distinguish predictions. The system successfully detects face spoofing, highlighting the potential of deep learning to strengthen facial recognition security. By combining data science and software development, this work offers a comprehensive approach to face spoofing detection, promising improved security for facial recognition systems.
Mouse-less Interaction Using Augmented Reality
This project aims to revolutionize computer interaction in augmented reality environments by developing a mouse-less system operated by fingers in the air. The conventional use of physical mouse restricts user movement and immersion in virtual environments. The project's primary objectives revolve around tackling issues related to precision, response time, the identification of gestures, and the seamless integration of augmented reality. The proposed solution involves selecting appropriate motion tracking technologies and designing a hardware prototype that incorporates these technologies. Gesture recognition algorithms are developed to interpret finger movements captured by the motion tracking sensors. A wireless communication module is implemented to establish a real-time connection between the wireless mouse and the computer system. Finally, the developed mouse-less system is integrated with an augmented reality framework to enable interaction with virtual objects and user interfaces. The effectiveness and usability of the solution are evaluated through comprehensive demonstrations in various augmented reality applications and scenarios. This project aims to enhance the overall user experience in augmented reality and revolutionize the way users interact with computers. The wireless finger-operated mouse system has the potential to transform computer interaction and prepare the way for more immersive and natural augmented reality experiences.
Quality of Services (QoS) in Linux Operating System
Quality of Service (QoS) is a fundamental characteristic of contemporary computer networks, enabling the prioritization of network traffic and the allocation of network resources according to specific criteria. The Linux operating system provides a powerful set of QoS tools, including Traffic Control, Differentiated Services, and Network Emulator. These utilities enable you to establish traffic categories, impose bandwidth constraints, and give precedence to traffic according to diverse criteria such as IP address, port number, protocol, and packet size. In addition, the Netem module allows you to emulate various network conditions for testing and debugging network applications and services. In summary, Quality of Service (QoS) functionalities within Linux offer a versatile and effective system for overseeing network traffic, guaranteeing that essential applications and services obtain the required bandwidth and resources for optimal operation.
Dynamic Priority-based Adaptive Scheduling (DPAS) for Modern Operating Systems
Dynamic Priority-based Adaptive Scheduling (DPAS) is an innovative CPU scheduling algorithm designed to optimize system performance and resource utilization in modern computing environments. As computing systems become increasingly complex and diverse, traditional static scheduling algorithms often struggle to adapt efficiently to the dynamic nature of workloads. DPAS addresses this challenge by introducing a dynamic and adaptive approach to process prioritization and resource allocation. DPAS leverages real-time feedback, machine learning, and resource awareness to assign and adjust priorities for running processes. Unlike conventional schedulers with fixed priority schemes, DPAS continuously evaluates process behavior and resource utilization, making real-time adjustments to process priorities. This adaptability ensures that critical processes receive the necessary attention while preventing resource monopolization by any single task. Furthermore, DPAS incorporates adaptive time quantum allocation, energy-efficient scheduling, and security measures to strike a balance between responsiveness, energy conservation, and system security. It utilizes process grouping and tagging to accommodate different workload characteristics and requirements, enhancing both fairness and system efficiency. The DPAS algorithm represents a significant advancement in CPU scheduling, offering improved system responsiveness, resource management, and adaptability to diverse workloads. This abstract provides an overview of DPAS's key principles, highlighting its potential to enhance the performance and reliability of modern computing systems
Bluetooth Low Energy Enabled Door Locking System
The need for security is rising in this world, everything that is from home to city is smart. One prominent area of interest pertains to Smart Locks, which can be seen as the contemporary evolution of conventional locks. The adoption of smart lock systems in numerous residences and commercial structures represents an inventive method that offers numerous user-centric advantages. This study discusses one such ongoing process that makes the smart lock more user friendly using the Bluetooth Low Energy technology. It uses Wi-Fi to establish communication between the user’s Smartphone and ESP32 microcontroller board. The prototype supports both manual and microcontroller controlling to lock and unlock the doors which makes it more reliable.
