Natural Language Processing (NLP) Has Witnessed Rapid Advancements In Recent Years, Largely Driven By The Integration Of Deep Learning Techniques. This Survey Explores The Diverse Applications Of Deep Learning In NLP, Highlighting Key Models, Architectures, And Methodologies That Have Transformed The Field. Beginning With Foundational Neural Approaches Such As Word Embeddings And Recurrent Neural Networks, The Survey Traces The Evolution Toward More Advanced Architectures, Including Convolutional Neural Networks, Sequence-to-sequence Models, Attention Mechanisms, And Transformer-based Frameworks. The Applications Span A Wide Range Of Tasks, Such As Machine Translation, Sentiment Analysis, Question Answering, Text Summarization, Speech Recognition, And Dialogue Systems. Moreover, We Examine The Advantages Of Deep Learning In Capturing Semantic Meaning, Context, And Linguistic Nuances Compared To Traditional Statistical Methods. Challenges Such As Interpretability, Data Requirements, Computational Costs, And Ethical Concerns Are Also Discussed. This Survey Provides An Overview Of The Current Landscape Of Deep Learning In NLP While Pointing To Promising Directions For Future Research And Applications.
The Increasing Volume Of Digital And Textual Evidence In Forensic Investigations Necessitates Advanced Techniques For Efficient Information Extraction And Analysis. This Study Explores The Application Of Natural Language Processing (NLP) Methods, Particularly Transformer-based Models, To Automatically Extract Relevant Entities, Relationships, And Events From Forensic Documents. Leveraging The Contextual Understanding Of Transformers, The System Identifies Key Forensic Information With High Accuracy And Reduces Manual Effort In Case Analysis. Extracted Data Is Further Structured Into Knowledge Graphs, Enabling Intuitive Visualization Of Complex Relationships Between Entities Such As Suspects, Locations, Incidents, And Evidence. This Approach Not Only Enhances The Speed And Precision Of Forensic Investigations But Also Facilitates Pattern Recognition, Trend Analysis, And Decision-making. The Integration Of Transformer-based NLP With Graph Visualization Represents A Promising Paradigm For Modernizing Forensic Intelligence And Improving Investigative Outcomes.
In Software Engineering, The Clarity And Consistency Of Requirements Are Crucial For Successful Project Outcomes. However, Natural Language Requirements Are Often Ambiguous, Incomplete, Or Inconsistent, Leading To Costly Errors In Later Development Stages. To Address This, Requirements Templates Are Used To Standardize The Structure And Language Of Requirements. Despite Their Usefulness, Manually Verifying Conformance To These Templates Remains A Time-consuming And Error-prone Task. This Paper Presents An Automated Approach That Leverages Natural Language Processing (NLP) Techniques To Check The Conformance Of Requirements Documents Against Predefined Templates. The Proposed System Employs Syntactic And Semantic Analysis To Identify Structural Inconsistencies, Missing Elements, And Deviations From Standard Phrasing. By Integrating Machine Learning Models And Rule-based Methods, The System Can Adapt To Varying Template Styles And Domain-specific Vocabularies. Experimental Results On Real-world Datasets Demonstrate The Effectiveness Of The Approach In Improving Requirements Quality And Reducing The Manual Effort Required For Validation. This Work Contributes To Enhancing Automation In Requirements Engineering And Supports The Development Of More Reliable Software Systems.
The Rapid Advancement Of Deep Learning In Natural Language Processing (NLP) Has Enabled Recurrent Neural Networks (RNNs), Particularly Long Short-Term Memory (LSTM) Architectures, To Generate Human-like Text Sequences. Despite Their Impressive Fluency, The Statistical Properties Of LSTM-generated Texts Often Diverge From Those Found In Natural Human Language. This Study Investigates The Statistical Features Of LSTM-generated Texts By Examining Linguistic Distributions, Such As Word Frequency, Sentence Length Variability, Entropy Measures, And Zipf’s Law Conformity. Comparative Analysis With Human-authored Corpora Highlights Areas Where LSTM Models Successfully Capture Natural Language Regularities And Where They Fall Short, Such As Long-range Dependencies And Higher-order Semantic Coherence. The Findings Provide Insights Into The Strengths And Limitations Of LSTM-based Text Generation, Offering A Deeper Understanding Of How Statistical Patterns Emerge In Synthetic Language. This Contributes To The Broader Evaluation Of Generative Models And Informs The Development Of More Linguistically Grounded NLP Systems.
