In today’s digital age, we are constantly blasted with an overwhelming amount of information, products, and services. Recommendation systems have emerged as an invaluable tool to help us navigate through this vast sea of choices. Whether we are browsing an e-commerce website, streaming our favorite shows, or discovering new music, recommendation systems play a pivotal role in enhancing our online experiences. In this blog post, we will explore what recommendation systems are, how they work, and the underlying algorithms that power them.
What are Recommendation Systems?
A Recommender system predicts whether a particular user would prefer an item or not based on the user’s profile and user’s information. These systems aim to overcome information overload and provide personalized recommendations to a particular user.
The term recommender system provides personalized suggestions as a result and it affects the user in an individualized way to Favourable items from the large number of opinions. Recommendation systems are becoming increasingly important in today’s extremely busy world. People are always short on time with the myriad tasks they need to accomplish in the limited 24 hours. Therefore, the recommendation systems are important as they help them make the right choices, without having to expend their cognitive resources.
Understanding the Recommendation Algorithms:
Recommendation Algorithms: Recommendation algorithms are at the core of any recommendation system. There are several types of algorithms used, including.
Content-Based Filtering: Content-based filtering recommends items similar to those a user has liked or interacted with in the past. It analyses item attributes and user profiles to identify patterns and make recommendations based on similarity.
Collaborative Filtering: Collaborative filtering utilizes user behavior and preferences to recommend items. It looks for patterns and relationships between users with similar tastes and suggests items based on what similar users have liked or purchased.
Hybrid Approaches: Hybrid approaches combine multiple algorithms to leverage the strengths of both content-based and collaborative filtering. By using hybrid models, recommendation systems can provide more accurate and diverse recommendations.
Matrix Factorization: Matrix factorization techniques, such as Singular Value Decomposition (SVD) and Non-Negative Matrix Factorization (NMF), decompose the user-item interaction matrix into lower-dimensional matrices. These techniques capture latent factors or features that represent user preferences and item characteristics. By reconstructing the original matrix, the algorithm can predict the missing ratings or recommend items based on the inferred latent factors.
Association Rules: Association rule-based algorithms discover relationships and associations between items based on the co-occurrence of items in user transactions. The algorithm identifies frequently occurring item sets and generates recommendations based on these associations. For example, if many users who purchase diapers also buy baby food, the algorithm may suggest baby food to users who have bought diapers.
Conclusion:
Recommendation systems have revolutionized the way we discover and engage with content, products, and services online. By harnessing the power of data and advanced algorithms, these systems provide tailored recommendations, enhancing user experiences and driving engagement. As technology advances, recommendation systems will continue to evolve, becoming even more accurate, personalized, and indispensable in our digital lives.
Remember, the next time you stumble upon a perfectly curated playlist or discover a book that seems tailor-made for you, you have recommendation systems.
Augmented Reality (AR) has developed as a transformational and compelling technology that blurs the boundaries between the digital and physical worlds in a world where technology is continually developing. The term “augmented reality” describes the creative blending of digital components with our physical surroundings to produce an immersive and interactive experience that improves our perception of reality. AR enhances our everyday experiences by superimposing computer-generated images, visuals, or information onto the real world we view and interact with, in contrast to Virtual Reality (VR), which immerses users in wholly computer-generated settings. Through the employment of advanced technology like computer vision, sensors, and potent processing powers, this fusion of digital and physical aspects is made possible.
AR keeps redefining how we interact with our environment and obtain information in this quickly developing industry. We can use it to interact with difficult concepts, learn about items before making a purchase, and even help with medical operations. With its impact on society, culture, and daily life projected to increase rapidly in the future years, augmented reality’s potential seems limitless.
The way we see and interact with the world will surely change as augmented reality (AR) technology develops and becomes more widely available, stimulating new spheres of creativity and improving our comprehension of the places we live in. Technology’s fascinating new frontier, augmented reality, has the potential to transform many facets of our lives and influence how people interact with computers in the future.