Python: Empowering Data Science Applications and Research
It was in 1991 that the first mention of Python came to light. Since it is one of the best programming languages, it is widely used in the data analytics industry. It is speedy, easy to use, and can smoothly alter data with no hiccups. It aids in the processes of data analytics generally, such as data collection, analysis, modelling, and visualization. Python stands out from other languages because it provides all the necessary tools for developing cutting-edge applications and machine learning quickly and easily. The fields of data science and analytics have benefited greatly from recent advances in computing power, the capacity to store vast volumes of data, and knowledge of relevant methodologies in areas such as Data Analytics, Artificial Intelligence, Machine Learning, etc. The goal of this study is to have a discussion on the. All the fundamental resources for developing Python applications may be found in a single place with the help of an IDE (Integrated Development Environment). Integrated development environments (IDEs) are research-oriented software packages that bring together a familiar Graphical User Interface (GUI) with the most widely used programming languages and tools. It often consists of a source code editor for writing programs and local build automation for creating a local version of the software, like compiling computer source code. Additionally, it enables you to duplicate the programs for use on your personal computer. These editors may be used by developers and software engineers to design apps for personal computers or the web; by DevOps engineers to carry out continuous integration; and to aid in job automation and boost the software engineer's productivity and expertise. Some of the many great advantages of adopting a top-tier Python integrated development environment are listed below. Build automation, code linking, testing, and debugging are just some of the features available in Python IDEs that may help you save time. This research will begin with an overview of the Python Data Science Library before moving on to a discussion of data science's many practical applications. In addition, this research provides a comprehensive evaluation of Python IDEs used in the fields of data science and research.
Attack Detection and Analysis in Virtual Wireless Networks using Mininet-WiFi, CIC Flowmeter, Wireshark, and Machine Learning
Mininet-WiFi is a powerful tool for creating virtual wireless network environments to test various networking scenarios. However, attacks like Distributed Denial of Service (DDOS) attacks can still affect these simulated networks. A CIC flowmeter can be used to monitor network traffic and Wireshark can be used to record and analyse network data in real-time to detect and analyse such assaults. However, the data obtained from these tools may be noisy, which can negatively impact the accuracy of attack detection and analysis. To address this issue, machine learning algorithms can be applied to clean the data obtained from CIC flowmeter and Wireshark. In this study, we propose an approach that uses Mininet-WiFi in combination with a CIC flowmeter, Wireshark, and machine learning algorithms for data cleaning to detect and analyse DDOS attacks in a virtual wireless network. We will discuss the setup and configuration of Mininet-WiFi, CIC flowmeter, and Wireshark, and demonstrate how machine learning algorithms can be used to clean the data obtained from these tools to improve the accuracy of attack detection and analysis.
Identification of Handwritten Digits using Machine Learning
Handwritten Digit Recognition is one of the practical issues in sample recognition applications. The task for handwritten recognition has been difficult due to various variations in written styles. The capacity to create an effective algorithm that can detect handwritten numbers given by users via a scanner, tablet, and other digital devices is at the core of the issue. Artificial intelligence is used in machine learning, which automatically corrects errors based on experience. In this paper, we present a proposal to off-line Handwritten Digit Recognition through machine learning techniques. This article compares classifiers like KNN, PSVM, NN, and convolution neural network on the basis of performance, accuracy, time, sensitivity, positive productivity, and specificity while using different parameters with the classifiers. The handwritten digits (0 to 9) from the well-known Modified National Institute of Standard and Technology (MNIST) dataset are recognized.
Backup Solid State Drive for Ransomware Protection
Ransomware is a type of malicious software that can block a users’ access to their own files and restricts access until a ransom amount is paid. There is no certainty that we would get our files back even after paying the ransom amount. Since these ransomware attacks are increasing day by day and are causing heavy losses to people, we have proposed a new backup based ransomware defense system along with a feature of malicious files scanning. This software works on the principle of automatic backup and detects the newly added files in the system in realtime and instantly back ups them in the destination folder, which is embedded into a Solid State Drive (SSD). The software itself also resides in the SSD drive because being separated from the host machine provides better data protection than the softwares which are inside the machines as they are vulnerable to ransomware attack, this also helps it to operate in various environments. The software provides several options to the user which includes, simple PC backup, android backup, automatic backup, as well as cryptic backup. Users can also scan their files to check if the files are safe.