The Rapid Progress Of Generative Models Has Enabled The Creation Of Highly Realistic Synthetic Voices, Commonly Known As Audio Deep Fakes. While These Technologies Have Beneficial Applications In Entertainment And Assistive Systems, They Also Pose Significant Risks In Misinformation, Fraud, And Security Breaches. Detecting Audio Deep Fakes Remains A Challenging Task Due To The Increasingly Natural Prosody, Timbre, And Linguistic Coherence Of Synthesized Speech. This Paper Proposes A Multi-stage Framework For Audio Deep Fake Detection That Integrates Complementary Strategies Across Acoustic, Linguistic, And Deep Feature Domains. In The First Stage, Handcrafted Acoustic Features Such As Mel-frequency Cepstral Coefficients (MFCCs) And Spectral Distortions Are Extracted To Capture Low-level Signal Artifacts. The Second Stage Leverages Linguistic Consistency Analysis To Identify Irregularities In Phoneme Duration And Speech Rhythm. Finally, Deep Learning–based Embeddings From Pre Trained Models Are Employed To Capture High-level Semantic And Prosodic Patterns. By Combining These Heterogeneous Feature Spaces Through Ensemble Classification, The Proposed Framework Achieves Robust Performance Against State-of-the-art Synthesis And Voice Conversion Systems. Experimental Results On Benchmark Datasets Demonstrate Improved Generalization Across Multiple Attack Scenarios, Highlighting The Potential Of The Framework As A Practical Tool For Safeguarding Digital Communications Against Audio Forgery.
The Rapid Growth Of Digital Data Across Industries Has Created A Pressing Need For Efficient Methods Of Organizing And Extracting Meaningful Information. Document Classification And Data Extraction Have Emerged As Critical Techniques To Address This Challenge. Document Classification Leverages Machine Learning And Natural Language Processing (NLP) To Automatically Categorize Documents Based On Content, Structure, Or Intent, Enabling Streamlined Information Retrieval And Management. Complementarily, Data Extraction Focuses On Identifying And Retrieving Relevant Entities, Attributes, Or Patterns From Unstructured Or Semi-structured Documents, Transforming Raw Text Into Structured, Usable Datasets. Together, These Processes Enhance Decision-making, Improve Workflow Automation, And Reduce Manual Effort In Domains Such As Healthcare, Finance, Legal Systems, And Enterprise Operations. This Study Explores State-of-the-art Methodologies, Including Deep Learning Models, Rule-based Systems, And Hybrid Approaches, To Improve The Accuracy, Scalability, And Adaptability Of Document Classification And Data Extraction Systems. The Findings Highlight The Potential Of These Techniques To Unlock Hidden Insights, Reduce Information Overload, And Support Intelligent Information Systems In A Data-driven World.
With The Exponential Growth Of User-generated Health-related Content On Online Platforms, Patient Drug Reviews Have Become A Valuable Source Of Real-world Insights Into Drug Effectiveness And Side Effects. Traditional Drug Recommendation Systems Often Rely On Structured Clinical Data, Which May Not Fully Capture Patient Experiences And Sentiments. This Study Proposes A Drug Recommendation System Based On Sentiment Analysis Of Drug Reviews Using Machine Learning (ML) Techniques. The System Leverages Natural Language Processing (NLP) To Analyze Patient Reviews, Extracting Both Sentiment Polarity (positive, Negative, Neutral) And Key Opinion Aspects Related To Drug Efficacy, Safety, And Tolerability. Machine Learning Models Are Trained To Classify Sentiments And Predict Drug Suitability For Specific Conditions. By Integrating Sentiment-driven Insights With Recommendation Algorithms, The System Provides More Personalized And Patient-centric Drug Suggestions. Experimental Results On Benchmark Drug Review Datasets Demonstrate The Effectiveness Of The Proposed Approach In Improving Recommendation Accuracy Compared To Conventional Methods. This Work Highlights The Potential Of Combining Sentiment Analysis And ML To Support Informed Decision-making In Healthcare And Enhance Patient Outcomes.