Applications of Augmented Reality
A wide range of useful and cutting-edge applications for augmented reality (AR) effortlessly incorporate virtual components into the actual environment. With Pokémon GO, players may hunt virtual creatures in their actual surroundings, AR delivers engaging experiences in the entertainment and gaming industries. With the help of AR, education may make hard subjects more approachable for pupils by offering dynamic and entertaining learning content, such as anatomy representations or historical reenactments. By enabling customers to visually try on clothes or picture furniture in their homes before making a purchase, augmented reality (AR) in retail improves the buying experience.
Additionally, as it can superimpose maps and current information on the user’s field of view, augmented reality (AR) has useful uses in navigation and wayfinding. AR streamlines operations and lowers errors in the industrial sector by providing personnel with step-by-step visual guidance during maintenance and repair tasks. Medical personnel’s accuracy and safety are improved through AR-assisted procedures and medical visualization. AR continues to spur innovation across industries due to its adaptability and promise to improve many parts of our lives, creating new opportunities for user experiences, productivity, and communication.
Virtual Reality (VR)
Innovative technology known as virtual reality (VR) immerses viewers in computer-generated settings for a realistic and engaging experience. The use of customized headsets or head-mounted displays (HMDs) transports people to interesting and surprisingly lifelike virtual worlds. Advanced tracking technologies are used in VR to precisely replicate the user’s movements and gestures so they can interact with the virtual environment. This technology has a wide range of uses, including immersive gaming, simulated training scenarios for many industries, educational simulations, and therapeutic uses. VR technology has the potential to alter entertainment, education, training, and many other industries as it develops and becomes more widely available. It does this by opening up a whole new world of experiences that push the limits of conventional computer and human connection.
VR takes people to dynamic, lifelike landscapes using cutting-edge headsets and motion-tracking technologies, sparking their imaginations and offering chances for exploration, education, and enjoyment. VR has a wide range of applications, from gaming and education to medicine and architecture, among others. VR hardware’s potential to change industries and improve human experiences is limitless as it develops and becomes more widely available. Virtual Reality is at the vanguard of a revolutionary era in computing and human-computer interaction thanks to its capacity to open up new vistas of possibilities and transform how people interact with technology.
Applications of Virtual Reality
Virtual reality (VR) offers a wide range of uses in a variety of sectors and professions. In the world of video games, virtual reality offers gamers completely immersive experiences that immerse them in fanciful settings where they can interact with fictional characters and environments. By allowing for interactive and engaging simulations, virtual reality (VR) in education and training revolutionizes learning by allowing students to engage in historical events, scientific ideas, and professional settings.
Healthcare uses VR for patient therapy, medical teaching, and pain management, while professionals in architecture and design use it to show off 3D models and provide virtual tours of structures. The entertainment sector is also enriched by VR thanks to the additional storytelling opportunities and virtual concerts it provides. Social VR platforms enable meaningful connections by bringing people together in common virtual worlds. Other applications include virtual tourism, real estate, engineering simulations, military training, retail experiences, and artistic creations. As VR technology continues to evolve, its impact will expand, transforming industries and redefining the way we experience and interact with the world.
Top Market Players in AR-VR and Top patent holders
Several well-known corporations with a global presence lead the AR and VR industry, including Google, Microsoft, LG, Samsung Electronics, Sony, Apple Inc., etc. Through both organic and inorganic growth tactics, such as product launches and innovations, partnerships, contracts, expansions, and acquisitions, these businesses have enhanced their market positions. The following graph displays the businesses with the greatest number of global patents in the AR/VR industry:
Apple (Vision Pro)
A ground-breaking spatial computer that seamlessly combines digital material with the real environment while enabling users to remain present and socially engaged has been presented by Apple as Apple Vision Pro.
Vision Pro develops an unlimited canvas for apps that expands beyond the limitations of a conventional display and adds a completely three-dimensional user interface that is managed by the most instinctive and natural inputs — a user’s eyes, hands, and voice — that are currently available. Vision Pro’s vision, the first spatial operating system in the world, enables users to engage with digital information in a way that makes it seem like it is actually in their area. The ground-breaking Vision Pro design includes a dual-chip dual-display ultra-high-resolution display system with 23 million pixels spread over two monitors.
