System and method for website evaluation

Abstract

The system (100) introduces a novel approach to multimedia analysis and website optimization. It utilizes a custom neural network architecture and evaluates diverse multimedia elements on websites for quality and contextual relevance. The system (100) uses advanced Natural Language Processing (NLP) techniques and sentiment analysis for analysis of textual and audial content. DRAM features dynamic adaptation to evolving web content standards and employs predictive cross-browser compatibility testing for proactive issue identification. The system (100) integrates semantic -visual analysis, employing a proprietary matrix of similarity and visual feature extraction for comprehensive content assessment. The system (100) also uses real-time benchmarking, user feedback loops, and a proprietary scoring system for immediate and accurate evaluations of website relevance and effectiveness.

Description

SYSTEM AND METHOD FOR WEBSITE EVALUATIONTECHNICAL FIELD

[0001] The present invention relates to the field of information processing technologies and networking. More precisely, the present disclosure relates to a system and method for evaluating and ranking websites.BACKGROUND

[0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0003] Presently, website evaluation systems primarily rely on static methods that fail to adapt to evolving web standards and user expectations. Traditional approaches in the prior art often lack the sophistication to comprehensively analyze multimedia content and interpret nuanced textual information, especially in the context of governmental communication. Moreover, existing systems for website evaluation in the prior art can struggle with crossbrowser compatibility testing and real-time content evaluation, limiting their effectiveness in dynamic web environments.

[0004] In CN116150538, the disclosed system includes collecting webpage content to evaluate the quality of the webpages. The system uses target search words for searching the webpage for any relevant information and can also search for images on the provided webpage. However, webpage quality checking may produce false positives or negatives. For example, the system might incorrectly flag certain elements as problematic or fail to identify genuine issues in the webpage. The system is also focused on textual analysis of webpages. There is also no provision for analyzing multimedia content in webpages.

[0005] Therefore, there is a need for a system with a dual-analysis framework that encompasses both textual and multimedia content assessment.OBJECTS OF INVENTION

[0006] Some of the objects of the present disclosure, that at least one embodiment herein satisfy are as listed herein below.

[0007] It is an object of the present disclosure to accurately evaluate multiple websites and a content therein.

[0008] It is an object of the present disclosure to continuously learn from evolving web standards and user interactions, ensuring the system’s agility and adaptability to different types of dynamic web content.

[0009] It is an object of the present disclosure to analyze the performance of websites, evaluating factors such as loading speed, server response times, and resource loading efficiency.

[0010] Yet another object of the present disclosure is to assess the quality and contextual relevance of multimedia elements, ensuring a comprehensive evaluation of images, videos, and other multimedia elements on websites.SUMMARY

[0011] Within the scope of this application, it is expressly envisaged that the various aspects, embodiments, examples and alternatives set out in the preceding paragraphs, in the claims and / or in the following description and drawings, and in particular the individual features thereof, may be taken independently or in any combination. Features described in connection with one embodiment are applicable to all embodiments, unless such features are incompatible.

[0012] In an aspect of the present disclosure, the system for website evaluation comprises a crawling module to parse multimedia content, indexing it into an organized database. The neural network module extracts features and conducts contextual relevance analysis on the multimedia content, encompassing structure, pages, and web content. An evaluation module assesses the website's loading speed and overall performance, evaluating page load times, server response times, and resource loading efficiency. Additionally, the system identifies and analyzes performance bottlenecks like large image files, excessive scripts, and inefficient code. The system provides a detailed and holistic evaluation of websites, covering both content relevance and technical performance aspects.

[0013] In another aspect of the present disclosure, the method can include employing a crawling module to parse multimedia content and index it in an organized database. A neural network module can extract features and perform contextual relevance analysis on the multimedia content, covering structure, pages, and web content. An evaluation module assesses the website's loading speed and overall performance by evaluating page load times, server response times, and resource loading efficiency. Furthermore, the method identifies and analyzes performance bottlenecks such as large image files, excessive scripts, andinefficient code within the website. The method provides a comprehensive and detailed evaluation, addressing both content relevance and technical performance aspects of websites.

[0014] Various objects, features, aspects, and advantages of the inventive subject matter will become apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.BRIEF DESCRIPTION OF DRAWINGS

[0015] The specifications of the present disclosure are accompanied with drawings of the system and method to aid in better understanding of the said invention. The drawings are in no way limitations of the present disclosure, rather are meant to illustrate the ideal embodiments of said disclosure.