In The Era Of E-commerce, Vast Amounts Of User-generated Product Reviews Provide Valuable Insights Into Customer Satisfaction And Product Quality. However, Extracting Meaningful Information From Such Unstructured Textual Data Remains A Major Challenge. This Study Focuses On Amazon Product Review Classification Using Natural Language Processing (NLP) Techniques And Logistic Regression As The Classification Model. The Proposed System Preprocesses Raw Textual Reviews Through Tokenization, Stop-word Removal, Stemming/lemmatization, And Feature Extraction Methods Such As Bag-of-Words And TF-IDF. Logistic Regression Is Then Employed To Classify Reviews Into Sentiment Categories (positive Or Negative), Enabling Automated Opinion Mining With High Interpretability. Furthermore, This Work Provides A Review Of Machine Learning-based Performance Prediction Systems, Highlighting The Role Of Various Algorithms In Improving Sentiment Analysis Tasks. By Comparing Logistic Regression With Other ML Models, The Study Emphasizes Its Simplicity, Efficiency, And Robustness In Handling Large-scale Review Data. The Findings Contribute To The Development Of More Reliable Recommendation Systems, Assisting Businesses In Decision-making While Enhancing Customer Experience.
Stone Inscriptions Are A Vital Source Of Historical Knowledge, Particularly In The Tamil Civilization, Which Has Preserved Cultural, Linguistic, And Political Information Through Ancient Scripts. However, These Inscriptions Face Challenges Such As Erosion, Complex Letter Forms, And Limited Accessibility For Modern Readers. This Study Proposes A System For Automatic Recognition And Speech Synthesis Of Ancient Tamil Characters From Stone Inscriptions Using Advanced Computational Techniques. The Methodology Involves Image Preprocessing To Enhance Inscription Clarity, Segmentation To Isolate Characters, And Recognition Using Deep Learning Models Such As Convolutional Neural Networks (CNNs) Trained On Ancient Tamil Script Datasets. Once The Characters Are Identified, They Are Mapped To Their Modern Tamil Equivalents And Converted Into Speechable Audio Using Text-to-Speech (TTS) Technology. This Approach Not Only Preserves Ancient Tamil Heritage But Also Makes Inscriptions More Accessible To Historians, Researchers, And The General Public. The Proposed System Contributes To The Fields Of Optical Character Recognition (OCR), Digital Preservation, And Cultural Informatics, Ensuring That Ancient Tamil Inscriptions Are Both Digitally Archived And Audibly Experienced By Future Generations.
Visual Impairment Poses Significant Challenges To Independent Mobility And Safe Navigation In Daily Life. Traditional Aids Such As White Canes And Guide Dogs Provide Partial Assistance But Lack The Ability To Perceive And Interpret Complex Environments. This Project Proposes A Guidance System Virtual Assistance Device That Integrates Sensors, Computer Vision, And Voice-based Interaction To Assist Visually Impaired Individuals In Real Time. The System Employs Ultrasonic Sensors For Obstacle Detection, A Camera With Deep Learning Models For Object And Path Recognition, And GPS For Outdoor Navigation. A Speech Synthesis Module Provides Audio Feedback To The User, Offering Instructions, Warnings, And Navigation Guidance. The Device Is Designed To Be Lightweight, Portable, And User-friendly, Ensuring Accessibility In Both Indoor And Outdoor Environments. By Combining Smart Sensing, AI-driven Decision-making, And Virtual Assistance, The Proposed System Aims To Enhance Mobility, Safety, And Independence For Visually Impaired Individuals, Ultimately Improving Their Quality Of Life.
Text Extraction From Images Is A Crucial Task In The Field Of Document Digitization, Heritage Preservation, And Information Retrieval. With The Growing Availability Of Optical Character Recognition (OCR) Technologies, It Is Now Possible To Efficiently Convert Printed Or Handwritten Text Into Editable And Searchable Digital Formats. This Project Focuses On Developing A Web-based Application For Extracting Tamil Text From Images Using The Tesseract OCR Engine Integrated With The Flask Framework. The System Preprocesses Input Images Through Techniques Such As Grayscale Conversion, Noise Removal, And Thresholding To Improve Recognition Accuracy. Tesseract OCR Is Then Employed To Recognize And Extract Tamil Characters, Which Are Displayed In A User-friendly Interface Powered By Flask. The Proposed System Enables Users To Upload Images And Retrieve The Extracted Tamil Text In Real Time, Thereby Offering An Effective Solution For Digitizing Documents, Preserving Ancient Scripts, And Improving Accessibility Of Tamil Content. This Approach Demonstrates The Potential Of Combining Open-source OCR Tools With Lightweight Web Frameworks To Create Efficient, Language-specific Text Recognition Systems.
