Chatbot For Bag Classification And Product Suggestion Is An Emerging Technology That Can Revolutionize The E-commerce Industry. The Chatbot Interacts With The User In A Conversational Manner And Uses Natural Language Processing And Machine Learning Algorithms To Classify Bags Based On Various Attributes Such As Color, Size, Material, And Style. The Chatbot Can Also Suggest Products Based On The User's Preferences, Purchase History, And Browsing Behavior. The Chatbot Can Enhance The User's Shopping Experience By Providing Personalized Product Recommendations, Reducing The Time And Effort Required To Search For Products, And Improving Customer Engagement And Satisfaction. The Chatbot Can Also Help E-commerce Businesses Streamline Their Inventory Management By Providing Insights Into User Preferences And Behaviour.
One Of The Most Rapidly Spreading Cancers Among Various Other Types Of Cancers Known To Humans Is Skin Cancer. Melanoma Is The Worst And The Most Dangerous Type Of Skin Cancer That Appears Usually On The Skin Surface And Then Extends Deeper Into The Layers Of Skin. However, If Diagnosed At An Early Stage; The Survival Rate Of Melanoma Patients Is 96% With Simple And Economical Treatments. The Conventional Method Of Diagnosing Melanoma Involves Expert Dermatologists, Equipment, And Biopsies. To Avoid The Expensive Diagnosis, And To Assist Dermatologists, The Field Of Machine Learning Has Proven To Provide State Of The Art Solutions For Skin Cancer Detection At An Earlier Stage With High Accuracy. In This Paper, A Method For Skin Lesion Classification And Segmentation As Benign Or Malignant Is Proposed Using Image Processing And Machine Learning. A Novel Method Of Contrast Stretching Of Dermoscopic Images Based On The Methods Of Mean Values And Standard Deviation Of Pixels Is Proposed. Then The OTSU Thresholding Algorithm Is Applied For Image Segmentation. After The Segmentation, Features Including Gray Level Co-occurrence Matrix (GLCM) Features For Texture Identification, The Histogram Of Oriented Gradients (HOG) Object, And Color Identification Features Are Extracted From The Segmented Images. Principal Component Analysis (PCA) Reduction Of HOG Features Is Performed For Dimensionality Reduction. Synthetic Minority Oversampling Technique (SMOTE) Sampling Is Performed To Deal With The Class Imbalance Problem. The Feature Vector Is Then Standardized And Scaled.
Machine Learning Is A Branch Of Artificial Intelligence. In Recent Years, With The Advantages Of Automatic Learning And Feature Extraction, It Has Been Widely Concerned By Academic And Industrial Circles. It Has Been Widely Used In Image And Video Processing, Voice Processing, And Natural Language Processing. At The Same Time, It Has Also Become A Research Hotspot In The field Of Agricultural Plant Protection, Such As Plant Disease Recognition And Pest Range Assessment, Etc. The Application Of Machine Learningin Plant Disease Recognition Can Avoid The Disadvantages Caused By Artificial Selection Of Disease Spot Features, Make Plant Disease Feature Extraction More Objective,and Improve There Search Efficiency And Technology Transformationspeed. This Review Provides There Search Progress Of Machine Learning Technology In The field Of Crop Leaf Disease Identification In Recent Years. In This Paper, We Present The Current Trends And Challenges For The Detection Of Plant Leaf Disease Using Machine Learning And Advanced Imaging Techniques. We Hope That This Work Will Be A Valuable Resource For Researchers Who Study The Detection Of Plant Diseases And Insect Pests. At The Same Time, We Also Discussed Some Of The Current Challenges And Problems That Need To Be Resolved.
Deep Learning Is An Effective And Useful Technique That Has Been Widely Applied In A Variety Of Fields, Including Computer Vision, Machine Vision, And Natural Language Processing. Deepfakes Uses Deep Learning Technology To Manipulate Images And Videos Of A Person That Humans Cannot Differentiate Them From The Real One. In Recent Years, Many Studies Have Been Conducted To Understand How Deepfakes Work And Many Approaches Based On Deep Learning Have Been Introduced To Detect Deepfakes Videos Or Images. In This Paper, We Conduct A Comprehensive Review Of Deepfakes Creation And Detection Technologies Using Deep Learning Approaches. In Addition, We Give A Thorough Analysis Of Various Technologies And Their Application In Deepfakes Detection. Our Study Will Be Beneficial For Researchers In This Field As It Will Cover The Recent State-of-art Methods That Discover Deepfakes Videos Or Images In Social Contents. In Addition, It Will Help Comparison With The Existing Works Because Of The Detailed Description Of The Latest Methods And Dataset Used In This Domain.