[0016] In the figures, similar components and / or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

[0017] FIG. 1 illustrates a block diagram of the system for website evaluation, in accordance with an embodiment of the present disclosure.

[0018] FIG. 2 illustrates an exemplary method for website evaluation in accordance with an embodiment of the present disclosure.

[0019] FIG. 3 illustrates an exemplary computer system for website evaluation, in accordance with an embodiment of present disclosure.DETAILED DESCRIPTION

[0020] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such details as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0021] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will beapparent to one skilled in the art that embodiments of the present invention may be practiced without some of the specific details.

[0022] If the specification states a component or feature “may”, ’’can”, ’’could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have that characteristic.

[0023] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.

[0024] The present invention relates to the field of information processing technologies and networking. More precisely, the present disclosure relates to a system and method for evaluating and ranking websites.

[0025] FIG. 1 illustrates a block diagram of the system for website evaluation, in accordance with an embodiment of the present disclosure.

[0026] Referring to FIG. 1, a system 100 for website evaluation, the system 100 comprising: a crawling module 110, to crawl a website to parse a multimedia content therein and wherein the crawling module 110 indexes the multimedia content into an organized database 118 for further analysis; extract, with a neural network, features and perform a contextual relevance analysis on the multimedia content comprising structure, pages, and a web content; evaluate, with an evaluation module 114, a loading speed and an overall performance of the website comprising assessing page load times, server response times, and resource loading efficiency; and identify and analyze performance bottlenecks, such as large image files, excessive scripts, or inefficient code in the website.

[0027] In an embodiment, the neural network 112 is tailored for adaptive evaluation of multimedia content on websites, comprising feature extraction and contextual relevance analysis. The system 100 utilizes a ResNet50 deep learning model for intricate image analysis, enhanced with customized convolutional neural network 112 (CNN) layers specifically designed for web content. It includes an algorithm for dynamic feature extraction from multimedia elements, assessing parameters such as resolution, color depth, and thematic relevance to the textual content. The evaluation employs vector space modeling and cosine similarity for semantic alignment, facilitating a comprehensive understanding of multimedia relevance within the context of web content.

[0028] In an embodiment, the neural network 112 may have a custom architecture for the specific requirements of multimedia analysis for web content. The architecture of the neuralnetwork 112 is likely designed to specialize in processing and evaluating multimedia elements, surpassing standard models such as ResNet50. The neural network 112 may include specialized layers for image and video processing.

[0029] The neural network 112 includes an input layer receives the initial data comprising either textual information, image pixels, or other multimedia features. Each input corresponds to a specific feature or aspect of the data. Further, the neural network 112 includes an embedding layer, which converts textual input, such as words or tokens, into dense vectors with semantic meaning. The embedding layer helps the network understand the relationships between words. The neural network 112 may also include a Bi-directional Encoder Representations from Transformer (BERT) or DistilBERT layers, which are part of an advanced Natural Language Processing (NLP) model in the system 100. The BERT layer provides contextualized representations of words, capturing their meaning based on the surrounding context. The neural network 112can also include a visual processing layers for visual feature extraction from images. The neural network 112can be effective in capturing hierarchical features, recognizing patterns, and extracting meaningful representations, and reducing the dimensionality of the extracted visual features, retaining the most important information. The neural network 112 can also combine the processed textual and visual features and ensure the seamless fusion of information from both modalities for further analysis.

[0030] The neural network 112may also include a mechanism for sentiment analysis wherein the sentiment is expressed in textual content, determining whether it is positive, negative, or neutral. The keyword extraction layer can identify key terms or phrases within the text, contributing to the understanding of important themes. The neural network 112can also group related content together based on thematic similarity, aiding in the organization and interpretation of information. If mentioned in the context of adapting the model to various content domains, the layers of the neural network 112can facilitate the transfer of knowledge learned in one domain to improve performance in another. The output layer can also produce the final results or predictions based on the processed information, which include assessing the relevance and effectiveness of multimedia elements on websites.

[0031] In an embodiment, the system 100 may utilize an adaptive benchmarking algorithm with machine learning to dynamically adjust evaluation parameters in real-time based on emerging web standards. The emerging web standards can include increasingly dynamic websites with more complex visual, textual and even audio content and a higher amount of user interaction. Therefore, the system 100 can use a proprietary scoring system100 for integrating Al-driven content analysis to generate user experience metrics for utilizing reinforcement learning for optimizing evaluation criteria through continuous feedback loops. The feedback loops can provide continuous feedback regarding the status of the websites and also the reinforcement learning used by the system 100 to update the evaluation parameters therein.