With The Rapid Growth Of Digital Information Processing, Extracting And Understanding Text From Images Has Become An Essential Task In Various Domains Such As Document Digitization, Education, And Multilingual Communication. Optical Character Recognition (OCR) Technology Enables The Automatic Conversion Of Printed Or Handwritten Text In Images Into Machine-readable Formats. This Project Focuses On Implementing An OCR-based System Using The Tesseract Engine To Detect And Extract Text From Images, Followed By Automatic Translation Into The Desired Language. The Methodology Involves Preprocessing Input Images Through Techniques Such As Grayscale Conversion, Noise Removal, And Thresholding To Enhance Accuracy. Once Text Is Extracted Using Tesseract OCR, A Translation Module Is Applied To Convert The Recognized Text Into The Target Language, Enabling Cross-lingual Accessibility. The Proposed System Provides An Efficient And Scalable Solution For Bridging Language Barriers And Making Text-based Information More Accessible For Diverse Users. Applications Of This Work Include Real-time Translation For Travelers, Digitization Of Multilingual Documents, And Assistance For Individuals With Limited Language Proficiency.
The Integration Of Artificial Intelligence (AI) Into Healthcare Has Opened New Opportunities For Delivering Personalized And Accessible Medical Guidance. Ayurveda, One Of The Oldest Traditional Systems Of Medicine, Emphasizes Holistic Well-being Through Natural Remedies, Diet, And Lifestyle Management. This Project Proposes The Development Of An AI-based Ayurvedic Chatbot Designed To Provide Healthcare Support And Personalized Diet Plans Rooted In Ayurvedic Principles. The Chatbot Leverages Natural Language Processing (NLP) To Interact With Users, Analyze Their Health Conditions, And Recommend Ayurvedic Remedies, Lifestyle Changes, And Diet Plans Based On Body Constitution (Prakriti) And Reported Symptoms. Machine Learning Models Are Employed To Enhance Recommendation Accuracy By Learning From User Interactions And Expert-verified Data. The System Also Integrates A Knowledge Base Of Ayurvedic Herbs, Treatments, And Dietary Guidelines, Ensuring Both Accessibility And Reliability. This Approach Enables Users To Receive Instant, Personalized, And Preventive Healthcare Support While Promoting The Traditional Wisdom Of Ayurveda Through Modern Technology. The Proposed Solution Aims To Bridge The Gap Between Ancient Health Practices And Contemporary Digital Healthcare Needs, Offering A Cost-effective, Scalable, And User-friendly Wellness Tool.
The Increasing Demand For Personalized Healthcare Solutions Has Accelerated The Integration Of Artificial Intelligence (AI) And Machine Learning (ML) Into Medical Assistance Systems. This Project Proposes The Development Of A Chatbot For Healthcare And Diet Planning That Leverages Natural Language Processing (NLP) And Machine Learning Techniques To Provide Intelligent, Real-time, And User-friendly Health Support. The Chatbot Is Designed To Interact With Users Through Conversational Interfaces, Collect Basic Health Parameters, Lifestyle Information, And Dietary Preferences, And Subsequently Generate Personalized Recommendations. Machine Learning Models Are Employed To Analyze User Inputs, Classify Health Conditions, And Predict Suitable Diet Plans Tailored To Individual Needs. The System Also Incorporates A Knowledge Base Of Medical Guidelines And Nutritional Data To Ensure Evidence-based Recommendations. By Offering 24/7 Accessibility, Scalability, And Cost-effectiveness, This Chatbot Has The Potential To Support Preventive Healthcare, Promote Healthy Lifestyles, And Reduce The Burden On Medical Professionals. The Proposed Solution Demonstrates The Role Of AI-driven Chatbots As A Valuable Digital Healthcare Assistant For Personalized Wellness Management.