Classification Is Now Simpler Thanks To Deep Learning, Bigger Datasets, And More Powerful Computing Power. Convolutional Neural Networks Have Emerged As The Most Popular And Widely Used Method For Categorising Images In Recent Years. Several Transfer Learning Techniques Are Employed In This Article To Categorise Photos From A Dataset Of Indian Food. Everyone Needs To Keep Track Of Their Eating Habits Because Food Is Essential To Life Because It Provides Us With A Range Of Nutrients. Food Classification Is Therefore Essential For A Better Way Of Life. In This Project, Pre-trained Models Are Used Instead Of Conventional Methods For Building A Model From Scratch, Which Reduces Processing Costs And Time While Simultaneously Generating Better Outcomes.
In The Present World, We Have Wide Varieties Of Species And Organisms. This Brings Into Light, The Criticality Of Classification Of Various Tangible Objects. Also, Keeping In Mind, The On-going Research On Genetics And Evolution By Various Scientists Across The World, Discerning The Resemblance Among Different Classes Also Becomes Very Crucial. This Paper Is Based On A Project Which Builds A CNN (Convolutional Neural Network) To Classify Different Dog Breeds. If The Image Of A Dog Is Found, This Algorithm Would Find The Estimate Of The Breed. The Resembling Dog Breed Is Identified If The Image Of A Human Is Supplied. We Have Built A Pipeline To Process Real World Images.
Underwater Fish Detection Plays A Crucial Role In Various Domains, Such As Marine Biology, Environmental Monitoring, And Fisheries Management. Traditional Methods For Fish Detection In Underwater Environments Often Suffer From Limitations In Accuracy And Efficiency. In Recent Years, Deep Learning-based Object Detection Models Have Shown Remarkable Success In Various Computer Vision Tasks. This Study Presents An Underwater Fish Detection System Utilizing The YOLOv8 Architecture, A State-of-the-art Deep Learning Framework
Deep Learning Is A Branch Of Artificial Intelligence. In Recent Years, With The Advantages Of Automatic Learning And Feature Extraction, It Has Been Widely Concerned By Academic And Industrial Circles. It Has Been Widely Used In Image And Video Processing, Voice Processing, And Natural Language Processing. At The Same Time, It Has Also Become A Research Hotspot In The field Of Agricultural Plant Protection, Such As Plant Disease Recognition And Pest Range Assessment, Etc. The Application Of Deep Learning In Plant Disease Recognition Can Avoid The Disadvantages Caused By Artificial Selection Of Disease Spot Features, Make Plant Disease Feature Extraction More Objective, And Improve Their Search Efficiency And Technology Transformation Speed. This Review Provides The Research Progress Of Deep Learning Technology In The field Of Crop Leaf Disease Identification In Recent Years. In This Paper, We Present The Current Trends And Challenges For The Detection Of Plant Leaf Disease Using Deep Learning And Advanced Imaging Techniques. We Hope That This Work Will Be A Valuable Resource For Researchers Who Study The Detection Of Plant Diseases And Insect Pests. At The Same Time, We Also Discussed Some Of The Current Challenges And Problems That Need To Be Resolved.
Hairstyling Is An Art Of Fashion Transformed Since Ancient Era, With The Influences From Many Diverse Factors. It Has Been A Primary Aspect Of Human Lifestyle And Society In Various Different Ways With The Growth Of Research Fields Like Modeling Human, Visual Searching, Visual Matching, Facial Verification For Security Measures And Etc. Perfect Hairstyle Improves Specially A Woman’s Self-confidence. This Paper Presents A Hairstyle Recommendation System Based On Face Shapes And Suitable Hairstyle With Expert’s Knowledge For The Face Shape Derived From Face Shape Classification Algorithm. Different Noise Types And Lighten, Which Have Given Less Accuracy Of Features Landmark Detection. The Images Whose Values Are Confined To Some Specific Range Of Values Only. For Brighter Image Will Have All Pixels Confined High Values. But A Good Image Will Have Pixels From All Regions Of The Image. Therefore, We Applied Histogram Equalization Improves The Contrast Of The Image. The Median Filter Is Used To Remove The Different Types Of Noises.
Underwater Fish Detection Plays A Crucial Role In Various Domains, Such As Marine Biology, Environmental Monitoring, And Fisheries Management. Traditional Methods For Fish Detection In Underwater Environments Often Suffer From Limitations In Accuracy And Efficiency. In Recent Years, Deep Learning-based Object Detection Models Have Shown Remarkable Success In Various Computer Vision Tasks. This Study Presents An Underwater Fish Detection System Utilizing The YOLO Architecture, A State-of-the-art Deep Learning Framework.