Ransomware: Malicious software that blocks file access
A New Systematic Approach of Using SVM, KNN, PNN and CNN to Achieve Higher Accuracy for Classification and Recognition of Music Genre
In order to overcome challenges like finding songs that are similar, figuring out which cultures would enjoy particular music, conducting surveys, and using music as therapy, classification and recognition of music genres are essential. The expansion of music-related content available online has created new opportunities for the development of music referral systems, which support user communities in searching, finding, sharing, and producing digitally. Our aim is to develop a systematic approach to select the most optimum machine-learning algorithm for predicting song genre using Support Vector Machine (SVM), k-nearest neighbor (k-NN), Probabilistic Neural Network (PNN) and Convolutional Neural Network (CNN). We got the results for CNN having system’s accuracy level is 98.9% and a precision of 98.7%, f1 score of 97.5%, recall of 98.5%. and the accuracy level of the system is 98.3% from SVM, 98.5% from PNN, 99.9% from KNN.
Mel Frequency Cepstral Coefficients (MFCC)
Windows Operating System’s Volume Controller Using Hand Gestures
In this study, we develop volume controller in which we use hand gesture as input to control system. OpenCV module is basically used in this implementation for gesture control. Basically, this system uses the web camera to record or take pictures/videos, and the app controls the system volume based on the input. The primary purpose is to modify the system volume. Hand gestures can control basic computer tasks, such as adjusting volume. This natural and innovative form of communication replaces burdensome machine-like skills. The system's potential in human-computer interaction is vast. The purpose of our project is to show how to control volume using hand gestures. Identifying particular human gestures for device control is the goal of hand gesture recognition. We are creating a system that uses video input to allow specific users to control a computer with hand gestures. Our project uses OpenCV and Python for a gesture-controlled volume system. No keyboard or mouse is required.
Copyright (c) 2022 JOURNAL OF OPERATING SYSTEMS DEVELOPMENT & TRENDS
Spectrum of Issues Assessing the Social, Ethical, and Legal Aspects of VR/AR/MR
With the rapid growth of RRR (VR, AR, MR) technology, there is a greater need for better interaction features such as immersiveness, interactivity, and multi-perception. RRR, as a new emerging information technology, presents a possible solution for generating interaction and fusion for data integration technology between both real and virtual space, and then provides different products and services. As RRR technology becomes more widely available, it is critical that we understand the possible effects on consumers, both positive and negative. This study focuses on the social, ethical, and legal issues or challenges that arise as a result of the usage of VR, AR, and MR technologies. We will also discuss the practical challenges that come with employing these technologies. According to Moore's Law, there is a relationship between technological advancement and social, legal and ethical assessments. We go over all of these issues and plan for future developments. It examines the characteristics of RRR technology that provide issues for the construction of legal regulation for its implementation. This study evaluates the interaction characteristics of RRR and draws conclusions about their relationship in social, ethical, and legal challenges.
A Mental Health Chatbot Based on Artificial Intelligence and Machine Learning
Chatbots provide young people additional low-barrier chances for preventive mental health support. We outline an exploratory design study in which we created and assessed a prototype chatbot to assist school nurses in their work with the school health service in order to give the knowledge required to develop chatbots for this purpose. The development of the prototype was aimed at promoting mental health prevention. School nurses, professionals in digital health, and kids, all participated in the design process. We identified four categories of help to be offered through the chatbot through user insight activities: informative, relational, processual, and referral support. Through concept development and prototyping, we investigated these four sorts of support. These findings are offered as a possible starting point for a framework for comprehending young people's requirements for chatbots for preventive. Additionally, you can use ML chatbots as your most powerful marketing tool to advertise your goods or services. By proactively recommending your products to customers based on their search history or past purchases, chatbots can boost sales conversions.