Identity Verification Is A Critical Requirement In Sectors Such As Banking, Government Services, Security, And Digital Onboarding. Manual Extraction Of Information From Identity Cards Is Time-consuming, Error-prone, And Inefficient. To Address This Challenge, Optical Character Recognition (OCR) Integrated With Image Processing Techniques Offers An Automated And Reliable Solution. This Project Proposes The Development Of An Identity Card Recognition System That Captures And Processes Images Of ID Cards To Extract Textual Information Such As Name, Date Of Birth, Address, And Identification Number. The Methodology Involves Preprocessing The Image Using Techniques Like Grayscale Conversion, Noise Reduction, Edge Detection, And Contrast Enhancement To Improve Recognition Accuracy. The Processed Image Is Then Fed Into An OCR Engine, Such As Tesseract, To Convert Printed Or Handwritten Text Into A Machine-readable Format. Post-processing Steps, Including Text Segmentation And Error Correction, Further Refine The Extracted Data. The System Is Designed To Provide Fast, Accurate, And Secure Extraction Of Identity Information, Enabling Seamless Integration With Authentication And Verification Systems. This Approach Enhances Efficiency In Applications Like KYC (Know Your Customer), E-governance, And Digital Record Management While Reducing Manual Effort And Minimizing Fraud Risks.
The Rapid Growth Of Social Media And Online Communication Platforms Has Led To An Increase In Cyberbullying Incidents, Negatively Impacting The Mental Health And Well-being Of Individuals, Especially Adolescents. Detecting Cyberbullying Manually Is Challenging Due To The Massive Volume Of User-generated Content And The Subtlety Of Abusive Language. This Project Proposes An Automated System For Cyberbullying Detection Using Machine Learning Integrated With The Flask Framework For Web-based Deployment. The System Leverages Natural Language Processing (NLP) Techniques To Preprocess Textual Data, Including Tokenization, Stopword Removal, And Vectorization, And Applies Machine Learning Algorithms Such As Logistic Regression, Support Vector Machines, And Random Forest To Classify Content As Bullying Or Non-bullying. The Flask Framework Provides A User-friendly Web Interface For Real-time Input And Detection, Enabling Users To Monitor Social Media Posts Or Messages Efficiently. Experimental Results Demonstrate That The Proposed System Can Accurately Identify Cyberbullying Content, Offering A Scalable And Practical Solution To Mitigate Online Harassment And Promote Safer Digital Interactions.
The Rapid Growth Of Social Media Platforms, Particularly Twitter, Has Resulted In The Generation Of Vast Amounts Of User-generated Content That Reflects Public Opinions, Emotions, And Sentiments On Diverse Topics. Analyzing This Data Provides Valuable Insights For Businesses, Governments, And Researchers In Areas Such As Market Analysis, Policy-making, And Public Opinion Monitoring. This Project Focuses On Sentiment Analysis Of Twitter Data Using A Combination Of Machine Learning Approaches And Semantic Analysis Techniques. The Proposed System Involves Data Collection Through Twitter APIs, Preprocessing Of Textual Data Using Natural Language Processing (NLP) Methods, And The Application Of Supervised Learning Algorithms Such As Naïve Bayes, Support Vector Machines (SVM), And Logistic Regression For Sentiment Classification. Additionally, Semantic Analysis Techniques Are Integrated To Improve Context Understanding And Handle Challenges Such As Sarcasm, Ambiguity, And Slang, Which Are Common In Social Media Text. The Performance Of The Models Is Evaluated Using Metrics Like Accuracy, Precision, Recall, And F1-score To Determine Their Effectiveness. The Integration Of Machine Learning With Semantic Analysis Is Expected To Enhance Sentiment Detection Accuracy And Provide A Deeper Understanding Of Public Sentiment Trends On Twitter.