[0032] In an embodiment, the system 100 can carry out context-aware semantic processing for topic extraction and sentiment analysis. In an example, the system 100 can consider a news article about a technology product launch. The article might contain the following sentences: "The new smartphone is amazing. The camera quality is outstanding, and the design is sleek." In provided example without context-aware processing, it might be challenging to precisely extract the topic and sentiment. The words "amazing," "outstanding," and "sleek" suggest positive sentiment, but the exact topic (e.g., the smartphone's camera, design, or overall features) remains somewhat ambiguous. Further, with context-aware processing: the sentence is "Despite the incredible camera quality and sleek design, some users experienced issues with the battery life." In the given example, context-aware semantic processing can consider the surrounding text to extract more precise topics and sentiments. The positive sentiments related to the camera quality and design can be acknowledged. However, the inclusion of "some users experienced issues with the battery life" provides additional context. The information helps in understanding potential drawbacks or challenges associated with the product launch. The topic extraction can include identification of key terms or phrases that indicate the main topics in the text and using context to disambiguate between multiple possible topics and select the most relevant ones. Therefore, the example output can comprise topics such as Smartphone, Camera Quality, Design, Battery Life and the sentiment analysis can analyze sentiment-bearing words and phrases to determine the overall sentiment.

[0033] In an embodiment, the system 100 can consider the context to understand whether sentiment expressions are positive, negative, or neutral. For example, the sentiment can be Positive (for Camera Quality and Design), mixed (due to reported issues with Battery Life) and can also combine topic and sentiment information to provide a more nuanced understanding of the text, while recognizing that sentiments may vary depending on the context or specific aspects of the topic. Sample output will be, "Despite some users experiencing issues with the battery life, the new smartphone impresses with its amazing camera quality and sleek design." By leveraging context-aware semantic processing, the analysis takes into account the interplay between different topics and sentiments in the text.The approach enhances the precision and depth of topic extraction and sentiment analysis, providing a more accurate representation of the information conveyed in the content.

[0034] In an embodiment, the evaluation parameters in the reinforcement learning can include performance metrics such as page load time for website ranking, a server response time, navigation ease, mobile responsiveness, relevance of content, readability, conversion rate, website analytics, click through rate, keyword optimization and metatags.

[0035] In an embodiment, the system 100 includes a mechanism for simultaneous semantic and visual analysis of textual and multimedia content on websites. The mechanism involves cross-referencing textual and multimedia elements to evaluate effectiveness, employing a sophisticated matrix of semantic similarity and visual feature extraction. It incorporates a hybrid NLP model that combines BERT and DistilBERT architectures for in- depth semantic analysis of website text. The layered approach encompasses sentiment analysis, keyword extraction, and thematic clustering. Furthermore, the method 200 utilizes transfer learning techniques to adapt the model to various content domains, enabling nuanced understanding and interpretation of language across diverse topics, irrespective of the sector.

[0036] In an embodiment, an automated testing module within the system 100 leverages Al to simulate user interactions across various web browsers, with a focus on predictive modeling and anomaly detection algorithms. The system 100 identifies and analyzes discrepancies in website performance and visual rendering across different browsers. It integrates browser rendering engines with an Al-based interpretative layer, facilitating realtime analysis of browser-specificbehaviors and providing adaptive suggestions for compatibility optimizations. By preemptively addressing potential compatibility issues, this system 1 00 enhances the overall user experience across diverse browser environments.

[0037] In an embodiment, the system 100 can identify discrepancies in the websites such as iscrepandcies in how web pages are rendered across different browsers. The system 100 can analyze rendering performance and layout consistency across popular browsers. The system 100 can also check Cascading Style Sheets (CSS) compatibility issues that affect the appearance of the website, while identifying inconsistencies in how CSS styles are applied or interpreted by different browsers. To check for JavaScript compatibility, the system 100 can look for inconsistencies in how JavaScript functions or features are supported across browsers, while analyzing for potential issues related to browser-specific implementations. The features can be compatible across multiple browsers.

[0038] In an embodiment, the system 100 can look for variation in page load times or responsiveness across the different browsers, while monitoring and comparing performancemetrics such as load times, resource loading, and script execution times. The system 100 can also identify and flag known bugs or issues specific to certain browsers and utilize historical data and bug database 118s to predict and identify browser-specific issues.