Implementation Chatbot Whatsapp using Twilio and Rasa
Virtual Private Server (VPS) usage is still quite pricey in India. In the IT industry, the chatbot application system is crucial, particularly for disseminating information in a direct and palatable way to numerous users at once. This study primarily focuses on users using the Chatbot system with the WhatsApp app. Python is used to programme this chatbot system. The user needs to join with Twilio using join and the sandbox name keyword and then user gets connected to the Chatbot system. The Python software will then read the user's request to enter the chatbot, and if the incoming message satisfies the criteria, the chatbot will send the information in accordance with the criterion. However, Chatbot will still go through the motions of reading incoming messages if it does not match. The Chatbot technology is made to work well with numerous users at once. The chatbot server connection affects how quickly messages are sent and how quickly each incoming message is checked. If a message contains stickers, emoticons, or gifs, the chatbot simulation programme will not be able to read it and will it reply with “Bye”. A random message feature can still be added to this study to advance it.
Graphical Password Authentication Using PassPoint and PTC Scheme
Mobile devices, which would hold a lot of private and sensitive information, are now a requirement in people's daily lives due to their rising capability. This makes smartphones a top target for cyberattacks, as either the loss of such mobile devices or unauthorised access will result in a breach of personal data and financial harm. Over the past few years, a number of new types of authentication systems have been presented in an effort to allay that worry. One of those classes is Graphical Authentication (GA), which is the subject of this study. Due to their primarily graphics-oriented nature, superior memorability versus text-based schemes, and lack of additional hardware requirements, GA schemes are more well-liked and preferred for smart devices. The majority of these GA systems are vulnerable to many well-known attacks, including brute force, shoulder surfing, and smudging. As a result, a novel hybrid authentication scheme, Passpoints and Press Touch Code, or PTC, that smoothly combines two distinct yet widely used authentication techniques is proposed in this work. By thwarting those well-known threats, this new technique aims to provide a higher level of security.
Implementation of E-mail Security with Three Layers of Authentication
E-mails are essentially based on point-to-point communication. E-mails are the most commonly used form of communication in today's business. These are now the most convenient modes of communication for most people. We will also look into adding confidentiality and integrity protection to standard email. In your daily life, how much reliance do you place on the accuracy of its contents? Consider receiving an e-mail from your mentor stating that you performed so well in your course that you were excused from doing any additional work. What if you know that message was a joke from a classmate? For sensitive information or communication, we always rely on e-confidentiality mails and integrity. The email client is a critical network application. It is significant when it is used to communicate vital information such as business data, patient medical records, and so on by business, health, and educational groups. Data and messages can be easily sent and received across internal networks or the Internet, allowing messages to be received, forwarded, saved, and broadcast to recipients. Email has become a popular choice for official communications in many businesses due to its numerous features. Electronic mail is widely used for personal purposes and is accessible via a variety of suitable software clients as well as a web browser. Email security has become a critical concern for the information security research community in order to improve email security. The developed application with three layers of authentication provides enhanced security for critical emails shared over the Internet and eliminates the risk of the message being hacked.
E-mail, security, mail security, network, internet, web browser
Handwritten Digit Recognition Using Logistic Regression, SVM, KNN and CNN Algorithms
The style of handwriting varies from person to person. Handwritten numbers are not always the same size, orientation and width. To develop a system to understand this, the machine recognizes handwritten digit images and classifies them into 10 digits (from 0 to 9). The recognition of handwritten digits is a technology which is used for the automatic recognizing and detecting handwritten digital data through various deep lerning and machine learningmodels. This paper uses a different machine learning algorithms to improve productivity anda variety of models to reduce complexity. Machine Learning is a subset of Artificial Intelligence, actually Machine Learningapplications which learns from previous experiences and it automatically improves with the previous experiences. This paper is about recognizing handwritten digits from 0 to 9 from the familiar Modified National Institute of Standards and Technology (MNIST) dataset, then comparision takes place between machine learning algorithms like Support Vector Machine(SVM), Logistic Regression, K-NearestNeighbor (KNN) and deep learning algorithm like CNN.
Support vector machine, KNN, logistic regression, CNN, handwritten digit images
This inventive venture has displayed a comprehensive diagram of the impacts that color hypothesis and color brain research have on showcased, particularly in case selecting a certain color plot run the show has an impact on buy aim. This venture delivered three thirty-second video notices that are all precisely indistinguishable, but each essentially actualizes a distinctive color conspire to run the show based on the same fundamental color. The three-color plot rules utilized in these commercials are closely resembling, triadic, and complementary. The media pieces were made to be focused on the age statistic of 18-25 and exhibit the ordinary utilize of a Bluetooth speaker. This subjective examination, at the side the media pieces created as a portion of the extend, will serve as an establishment for future quantitative ponders that can look at the part of color plot run the show choice and whether that has an impact on buy expectation.