Online Reviews Have Become An Important Source Of Information For Users Before Making An Informed Purchase Decision. Early Reviews Of A Product Tend To Have A High Impact On The Subsequent Product Sales. In This Paper, We Take The Initiative To Study The Behaviour Characteristics Of Early Reviewers Through Their Posted Reviews On Two Real-world Large E-commerce Platforms, I.e., Amazon And Yelp. In Specific, We Divide Product Lifetime Into Three Consecutive Stages, Namely Early, Majority And Laggards. A User Who Has Posted A Review In The Early Stage Is Considered As An Early Reviewer. We Quantitatively Characterize Early Reviewers Based On Their Rating Behaviours, The Helpfulness Scores Received From Others And The Correlation Of Their Reviews With Product Popularity. We Have Found That (1) An Early Reviewer Tends To Assign A Higher Average Rating Score; And (2) An Early Reviewer Tends To Post More Helpful Reviews. Our Analysis Of Product Reviews Also Indicates That Early Reviewers’ Ratings And Their Received Helpfulness Scores Are Likely To Influence Product Popularity. By Viewing Review Posting Process As A Multiplayer Competition Game, We Propose A Novel Margin-based Embedding Model For Early Reviewer Prediction. Extensive Experiments On Two Different E-commerce Datasets Have Shown That Our Proposed Approach Outperforms A Number Of Competitive Baselines.
Recognizing Ancient Tamil Inscription Characters Enable Archeologists To Reveal Historical Events In Ancient Sri Lanka. Currently, This Is Done By The Archaeology Experts With A Huge Effort. The Inefficiency Of This Manual Procedure Will Negatively Impact On The Future Research In Field Of Archaeology. This Research Involves In Developing An Application With Optical Character Recognition (OCR) Functionality To Recognize Ancient Tamil Inscription. This Paper Focus On The OCR Module Of The Application. OCR Module Comprises Of The Technologies Of Artificial Neural Network (ANN) And Convolutional Neural Network (CNN). Experiments Were Carried Out To Evaluate The Recognition Rate Of The Two OCR Technologies Which Performs On Train Data, Test Data (preprocessed) And Test Data (real Images). After Evaluating Each OCR Solution, CNN Was Selected As The Best Resulted OCR Solution. Lack Of Data Is The Main Limitation Of This Research And It Will Be Highly Impacted On The OCR Accuracy.
In The Modern, Highly Technological World, It Is Acknowledged That People With Visual Impairments, Whose Main Issue Is Social Isolation, Need To Be Able To Live Independently. Visually Impaired People Experience Difficulties And Are At A Disadvantage Because They Lack The Most Visual Information, Which Is The Information They Miss The Most, In Their Environment. Visually Challenged People Can Be Aided With The Use Of Cutting-edge Technology. They Suffer In Strange Circumstances With No Manual Assistance. Since Most Tasks Are Based On Visual Information, People Who Are Blind Are At A Disadvantage Because They Lack The Necessary Information About The Environment. It Is Possible To Extend The Assistance Provided To People With Visual Impairment With The Most Recent Advancements In Inclusive Technology. Using Image And Text Recognition, Machine Learning, And Artificial Intelligence, This Project Aims To Assist Visually Impaired Individuals. The Concept Is Implemented By Means Of A Desktop Application That Focuses On Voice Assistant, Image Recognition, Google Search, To-do List, Weather, Screen Shots, Directions On A Map Chat Bot, Capture Photo, And Other Features. The App Can Assist With Object Recognition And Distance Estimation Using Voice Commands Using YOLOV4 Algorithm. Blind People Will Be Able To Use The Technology's Features And Interact With The Environment In A More Effective Manner Thanks To This Method.
In This Paper We Describe A Methodology And An Algorithm To Estimate The Real-time Age, Gender, And Emotion Of A Human By Analysing Of Face Images On A Webcam. Here We Discuss The CNN Based Architecture To Design A Real-time Model. Emotion, Gender And Age Detection Of Facial Images In Webcam Play An Important Role In Many Applications Like Forensics, Security Control, Data Analysis, Video Observation And Humancomputer Interaction. In This Paper We Present Some Method & Techniques Such As PCA,LBP, SVM, VIOLA-JONES, HOG Which Will Directly Or Indirectly Used To Recognize Human Emotion, Gender And Age Detection In Various Conditions.