[0039] In an embodiment, the system 100 can gather data from various browsers, including Chrome, Firefox, Safari, Edge and collect data on user interactions and behaviors across different browsers. The system 100 can also acquire historical data on compatibility issues and resolutions and define features that capture browser-specific attributes (e.g., rendering engine, version, supported features). The system 100 can also extract features related to user behavior and interactions with the website and develop metrics for assessing compatibility challenge. The system 100 can train machine learning models (e.g., binary classifiers) to predict whether a compatibility issue is likely to occur. The system 100 can also utilize regression models for predicting the severity or impact of potential issues on user experience and combine multiple models for improved accuracy.

[0040] In an embodiment, the system 100 can utilize training data and use labeled datasets containing instances of compatibility issues and non-issues across different browsers. For user interaction patterns, the system 100 can include data on user interactions that may indicate browser-specific challenges. The system 100 can utilize supervised learning algorithms and make use of decision trees, random forests, or gradient boosting for classification tasks. The system 100 can choose algorithms like linear regression or ensemble method 200s for predicting severity. For the purpose of cross-validation, the system 100 can also employ techniques like k-fold cross-validation to assess model performance and evaluate models using metrics such as accuracy, precision, recall, Fl -score, or area under the ROC curve.

[0041] The system 100 can implement algorithms for identifying anomalies or deviations from expected behavior and develop algorithms that recommend optimizations based on predicted compatibility challenges. For the purpose of continuous learning, the system 100 can implement continuous learning mechanisms to adapt models over time based on evolving browser standards.

[0042] In an embodiment, the system 100 can include testing frameworks for validation of all kinds of prediction in the workflow. The system 100 can include Application Programming Interface 106s (APIs) or webhooks for developers to integrate compatibility predictions into the workflow. The system 100 can also connect with automated testing frameworks to validate predictions during testing phases.

[0043] In an embodiment, the system 100 identifies the type and version of the web browser being used by website visitors. The system 100 reads the browser details for anticipating potential rendering and functionality variations. The system 100 also utilizes machine learning for predicting potential issues that may arise on specific browsers based on historical data and evolving web standards. Proactive identification can allow for preemptive problem-solving and optimization, ensuring a seamless user experience across different browsers. The system 100 can also employ adaptive learning within neural networks specifically for web content, continuously refining algorithms based on real-time global web communication trends, while ensuring that the system 100 stays up-to-date with the latest browser trends and variations. The system 100 can also use machine learning for proactively identifying and predicting compatibility challenges across diverse web browsers. The system 100 can also predict potential issues before they occur, allowing for proactive optimization.

[0044] In an embodiment, the neural network 112can be integrated into the website evaluation system 100 to enhance the analysis of multimedia content through a sophisticated machine learning model. The machine learning model can incorporate both supervised and unsupervised learning mechanisms, thereby significantly enriching the system’s lOOability to understand and evaluate website content. The supervised learning component of the machine learning model involves training on labeled datasets. The datasets contain examples of multimedia content that are explicitly categorized as relevant or irrelevant to the evaluation criteria. By leveraging supervised learning, the machine learning model becomes adept at classifying multimedia elements based on predefined categories, allowing for a precise assessment of contextual relevance.

[0045] In an embodiment, the unsupervised learning aspect employs sophisticated clustering techniques, allowing the model to identify inherent patterns, relationships, and groupings within multimedia content without relying on predefined labels. An anomaly detection mechanism can enable the neural network 112 to identify anomalies or emerging patterns that might not be explicitly labeled, contributing to a more nuanced understanding of website content. The machine learning model can seamlessly integrate both supervised and unsupervised learning, adapting to scenarios with labeled and unlabeled data. The adaptability enhances the model's versatility in handling various types of multimedia content. The combination of supervised and unsupervised learning mechanisms facilitates comprehensive feature extraction. By leveraging insights from both labeled and unlabeled data, the machine learning model can also capture intricate contextual features, contributing to a more holistic understanding of website content.

[0046] In an embodiment, the neural network 112 can incorporate a feedback loop mechanism that iteratively refines the model's performance based on user interactions and feedback. User feedback can enhance the machine learning model's capabilities in both supervised and unsupervised scenarios, ensuring continuous learning and adaptability to evolving user expectations. The unsupervised learning mechanisms include dimensionality reduction techniques, reducing the complexity of high-dimensional multimedia data for more efficient analysis. The machine learning model can also synergistically analyse textual and visual elements, benefiting from both supervised and unsupervised learning to comprehend the interplay between different modalities in website content.