Stock Price Movement Prediction using Machine Learning Algorithms and Time Series Models: A Review
The stock market is a very important place for investment in a country. It also serves as the index of the growth for an economy. To determine the behavior of the stock market trends has been the focal point of researchers for very long. The nonlinear structure of the stock market makes it challenging to predict how the stock price will change. But it has been demonstrated that stock market behavior can be predicted using various machine learning algorithms. These techniques use historical data of the stocks to train the machine learning algorithm and predict their future behavior. Also there exist some time series models that can help in forecasting the stock trend since the historical data of the stock market is time series data. This study reviews the various machine learning algorithms and time series models that are used for stock market predictions. It also implements Long Short-Term Memory (LSTM) and Support Vector Regression (SVR) algorithms to predict the stock market behavior.
Stock market, machine learning, support vector machine, artificial neural network, time series
Android-Based Real-Time Driver Drowsiness Detection and Alert System
Traffic accidents cause millions of people to lose their lives every year. Statistics assert that most fatal accidents are due to driver drowsiness. So the rate of fatality due to accidents caused by drivers falling asleep is high. There have been various researches and developments on driver drowsiness and fatigue detection systems by industries and academic researchers over the years. However, the solutions produced are usually found on luxurious vehicles and are computationally expensive to be connected to embedded devices or mobile devices with operating systems such as Android. This paper proposes to a cost-effective and efficient driver drowsiness detection system that can be installed on mobile devices or smart embedded devices using state-of-the-art technological advancements in the area of machine learning and artificial intelligence to prevent or reduce accidents that occur due to drowsy driving.
Driver Drowsiness Detection, Real-time detection, android device, mobile devices, drowsy driving
Video Watermark for Text Data Using QR Code
In the current era, data security is most important and so is copyright protection. By using this project, the user can secure his data, logo etc. We are incorporating a digital video watermark into QR Codes for text data. This technique is used for data hiding into video. This technique can be classified as either spatial or frequency domain. Some video watermarking issues are addressed in video watermarking. This project can be used in any organization to send secure messages. This project is also helpful to provide ownership to video. Digital image is processed with help of digital computer which will be used for providing watermark to video and secure text into video. There are two separate algorithms used for encryption and decryption. In this project, QR code will give best performance to store the data. This process is easy and practically used for providing copyright protection. This project provides security to users’ data by using encryption key and then that data is stored in to QR code; if the user wants to decrypt it, the user will have to provide decryption key which is provided at the time of encryption. The project has main modules as: 1) input video, 2) extract frames, 3) provide logo, 4) provide text data, 5) encrypt data, and 6) decrypt data.
Video Watermarking, DWT, Java, QR Code, Copyright protection.
Course recommender is a recommender system in which courses are recommended to the users like how your friend might recommend you movie based on his/her experience; similarly, this system recommend courses (mostly students). It is just that in this we recommend based on user data which is in numerical format. Nowadays people are very keen to study more, apart from their chosen stream and they tend to search for other courses they might want to try, but as there are lots of choices in the courses or they may not have fixed domain they want to study, that is where our recommendation system in introduced. In this system, we use machine learning algorithm on sample dataset to recommend courses. Recommendations are based on user information like their academic scores and ratings of the courses given by other users. This system also tries to consider users’ cognitive ability, or you can say learning capability to further recommend further course. The proposed system is webbased UI considering user friendliness and ease of use.