Poor Nutrition Can Lead To Reduced Immunity, Increased Susceptibility To Disease, Impaired Physical And Mental Development, And Reduced Productivity. A Conversational Agent Can Support People As A Virtual Coach, However Building Such Systems Still Have Its Associated Challenges And Limitations. This Paper Describes The Background And Motivation For Chatbot Systems In The Context Of Healthy Nutrition Recommendation. We Discuss Current Challenges Associated With Chatbotapplication, We Tackled Technical, Theoretical, Behavioural, And Social Aspects Of The Challenges. We Then Propose A Pipeline To Be Used As Guidelines By Developers To Implement Theoretically And Technically Robust Chatbot Systems
Poor Nutrition Can Lead To Reduced Immunity, Increased Susceptibility To Disease, Impaired Physical And Mental Development, And Reduced Productivity. A Conversational Agent Can Support People As A Virtual Coach, However Building Such Systems Still Have Its Associated Challenges And Limitations. This Paper Describes The Background And Motivation For Chatbot Systems In The Context Of Healthy Nutrition Recommendation. We Discuss Current Challenges Associated With Chatbotapplication, We Tackled Technical, Theoretical, Behavioural, And Social Aspects Of The Challenges. We Then Propose A Pipeline To Be Used As Guidelines By Developers To Implement Theoretically And Technically Robust Chatbot Systems.
Exponential Growth Of Fake ID Cards Generation Leads To Increased Tendency Of Forgery With Severe Security And Privacy Threats. University ID Cards Are Used To Authenticate Actual Employees And Students Of The University. Manual Examination Of ID Cards Is A Laborious Activity, Therefore, In This Paper, We Propose An Effective Automated Method For Employee/student Authentication Based On Analyzing The Cards. Additionally, Our Method Also Identifies The Department Of Concerned Employee/student. For This Purpose, We Employ Different Image Enhancement And Morphological Operators To Improve The Appearance Of Input Image Better Suitable For Recognition. More Specifically, We Employ Median Filtering To Remove Noise From The Given Input Image.
Social Connections Evolved Within Local Cultural Boundaries Suchs As Geosspatials Areas Prior To The Invention Of Informations Communicationstechnologys (ICT). The Recent Advancements In Communication Technologies Have Significantly Surpassed The Old Communications' Time And Spatial Limits. These Social Technologiess Have Ushereds In A News Eras Of User-generated Content, Online Human Networks, And Rich Data On Human Behaviour. However, The Misuses Of Socials Technologiess Such As Socialsmedias (SM) Stages Has Resulted In A New Type Of Online Anger And Violence. This Research Highlights A Novels Ways Of Exhibiting Hostile Conduct On Social Media Websites. The Reasons For Developing Prediction Models To Combat Aggressive Behaviour In SM Are Also Discussed. We Examine Cyberbullyings Predictionsmodelss In Depth And Identify The Major Challenges That Arise While Building Cyberbullying Predictionsmodelssin SM. This Paper Gives A Summarys Of The General Procedure For Detecting Cyberbullying And, More Crucially, The Technique. Despites The Facts That The Data Collecting And Feature Engineering Processes Have Been Detailed, The Focus Is Mostly On Feature Selection Methods And Then The Application Of Various Machine Learning Algorithms To Anticipate Cyberbullyingbehaviours. Finally, The Concerns And Obstacles Have Been Identified, Presenting New Study Directions For Scholars To Investigate.
Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used.
Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used.
Poor Nutrition Can Lead To Reduced Immunity, Increased Susceptibility To Disease, Impaired Physical And Mental Development, And Reduced Productivity. A Conversational Agent Can Support People As A Virtual Coach, However Building Such Systems Still Have Its Associated Challenges And Limitations. This Paper Describes The Background And Motivation For Chatbot Systems In The Context Of Healthy Nutrition Recommendation. We Discuss Current Challenges Associated With Chatbotapplication, We Tackled Technical, Theoretical, Behavioural, And Social Aspects Of The Challenges. We Then Propose A Pipeline To Be Used As Guidelines By Developers To Implement Theoretically And Technically Robust Chatbot Systems.
Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used. Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used.
Prediction Of Academic Performance Of Students Beforehand Provides Scope To Universities To Lower Their Dropout Rate And Help The Students In Improving Their Performance. In This Field, Research Is Being Done To Find Out Which Algorithm Is Best To Use And Which Features Should Be Considered While Predicting The Academic Performance Of Students. This Kind Of Research Work Has Been Increasing Over The Years. This Paper Performs A Survey On The Techniques Used In Various Research Papers For Academic Performance Prediction And Also Point Out The Limitations If Any, In The Methodology Used.