[0047] In another embodiment, the neural network 112 can adopt a hybrid architecture, seamlessly integrating components optimized for supervised and unsupervised learning tasks. The machine learning model may leverage Convolutional neural Networks (CNNs) for image analysis and Recurrent Neural Networks (RNNs) or textual analysis, combining the strengths of both architectures.

[0048] In an embodiment, a predictive analytics mechanism can employ advanced algorithms and historical data analysis to anticipate and assess potential issues related to responsive design. Responsive design issues may include layout inconsistencies, elements not adapting appropriately to different screen sizes, or challenges in achieving a consistent user experience across various devices. The predictive analytics mechanism can anticipate challenges and evaluates potential issues associated with multimedia playback on the website. It involves predicting problems such as buffering issues, compatibility with different multimedia formats, or discrepancies in how multimedia elements are displayed or played across various browsers and devices. The predictive analysis mechanism can also leverage historical data on website performance, user interactions, and past issues to train machine learning models.

[0049] In an embodiment, the evaluation module 114 assesses various aspects of web content. It performs tasks such as analysing multimedia elements, assessing loading speed, identifying performance bottlenecks, and overall evaluating the website's effectiveness. The evaluation module 114 can incorporate a specialized NLP framework to delve into the semantic aspects of textual content present on the website. The NLP framework is specifically designed for in-depth semantic analysis, aiming to comprehend the meaning, context, and nuances conveyed through the textual components of the web content. The NLP framework can interpret and understand the textual information present on the website. It goes beyond mere keyword recognition, focusing on understanding the contextual relevanceof the language used in different sections of the website. The NLP framework can also leverage advanced semantic analysis techniques to extract meaning from textual elements. It can implement context-aware processing to ensure a nuanced understanding of language that takes into account the specific context of the website and its content. The NLP framework can also seamlessly integrate with the broader tasks performed by the evaluation module 114. By adding semantic analysis to the evaluation process, the system 100 gains a more profound understanding of the textual content and its impact on the overall quality and relevance of the website.

[0050] In an embodiment, the NLP framework can be enriched with advanced Artificial Intelligence (Al) techniques to enhance the interpretability of the results obtained from topic extraction and sentiment analysis. The Al techniques can be strategically incorporated to augment the depth and transparency of the analysis. The NLP framework can also employ explainable Al techniques in the process of topic extraction, which ensures that the steps involved in identifying and extracting topics from multimedia content are transparent and interpretable, allowing stakeholders to understand how conclusions are reached. Al techniques can be utilized in sentiment analysis prioritize the use of transparent models, which ensures that the sentiment analysis results are interpretable, providing insights into the factors influencing the classification of sentiments in multimedia content. The NLP framework incorporates features designed to explain the reasoning behind topic extraction and sentiment analysis. It can contribute to the overall interpretability of the model's decisions, allowing users to grasp the significance of identified topics and sentiments.

[0051] The system 100 can be equipped with interface 106s that present the extracted topics and sentiment analyses in a user-friendly manner. Users, including non-experts, can access and understand the outcomes of the analysis, fostering transparency and usability. The Al techniques incorporated into the NLP framework are context-aware, taking into account the unique characteristics of multimedia content and its contextual relevance. It ensures that the Al-driven analysis aligns with the nuances of the multimedia content being evaluated, resulting in more accurate and contextually relevant interpretations.

[0052] In an embodiment, the NLP framework extracts textual content from various elements of the web, including articles, descriptions, comments, and other text-based components. Text data can undergo preprocessing steps, including tokenization, stemming, and removal of stop words, to prepare it for sentiment analysis. The NLP framework can seamlessly integrate textual content extracted from multimedia elements such as image captions, video descriptions, or audio transcripts. The NLP framework can also enable aholistic analysis that considers both textual content and multimedia elements. The NLP framework can also employ sentiment analysis algorithms to determine the sentiment expressed in the textual content. The algorithms may involve the use of machine learning models, such as Support Vector Machines (SVM), Recurrent Neural Networks (RNN), or Transformers like BERT, depending on the complexity and requirements of the sentiment analysis task.

[0053] In an embodiment, the system 100 can use sentiment analysis models that can be fine-tuned to be context-aware, considering the unique language and expressions found in the specific domain of web content. It ensures that the sentiment analysis is sensitive to nuances, adapting to changes in language use and context over time. In cases where the web content includes governmental language, the sentiment analysis is tailored to understand the sentiment nuances specific to government communications. The NLP framework may also incorporate domain-specific lexicons to capture sentiment expressions unique to governmental language. The NLP framework can integrate explainable Al techniques to provide clear interpretations of sentiment analysis results. The interpretations can be presented in a user-friendly manner, ensuring that users can easily understand the sentiments expressed in the web content.