The brand-new Apple Vision Pro App Store is open to users and contains thousands of popular iPhone and iPad applications that work smoothly and automatically with the new input method for Vision Pro. Apple’s developer community may go much further and produce entirely new app experiences as well as modify existing ones for spatial computing by harnessing the potent and distinctive capabilities of Vision Pro and visionOS. The visionOS three-dimensional interface gives digital content the appearance and experience of being physically present in the user’s surroundings. It helps the user understand scale and distance by casting shadows and dynamically responding to ambient light.
Apple Vision Pro presents a whole new input system that is managed by a person’s eyes, hands, and voice to enable user navigation and interaction with spatial information. Apps may be browsed through by merely looking at them, selecting with a tap, scrolling with a wrist flick, or speaking commands.
Microsoft HoloLens
Microsoft HoloLens is a cutting-edge mixed-reality gadget created by Microsoft. HoloLens provides an immersive computing experience by combining augmented reality and virtual reality. It is worn like a headset and allows users to interact with 3D holographic pictures while remaining linked to reality. The gadget integrates gesture and voice control, allowing for hands-free and easy virtual content navigation. HoloLens uses powerful sensors and cameras for spatial mapping, ensuring that virtual items are precisely integrated into the user’s environment.
This technology is used in a variety of areas, including education, healthcare, and entertainment, and allows for visualization, training, and collaboration. Microsoft offers a holographic development platform to developers, encouraging creativity and the creation of unique apps. The second-generation HoloLens improves on its predecessor with improved ergonomics and a broader field of view. Microsoft HoloLens is a big step forward in mixed reality technology, providing users with a seamless combination of digital and physical worlds and unleashing immense possibilities for future applications.
Microsoft HoloLens
Microsoft HoloLens is a cutting-edge mixed-reality device that changes the way people interact with digital material. It combines the finest of augmented and virtual reality technology to provide a one-of-a-kind and immersive computing experience. Users may see and interact with holographic pictures that are perfectly blended into their real-world surroundings by wearing the headset.
This is made feasible through a network of sensors, cameras, and processors that work together to correctly map and interpret the physical environment. The technology enables easy and natural holographic interaction. Hand gestures and voice instructions can be used to handle and control virtual objects, allowing for hands-free and immersive engagement. This breakthrough offers up a slew of new opportunities in a variety of industries, making it a potent tool for education, design, training, remote cooperation, and entertainment
Microsoft’s dedication to cultivating a dynamic ecosystem resulted in the development of the HoloLens development platform. This platform enables developers to create their own mixed-reality apps, stimulating creativity and propelling the technology’s progress in a variety of sectors. Microsoft debuted HoloLens 2, the device’s second incarnation, in 2019. This version has substantial enhancements such as a more ergonomic design, a bigger field of view, and improved hand-tracking capabilities.
HoloLens 2 expands on its predecessor’s triumphs, fixing prior shortcomings and giving an even smoother and more exciting mixed reality experience. Microsoft HoloLens continues to push the frontiers of mixed reality with its innovative technology and transformational applications, defining the future of how we perceive, interact with, and integrate digital material into our lives.
Nowadays we see the use of Artificial Intelligence (AI) in every field of study, even in image processing, data analytics, text processing, robotics, industries, software technologies, etc. Day by day increasing the use of AI helps users to perform automated tasks without involving the human physically present. The work can be completed automatically within the specified time as per the requirement. Even in the field of Software Engineering, the use of AI is growing day by day to perform automated tasks without causing errors. AI in software engineering provides automated non-error calculations, in the field of software engineering it provides the automated testing of software, automatic debugging of software, etc. to provide high quality (quality assurance), and efficiency of software applications within the budget.
Software Engineering is the method of developing, testing, and deploying computer software to solve problems and issues in the real world by utilizing software principles and best practices. It provides an organized and professional approach from developing the software to the deployment of the software. In the field of software engineering software metrics play a very important role. The software metrics are used to evaluate the reusability, quality, portability, functionality, and understandability. The AI-based software metrics are error-free and automated, they will used to identify or predict the defects in the software and efficient solutions in the real world scenario without involving humans.