Fake Review Detection And Its Elimination From The Given Dataset Using Different Natural Language Processing (NLP) Techniques Is Important In Several Aspects. In This Article, The Fake Review Dataset Is Trained By Applying Two Different Machine Learning (ML) Models To Predict The Accuracy Of How Genuine Are The Reviews In A Given Dataset. The Rate Of Fake Reviews In Ecommerce Industry And Even Other Platforms Is Increasing When Depend On Product Reviews For The Item Found Online On Different Websites And Applications. The Products Of The Company Were Trusted Before Making A Purchase. So This Fake Review Problem Must Be Addressed So That These Large E-commerce Industries Such As Flipkart, Amazon, Etc. Can Rectify This Issue So That The Fake Reviewers And Spammers Are Eliminated To Prevent Users From Losing Trust On Online Shopping Platforms. This Model Can Be Used By Websites And Applications With Few Thousands Of Users Where It Can Predict The Authenticity Of The Review Based On Which The Website Owners Can Take Necessary Action Towards Them. This Model Is Developed Using ‘NAIVE BAYES’. By Applying These Models One Can Know The Number Of Spam Reviews On A Website Or Application Instantly. To Counter Such Spammers, A Sophisticated Model Is Required In Which A Need To Be Trained On Millions Of Reviews. In This Work ”amazon Yelp Dataset” Is Used To Train The Models And Its Very Small Dataset Is Used For Training On A Very Small Scale And Can Be Scaled To Get High Accuracy And Flexibility.
The Intent Recognition And Natural Language Understanding Of Multi-turn Dialogue Is Key For The Commercialization Of Chatbots.Chatbots Are Mainly Used For The Processing Of Specific Tasks, And Can Introduce Products To Customers Or Solve Related Problems, Thus Saving Human Resources. Text Sentiment Recognition Enables A Chatbot To Know The User’s Emotional State And Select The Best Response, Which Is Important In Medical Care. In This Study, We Combined The Multiturn Dialogue Model And Sentiment Recognition Model To Develop A Chatbot, That Is Designed For Used In Daily Conversations Rather Than For Specific Tasks. Thus, The Chatbot Has The Ability To Provide The Robot’s Emotions As Feedback While Talking With A User. Moreover, It Can Exhibit Different Emotional Reactions Based On The Content Of The User’s Conversation.
Intelligent Personal Assistant (IPA) Is A Software Agent Performing Tasks On Behalf Of An Human Or Individual L Based On Commands Or Questions Which Are Similar To Chat Bots. They Are Also Referred As Intelligent Virtual Assistant Which Interprets Human Speech And Respond Via Synthesized Voices. IPAs And IVAs Finds Their Usage In Various Applications Such As Home Automation, Manage To-do Tasks And Media Playback Through Voice. This Paper Aims To Propose Speech Recognition Systems And Dealing With Creating A Virtual Personal Assistant. The Existing System Serves On The Internet And Is Maintained By The Third Party. This Application Shall Protect Personal Data From Others And Use The Local Database, Speech Recognition And Synthesiser. A Parser Named SURR(Semantic Unification And Reference Resolution) Is Employed To Recognise The Speech. Synthesizer Uses Text To Phoneme. ‘DNN ALGORITHM’ Is Used.
Poor Nutrition Can Lead To Reduced Immunity, Increased Susceptibility To Disease, Impaired Physical And Mental Development, And Reduced Productivity. A Conversational Agent Can Support People As A Virtual Coach, However Building Such Systems Still Have Its Associated Challenges And Limitations. This Paper Describes The Background And Motivation For Chatbot Systems In The Context Of Healthy Nutrition Recommendation. We Discuss Current Challenges Associated With Chatbotapplication, We Tackled Technical, Theoretical, Behavioural, And Social Aspects Of The Challenges. We Then Propose A Pipeline To Be Used As Guidelines By Developers To Implement Theoretically And Technically Robust Chatbot Systems.
Voice Control Is A Major Growing Feature That Change The Way People Can Live. The Voice Assistant Is Commonly Being Used In Smartphones And Laptops. AI-based Voice Assistants Are The Operating Systems That Can Recognize Human Voice And Respond Via Integrated Voices. This Voice Assistant Will Gather The Audio From The Microphone And Then Convert That Into Text, Later It Is Sent Through GTTS (Google Text To Speech). GTTS Engine Will Convert Text Into Audio File In English Language, Then That Audio Is Played Using Play Sound Package Of Python Programming Language.
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