[0054] PIG. 2 illustrates an exemplary method 200 for website evaluation in accordance with an embodiment of the present disclosure.

[0055] Referring to FIG. 2, an exemplary method 200 for website evaluation is disclosed.

[0056] At step 202, the system 100 can utilize a dedicated crawling module 110 to initiate the crawling process. The crawling module 110 can begin by selecting predefined starting points within the website or by following links from an initial URL. The crawling module 110 can retrieve web pages from the selected starting points. The system 100 can also utilize a traversal algorithm (e.g., breadth-first or depth-first), the module system lOOatically explores the website's structure. As web pages are fetched, the crawling module 110 parses the HTML structure to identify multimedia content. Techniques may include recognizing HTML tags associated with images, videos, audio files, or other multimedia elements. The crawling module 110 extracts multimedia elements such as images, videos, or audio files from the parsed HTML. Relevant metadata, including alt text, captions, or descriptions, is parsed to enrich the understanding of each multimedia asset. The crawling module 110 indexes the extracted multimedia content into an organized database 118. The database 118 can also be structured to efficiently store and manage information related to each multimediaelement. Metadata associated with multimedia content, parsed during the crawling process, is included in the database 118. It enhances the searchability and context-aware analysis of multimedia content during subsequent evaluations.

[0057] In an embodiment, the organized database 118 can maintain a structured representation of multimedia content. It may establish associations and relationships between multimedia elements and the web pages from which they were extracted. The organized database 118 can allow for efficient querying and retrieval of multimedia content based on various criteria. It sets the stage for further analysis, enabling the system 100 to retrieve specific multimedia assets for evaluation. The crawling module 110 can scale with the size of the website, handling a large volume of multimedia content. To maintain an up-to-date database 118, the crawling module 110 may support incremental updates, revisiting previously crawled pages for new or modified multimedia assets. The crawling module 110 can incorporates mechanisms to detect and handle errors, ensuring robustness against issues such as broken links or inaccessible multimedia content. The errors can be logged and reported to facilitate system 100 monitoring and maintenance.

[0058] At step 204, the website evaluation system 100 can incorporate a neural network 112designed for feature extraction and contextual relevance analysis. The neural network 112can serve as a sophisticated tool for understanding and interpreting multimedia content. The multimedia content can be fed into the neural network 112as input data. Within the neural network, there exists a dedicated layer responsible for feature extraction. The neural network 112can extract meaningful features from multimedia elements, capturing visual, textual, and possibly auditory aspects. The neural network 112can also integrate information from multiple modalities, such as images, videos, and textual descriptions, which enables a synergistic understanding of the interplay between different types of content within the multimedia elements. Extracted features can be used to create contextual embeddings, capturing the inherent relationships within and between multimedia elements. The neural network 112can apply semantic analysis techniques to understand the context in which multimedia elements exist. The neural network 112can analyse the hierarchical structure of multimedia content. It evaluates relationships between structural components, such as headers, footers, and embedded multimedia assets.

[0059] The neural network 112can assess each web page independently. It gauges the relevance of multimedia content within the context of each specific page. The neural network 112can also perform a holistic evaluation of the entire web content. It can consider relationships and coherence between multimedia elements across different pages. Extractedfeatures can contribute to the creation of semantic representations for each multimedia asset. The embeddings can serve as the foundation for in-depth analysis of contextual relevance. The neural network 112can employ adaptive learning mechanisms, continuously refining its feature extraction and analysis based on real-time global web communication trends, which ensures adaptive and up-to-date evaluations of multimedia elements in the ever-evolving online landscape.

[0060] In an embodiment, the system 100 can also incorporate a feedback loop that allows users to provide input on the contextual relevance analysis. The user feedback can contribute to the iterative refinement of the neural network 112model, enhancing its ability to understand and evaluate multimedia content.