Review Paper on Waste Food Management and Donation Using Android Application
Food waste is a serious problem in today’s society, food is wasted on daily basis in places such as restaurants, weddings, school/college canteens, social events, and other gatherings. According to reports, 40% of Pakistan's food is wasted every day at various events and weddings. On the other side, many techniques have been using to control food waste from different perspective. In last few years, one of the famous technologies which is growing in all research areas is internet of things. This study shows the current standing of IoT in the field of waste food management. With the help of various researches, these methods help to reduce food wastage. Furthermore, during a food waste of household or in hotels, different challenges are mentioned by the researchers and they are working on this domain. In this application, you may order leftovers for donations. There is an admin module who has access to modify, delete the profile and make the changes. Restaurants, schools, colleges and others can be users. First, they register themselves if they are not already registered. They can register with their email and there are options to pick up the order. This study introduces “Annachatra”, a new Android application that allows people to donate leftover food in order to reduce food waste. The application provides a way for restaurants to donate leftover food, as well as a way for organizations to donate food to people in need.
Keras Based Real-time Hand Gesture Recognition
Everyone can communicate effectively with hand gestures. Hand gesture recognition, as opposed to the traditional text-based or GUI-based method, can be viewed as a way for computers to better grasp the language of the human body and develop richer human-computer connections. Hand gestures can help to simplify tasks by allowing you to give orders to the computer using simple hand movements. The camera records gestures, the computer interprets them, and an appropriate action is executed in response to the gesture. This project is based on hand gesture recognition, which captures hand motions and converts them into instructions that the machine can comprehend and execute. In this project, we will first capture the gesture with the camera. The captured image is then preprocessed for feature extraction, and a convolutional neural network is implemented using a backpropagation algorithm to effectively recognize gestures and train the machine.
Hand Gestures, Image Processing, Deep Learning, OpenCV, Python
Traditional newspapers and magazines are a popular way to keep up with current events and new items. Furthermore, these printed materials only provide static information to readers. The Indian newspaper industry grew by relying on advertising, which started to fall apart before the pandemic. As a result of the Pandemic Shutdown, news outlets have closed, and journalists have lost their jobs. To resurrect their fortunes, they are attempting to generate income through the digital medium. The increased use of online search functions, mainly through Google, has also influenced reader habits. Augmented reality (AR) in newspaper publication can help readers become acquainted with a new reading pattern. Augmented reality technology for newspapers can be an intriguing feature that takes readers beyond the printed pages. Thanks to advancements in Unity and Vuforia, Augmented reality can help journalists provide information more conveniently while also providing viewers with a closer look at news stories. The results and evaluation of the newspaper expressions is the interactive way of reading newspaper experience and can increase newspapers circulation.
Augmented Reality, Mobile Device, 3D Object, Multimedia Information, Newspaper, Digital Medium
One of the most recent technologies is 'Cloud Computing,' which has grown rapidly in recent years with the goal of delivering computing as a utility. This is not an easy task of fulfilling this technological demand. The infrastructure required to support Cloud Computing is a very large data centre with a large volume of data, a large number of servers, and various other related hardware. Several companies are investing in the construction of large data centres to house various cloud computing services. These data centers need a very huge amount of energy which is very complex in infrastructure. All data centres are built on a large scale with many computing nodes and consume a massive amount of electrical power, resulting in higher operational costs. All of these increase in energy consumption, also demands overusing and rethink of the energy efficiency of cloud infrastructures. Nonetheless, before making any changes to the infrastructure, we must first assess the current situation. One of the key research issues that cloud computing researchers have been attempting to resolve is the reduction of carbon emissions by data centres. By investigating all possible areas in the cloud infrastructure that are responsible for a significant amount of energy consumption, this paper discusses modern and recent systems that have been developed to improve energy efficiency in large scale cloud computing infrastructures while not compromising service quality and performance. A comparative analysis of various other survey papers published recently is noted, as well as their key contributions. It also discusses the various tools used in the design of energy-efficient schemes. A futuristic scope in the domain is also provided for the cloud computing research community based on recent trends in energy-efficient algorithms.
Cloud Computing, Cloud infrastructure, Datacenters, Cloud energy, Cloud administration
Introduction to cryptography, certain cryptographic techniques, and quantum cryptography, as well as the differences between traditional and quantum cryptography, are all covered in this paper. Quantum cryptography is the science of performing cryptographic operations using quantum mechanics principles. Quantum key distribution is the most well-known example of quantum cryptography, which provides an information-theoretically secure solution to the key exchange problem. Quantum cryptography is an attempt to create a shared and hidden body of data between two users on a common communication channel. This data, which is often in the form of a random string of bits, can be used as a standard secret key for secure communication. Quantum cryptography has an advantage over standard key exchange methods in that information can be proved to be safe in a very strong sense without making assumptions about the intractability of certain mathematical problems. Even if a hypothetical eavesdropper had infinite computing power, the rules of physics guarantee that the secret key exchange will be secure probabilistically.