Background
Many researchers describe different software metrics and provide efficient solutions for the software metrics. Some research also describes automated solutions using deep learning techniques to improve the software metrics. Without using Artificial intelligence, some chances degrade the quality of the final software products. It may also include a lack of functionality and require more human interactions due to which it increases the cost of the software. Some researchers describe deep learning-based techniques to solve software metrics problems and provide efficient results. However, the can be a lack in using the dataset and training of the data. Using the correct dataset may cause erroneous training of data and provide the wrong results. The wrong results may cause issues and degrade the quality of the software metrics. Therefore, this blog provides the proposed solution that aims to solve the problem of software metrics using Artificial Intelligence.
Basic Concept
According to the authors, there are several studies considered and provided many definitions. Therefore, before discussing the proposed approach let us discuss some basics about the software metrics as discussed.
As discussed above the software metrics are used to evaluate the reusability, quality, portability, functionality, and understandability of the software to achieve high-quality software. The software, metrics are of two types: the system-level metrics and component-level metrics as described in Figure 1.
Figure 1: Software Metrics Categories
System Level Metrics: The system level metrics are further divided into three types that is Complexity, Metrics Suite, and System Complexity.
Component level Metrics: The component level metrics are further divided into four types that are Customization, component complexity, reusability, and coupling and cohesion metrics.
Complexity Metrics: The complexity metrics are the type of system-level metrics. There are many definitions given by many authors. According to IEEE, complexity is defined as the quality where the component or any system design and implementation is complex to understand and authenticate.
Metric Suite: It provides the requirements and functionality of the software that is needed by the users. It ensures to provide users a high quality, satisfactory fault-free software products.
System Complexity Metric: It is defined as the component metric having a set of components in the system.
Customization: The customization metric identifies whether the component can be customized according to the user’s needs or not.
Component Complexity Metrics: The Component Complexity Metrics have component plain complexity (CPC), Component Static Complexity (CSC), and Component Dynamic Complexity (CDC).
Component Reusability Metric: This type of metric is the ratio of the sum of interface methods that provides common reuse component features used in a feature.
Coupling & Cohesion metrics: It is the degree or power with which software components are related to each other.
Proposed Approach
The proposed approach describes the AI-based method to identify as well as predict the defects in the software metrics. In this proposed approach, first, the real-time software metrics dataset is collected from multiple sources. The software metrics dataset may include the data regarding the identified software metrics with labels. The Figure 2 describes the architecture of the proposed approach and systematic details process of this approach.
Figure 2: Proposed Approach
Obtaining data: This step is very essential and very important step of the approach. Correct data means high performance with minimum defects and results in high-quality software. Therefore, in this step, the labeled dataset of the software metrics will be collected. The dataset will include above 50K software modules and the number of defects predicted in the software module. The predicted defects can be binary values in the form of zero and one.
Data Pre-Processing: After data, collection data pre-processing is an essential step of this proposed approach. After data collection, the data must be cleaned and normalized properly to make the analysis simpler and more efficient. In this step, the data is cleaned by removing empty rows, duplicate values, indexing of data, etc.
Artificial Intelligence Model: After data, cleaning the Artificial Intelligence Model will be applied that analyzes the data such as determining the combinations, and automating the detection procedure without human interaction. AI model can be any algorithm applied such as Linear Regression, Logistic Regression, Naïve Bayes, Support Vector Machine, etc. These algorithms will analyze the data and determine the combinations. In this step, the data can also be converted into some kind of vectors also known as numbers based on the software metrics given in the dataset. The AI model is also known as the machine-learning model.
Training and Testing: After the AI model is applied, the data will be split into train sets and test sets that are different from each other. 75% data is given for the train set and the remaining 25% of the data is given for the test set. Then the model is trained on the train set that will train the model based on the combinations and the test set will be used for our model that does not contain any labels, the machine will identify the faults automatically based on the training of the model.
Results: After applying the AI or ML model on the test set the results are obtained on the test set that will determine how best the model works. The results will be determined in terms of accuracy, the area under the curve, f1-score, confusion matrix, etc. The expected accuracy will achieve 98% by applying logistic regression on the test set.
This blog is inspired and in contributed with Dr. Kiran Narang, Department of Computer Science Engineering, SRM University
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