[0061] At step 206, the system 100 can incorporate a dedicated evaluation module 114 tasked with assessing the performance of the website. The evaluation module 114 can also be triggered manually or operate on a scheduled basis to ensure regular assessments. The evaluation module 114 collects data on the time it takes for individual web pages to load. Page load time can be measured from the initiation of the request to the completion of rendering. The evaluation module 114 can monitor and analyse the time taken by the server to respond to user requests. It can also identify delays in server response, including factors such as server processing time and network latency. The evaluation module 114 scrutinizes the efficiency of loading various resources, such as images, scripts, stylesheets, and other multimedia elements. The evaluation module 114 can also perform a holistic assessment, considering multiple performance metrics simultaneously. It can strike a balance between various elements, acknowledging that the website's overall performance is influenced by the interplay of different factors. The evaluation module 114 may operate in real-time or periodically to provide ongoing insights into website performance. Real-time monitoring may enable the detection of immediate issues that may impact user experience. The evaluation module 114 can also generate comprehensive reports, summarizing key performance metrics and highlighting areas that may require attention.

[0062] In an embodiment, the evaluation module 114 may be configured with predefined benchmarks and performance thresholds. Performance metrics can be compared against these benchmarks, aiding in the identification of performance bottlenecks. If performance bottlenecks are detected, the evaluation module 114 may provide recommendations for resource optimization. Recommendations can include suggestions for optimizing images, compressing scripts, or leveraging browser caching to enhance resource loading efficiency. The evaluation module 114 acknowledges that website performance directly impacts userexperience, and strives to optimize loading speed and overall performance to ensure a seamless and user experience.

[0063] At step 208, the system 100 can analyse performance bottlenecks in a website, specifically related to large image files, excessive scripts, or inefficient code, involves a detailed examination to identify and address issues impacting the site's speed and responsiveness. Regular monitoring of image file sizes can be used on the website. The method 200 includes establishing thresholds for acceptable image sizes, considering user experience and page load times. The system 100 can also implement techniques like lossless or lossy compression to reduce image file sizes. The system 100 may use responsive image techniques to load appropriately sized images based on the user's device. The method 200 may include monitoring the loading of scripts, including JavaScript files, on each web page. The system 100 can also assess the complexity and volume of scripts used across the website. It may include reducing script file sizes by removing unnecessary characters and whitespace and implementing deferred loading for non-essential scripts to prioritize critical content first. The system 100 may also use asynchronous loading for scripts to prevent blocking page rendering.

[0064] In an embodiment, the system 100 may profile the website's code to identify inefficient segments or practices. The system 100 can also correlate code execution times with overall page load times. The system 100 may use optimization strategies for restructuring inefficient code segments to improve execution speed. The system 100 may also employ lazy loading for certain elements to defer their loading until they are needed. The system 100 can optimize server-side code to enhance overall performance.

[0065] In an embodiment, the method 200 may include conducting a comprehensive analysis that considers the interplay of large images, scripts, and code efficiency. The method 200 also includes utilizing performance testing tools to simulate various scenarios and evaluate overall performance while establishing performance budgets to guide optimization efforts and maintain a consistently high-performing websites. The system 100 may implement continuous monitoring to detect and address new performance bottlenecks as the website evolves.

[0066] FIG. 3 illustrates an exemplary computer system for website evaluation, in accordance with an embodiment of present disclosure.

[0067] Referring to FIG. 3, a block diagram of an exemplary computer system 300 is disclosed. The computer system 300 includes input devices 302 connected through I / O peripherals. The system 300 also includes a Central Processing Unit (CPU) 503, and OutputDevices 308, connected through the I / O peripherals. The CPU 303 is also attached to a memory unit 316 along with an Arithmetic and Logical Unit (ALU) 313, a control unit, 312, along with secondary storage devices 310 such as Hard Disks and a Secure Digital Card (SD). The data flow and control flow 306 is indicated by a straight and dashed arrow respectively. The CPU consists of data registers that hold the data bits, pointers, cache, Random Access Memory 104 (RAM), and a main processing unit containing the processing engine. The system 300 also consists of communication buses used to transport the data internally in the system 300.

[0068] In an embodiment, a processor 303 of the system 300 is used for automated review generation and rating recommendation. The system 300 may include more than one processor 303 and communication ports for ease of function. Examples of processors 303 include, but are not limited to, an Intel® 19 Itanium® or Itanium 2 processor (s), or AMD® Opteron® or Athlon MP® processor 102 (s), Motorola® lines of processors, FortiSOC™ system 300 on a chip processor 102 or other future processors. The processor 303 may include various modules associated with embodiments of the present disclosure. The input component can also include communication ports, ethemet ports, gigabit ports, parallel port, or another Universal Serial Bus (USB). The communication port can also be chosen depending on a specific network such as a Wide Area Server (WAN), Local Area Network (LAN), or a Personal Area Network (PAN). The communication port can be a RS-232 port that can be used with the remote dialing and internet connection options of the system 300. A Gigabit port can be used to connect the system 300 to the internet at all times. And the Gigabit port can use copper or fiber for connection.EXAMPLES

[0069] Considering an example, the system 100 of the present disclsoure can be used to assess performance and relevance of government websites. The system 100 can use a custom NLP for analyzing governmental language, dynamic adaptation for evolving standards, and real-time benchmarking for immediate scoring.