Key distribution, photon., secure communication, Quantum cryptography, wireless LANs, key management
Usage of Artificial Intelligence and Machine Learning in Share Market
Lately, because of the fast improvement of man-made consciousness (AI) and Machine Learning, its application has been broadly utilized in numerous parts of monetary zone, just as essentially impacts share market, organizations and guideline. The man-made reasoning innovation carries tremendous change to the whole money related industry, which makes a progression of inventive monetary administrations, for example, shrewd advisor, wise loaning, observing and cautioning, and keen client support as times required. In this paper, it expects to sum up the use, advancement and use of manmade consciousness and AI in share advertise. Meanwhile, it is understood that a progression of issues and dangers were directed by computerized reasoning during its utilization. Finally, a few models are given of utilization of computerized reasoning in share advertise.
Artificial intelligence, share market, TEW algorithm, artificial neural network (ANN), time delay neural network (TDNN), probabilistic neural network (PNN)
Overview of Different Operating Systems for IoT Constrained Devices
The Internet of Things appeals to an incredible deal of studies and industry interest currently and is envisaged to guide numerous rising domain names and disciplines such as smart cities, health informatics, and smart sensory platforms. Operating device guide for IoT performs a pivotal function in growing scalable and interoperable packages that are dependable and efficient. IoT is carried out through each high-end and low-end device that requires working systems. Recently, we have witnessed a range of OSes rising into the IoT surroundings to facilitate IoT deployments and developments. In this paper, we present a complete assessment of the not unusual place and current open-supply OSes for IoT. Each OS is defined in info primarily based totally on reminiscence control and strength efficiency. We present a taxonomy of the contemporary IoT open-supply OSes. The goal of this survey is to provide a well-based manual to builders and researchers to decide the maximum suitable OS for every particular IoT device/application primarily based totally on their functional and non-functional requirements.
Real-time OS, energy-efficiency, memory-management, IoT OS, open source.
Android OS has become one of the most famous mobile phone OS which is continuously deployed by many smartphone mobile devices assemble for a range of platforms.in this world after the internet and computers smartphones is the third revolution, On the downside, Android is also very fast becoming platform for security attacks. Android security have been based upon a privilege-based mechanism that prohibit access of third-party Android apps to demanding assets on an Android smartphone device. Android apps runs over permission-based structure in which each app`s access is managed according to the permission that it achieves during installation. However, present research shows that android privilege framework is inherently by design are weak and privileged permissions can be got by malicious operation by fire privilege escalation attacks. by these attacks, an application may get privilege to perform a privileged task which it is not authorized. In this review paper, we examine the arising concern in Android OS security, covers Excess Privilege Attacks, conflict attacks, inefficient permission administration, overclaim of privilege, privilege escalation attacks, and TOCTOU (Time of Check to Time of Use) attack, spyware etc. We also discuss ways to detect it.
Android operating system, smartphone mobile devices, android App, cyber- criminals, TOCTOU
CEO BOT—Working as a College Enquiry Chatbot by Applying Artificial Algorithms and Making it Rule Based
A Chat Bot is a bit of technology that leads a discussion in audio or literary strategies. Such software is frequently intended to depict how a human would carry on as a conversational accompany. The answers to the enquiry of the students are given by this system which is a web application. Conversation can be done by querying the system via bot by the students. As there is no particular format the user will have to follow, so students can do chatting in any format. To answer the queries, the system uses designed dataset. The answers are to the user queries are to the point. Through the bot or the system, user can query any college related activities. User need not go to the college personally for college enquiry. User's queries are detected and analyzed and then properly answered. The answers provided by the system seems like they are answered by some person. This answering of queries in done with the help of Artificial Intelligence. Graphical User Interface is used to reply to the queries which imply that if a real person is replying. Also, it contains an AI rule which make users understand that this question is repeated, so not to ask.