[0070] The system 100 of the present disclsorue can also monitor news websites and online media platforms. The NLP framework can be used for sentiment analysis, semantic- visual analysis for comprehensive content understanding, and dynamic adaptation for staying updated on global communication trends in a dynamic manner.

[0071] The system 100 can enhance the performance and content relevance of e-leaming platforms. The system 100 may use custom NLP for analyzing educational content, real-timebenchmarking for continuous improvement, and adaptive learning for staying current with educational trends.

[0072] It is to be appreciated by a person skilled in the art that while various embodiments of the present disclosure have been elaborated for a system 100 and method 200 for website evaluation. However, teachings of the present disclosure are also applicable for other types of applications as well, and all such embodiments are well within the scope of the present disclosure. However, a system and method for website evaluation, and all such embodiments are well within the scope of the present disclosure without any limitation.

[0073] Moreover, in interpreting the specification, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a nonexclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C....and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

[0074] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are comprised to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.ADVANTAGES OF THE PRESENT DISCLOSURE

[0075] The proposed disclosure provides a system that excels in processing and evaluating multimedia elements, offering a comprehensive analysis of images, videos, and other multimedia content on websites.

[0076] The proposed disclosure provides a system that provides actionable insights for strategic decision-making by offering immediate measures of website relevance and effectiveness.

[0077] The proposed disclsoure provides a system that analyses performance bottlenecks, including large image fdes, excessive scripts, and inefficient code.

[0078] The proposed disclosure also provides a system that utilizes machine learning models for proactive identification of potential browser-specific issues.

Claims

We Claim:

1. A system (100) for website evaluation, the system (100) comprising: a processor (102) and a memory (104) comprising instructions that when executed cause the processor (102) to: crawl, with a crawling module (110), a website to parse a multimedia content therein and wherein the crawling module (110) indexes the multimedia content into an organized database (118) for further analysis; extract, with a neural network module (112), features and perform a contextual relevance analysis on the multimedia content comprising structure, pages, and a web content; evaluate, with an evaluation module (114), a loading speed and an overall performance of the website comprising assessing page load times, server response times, and resource loading efficiency; and identify and analyse performance bottlenecks, such as large image files, excessive scripts, or inefficient code in the website.

2. The system (100) as claimed in claim 1, wherein the neural network (112) further comprises a machine learning model comprising a combination of supervised and unsupervised learning mechanisms.

3. The system (100) as claimed in claim 1, wherein the system (100) comprises a predictive analytics mechanism to assess potential issues related to responsive design, multimedia playback, and cross-origin resource sharing.

4. The system (100) as claimed in claim 1, wherein the evaluation module (114) further comprises a Natural Language Processing (NLP) framework for in-depth semantic analysis of the web content.

5. The system (100) as claimed in claim 4, wherein the NLP framework executes a sentiment analysis of the web content and text in the multimedia content.

6. The system (100) as claimed in claim 1, wherein the NLP framework incorporates Artificial Intelligence (Al) techniques to provide interpretable results in topic extraction and sentiment analysis of the multimedia content.

7. A method (200) for website evaluation, the method (200) comprising: crawling, through a crawling module (110), a website to parse a multimediacontent therein and wherein the crawling module (110) indexes the multimedia content into an organized database (118) for further analysis; extracting, through a neural network module (112), features and perform a contextual relevance analysis on the multimedia content comprising structure, pages, and a web content; evaluating, through an evaluation module (114), a loading speed and an overall performance of the website comprising assessing page load times, server response times, and resource loading efficiency; and analysing performance bottlenecks, comprising large image files, excessive scripts, or inefficient code in the website.

8. The method (200) as claimed in claim 7, wherein the crawling module (110) indexes the multimedia content into the organized database (118) based on content type, source, and metadata.

9. The method (200) as claimed in claim 7, wherein the evaluation module (114) employs predictive analytics to assess potential browser-specific issues and proactively identify compatibility challenges across diverse web browsers.

10. The method (200) as claimed in claim 7, wherein the method (200) further comprises dynamically adapting the evaluation criteria and neural network (112) mechanisms based on evolving web standards and user interactions.

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