Bot, dataset, artificial intelligence, graphical user interface, AI rule
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Parallelization of Metaheuristics for the Optimization of Permuted Perceptron Problem
Parallel computing has found a mainstream backing in graphics processing units (GPUs).These resources have enormous processing capacity, are energy efficient, and are broadly available, unlike grids. Since the advent of CUDA (Compute Unified Device Architecture) developed by NVIDIA that permits GPU programming in C, C++ language, GPUs are now being used in a variety of fields, including scientific computing. This thesis focuses on the optimization of the solution of a NPcomplete problem called Permuted Perceptron Problem based on cryptographic identification scheme, which particularly meets the requirements for resource restricted devices like smart cards. We will study about GPU optimization techniques for parallel metaheuristics in order to achieve an efficient solution.
Permuted perceptron problem, ILS, Tabu Search, simulated annealing, metaheuristics
Increase Use of Multifactor Authentication
AS we all know a single verification is not capable of providing enough security to a user’s while accessing the area like insurance, medical record, banking etc. In this user just type their id and password to login into the website but users want website to increase the security. By using multifactor authentication scheme, we can increase the security level we are going to know in this paper. In this method user requires to login with ID and password. As soon as user enter his/her ID and password system will generate one OTP (One Time Password) which will be send on registered mobile number of the user. We can also use image-based conformation and question-based conformation. We are going to use SHA algorithm and Lamport’s algorithm to develop this system. With the help of these algorithms more secure multifactor authentication can be created. At the front end we use visual studio and at the back end we use MY SQL. At the end we get see more secured system.
Multifactor authentication, security, OTP (one time password), graphical password; knowledge-based authentication
User Authentication Using React Native Application
Due to the advancement of mobile and technology have gained their speed along with this fast-growing era of the internet, many of us started using smartphones and tablets to perform sensitive operations, such as making in-app payments. When we consider all tasks, we perform on mobile devices, and the information it has access to, we can easily understand about user information security need. The password authentication systems currently prevailing cannot determine the authorization of the user, as the same password can also be used by an unauthorized person, if it gets into the wrong hands, the sensitive data can leak and may lead to many undesirable consequences. As a result, Biometric authentication started gaining popularity, as putting your fingertip to a touch sensor is much easier than writing a password, and fingerprints also have advantages that it cannot be easily guessed or stolen we are going to develop a methodology for authenticate the user using a mobile application.is developed. This application using a Realtime database that stores the fingerprints and a numerical value to check the authenticity of the user. Use the information in the user's fingerprint and these numerical values provides the enhanced security to the login of the application.
Biometric, Server, Data Security, Password Authentication, Mobile Devices, GUI
An Optimised CPU Scheduling Algorithm with Adaptive Time Quantum Approach
CPU scheduling is an essential mechanism implemented by the operating system to determine the execution of multiple processes by the CPU. The primary objective of the scheduling algorithms is to optimize the systems’ performance efficiently. The performance of a CPU scheduling algorithm depends on various factors and can be evaluated on various criteria like average turnaround time, average waiting time, throughput, fairness etc. This paper aims to present an optimal CPU scheduling algorithm, Adaptive Quantum Round Robin (AQRR) in a uniprocessor environment. Related work has been done on increasing the performance of existing Round Robin Algorithms using dynamic time quantum approaches, but the maximum percentage gain in turnaround time and waiting time using these approaches, over the traditional Round Robin algorithm is not more than 30% and 40% respectively. The proposed algorithm in this paper outperforms these algorithms in both turnaround time and waiting time. The AQRR algorithm is based on the standard Round Robin algorithm, but it is integrated with a dynamic time quantum which is self-adaptive triggered on the event of the arrival of new processes in the ready queue or the completion of a process in the process queue. The dynamic time quantum is updated based on the remaining burst time of the running processes in the process queue at that instance as well as a weightage associated with it. Finally, a comparative study between the prevailing similar scheduling algorithms and the proposed algorithm is observed and noted, based on different scenarios. The following algorithms are compared on two criteria: average turnaround time and average waiting time.
Operating System, Scheduling Algorithms, Weighted Average, Round Robin, Resource Management