One Of The Most Difficult Issues Is Conflict Between Humans And Animals. The Main Reason For Animal Detection, Particularly Wild Animal Infiltration Into Human Habitation, Has Been The Expansion Of Human Settlement Into Forest Areas. The Conflict Between Humans And Animals Is Largely A Result Of The Loss Of Habitat, Food, And Other Necessities For The Animal Kingdom. Many Catastrophic Events In The Future Can Be Prevented By The Early Recognition Of These Conflicts. The Scope Of Earlier Automated Image Detection Processes Aids In Finding A Remedy For These Occurrences. The Detection Can Utilise An Algorithm For Earlier Detection Because The Manual, Traditional Method Is More Difficult. Different Deep Learning Algorithms Help To Identify Invaders Sooner To Prevent Confrontation.
Rice Is One Of The World's Most Important Staple Crops, And The Health Of Rice Plants Is Crucial For Global Food Security. Detecting And Classifying Diseases In Rice Leaves Is Essential To Ensure A Healthy Crop Yield. In This Project, We Propose A System For The Classification Of Rice Leaf Diseases Using Convolutional Neural Networks (CNN) And Transfer Learning. We Aim To Leverage The Power Of Deep Learning And Pre-trained Models To Accurately Identify And Categorize Various Rice Leaf Diseases, Providing Farmers And Agronomists With An Efficient Tool For Early Disease Detection. This Paper Discusses The Existing System's Limitations And Presents The Advantages Of Our Proposed Approach.
Potato Is One Of The Prominent Food Crops All Over The World. In Some Places Potato Cultivation Has Been Getting Remarkable Popularity Over The Last Decades. Many Diseases Affect The Proper Growth Of Potato Plants. Noticeable Diseases Are Seen In The Leaf Region Of This Plant. Two Common And Popular Leaf Diseases Of The Potato Plants Are Early Blight (EB) And Late Blight (LB). However, If These Diseases Were Identified At An Early Stage It Would Be Very Helpful For Better Production Of This Crop. To Solve This Problem By Detecting And Analyzing These Diseases Image Processing Is The Best Option. This Paper Proposes An Image Processing And Deep Learning-based Automatic System That Will Identify And Classify Potato Leaf Diseases. In This Paper, Image Segmentation Is Done Over 152 Images Of Healthy And Diseased Potato Leaf, Which Is Taken From Publicly Available Plant Village Database And Seven Classifier Algorithms Are Used For Recognition And Classification Of Diseased And Healthy Leaves. Among Them, The Convolutional Neural Network Gives An Accuracy Of 97%. In This Manner, Our Proposed Approach Leads To A Path Of Automatic Plant Leaf Disease Detection.
Developing An Android Application For Character Recognition To Read The Text From An Image Is A Big Area Of Research. Nowadays, There Is A Trend Of Storing Information From The Handwritten Documents For Future Use. A Simple Way To Store The Information Is Image Capturing Of The Handwritten Document And Save It In Image Format. The Method To Transform Handwritten Data Into Electronic Format Is ‘Optical Character Recognition’. It Involves Several Steps Including Pre-processing, Segmentation, Feature Extraction And Post-processing. Many Researchers Have Been Used OCR For Recognizing Character. This System Uses The Android Phone To Capture The Image Of The Document And Further Steps Are Done By OCR. The Main Challenge Is To Recognize The Characters From Different Styles Of Handwriting. Thus, A System Is Designed That Recognizes The Handwritten Data To Obtain An Editable Text. The Output Of This System Depends Upon The Data That Has To Be Written By The Writer. Our System Offers 90% Accuracy For Handwritten Documents And Gives The Easiest Way To Edit Or Share The Recognized Data.
The World Is Advancing Towards An Autonomous Environment At A Great Pace And It Has Become A Need Of An Hour, Especially During The Current Pandemic Situation. The Pandemic Has Hindered The Functioning Of Many Sectors, One Of Them Being Road Development And Maintenance. Creating A Safe Working Environment For Workers Is A Major Concern Of Road Maintenance During Such Difficult Times. This Can Be Achieved To Some Extent With The Help Of An Autonomous System That Will Aim At Reducing Human Dependency. In This Paper, One Of Such Systems, A Pothole Detection And Dimension Estimation, Is Proposed. The Proposed System Uses A Deep Learning Based Algorithm YOLO (You Only Look Once) For Pothole Detection. Further, An Image Processing Based Triangular Similarity Measure Is Used For Pothole Dimension Estimation. The Proposed System Provides Reasonably Accurate Results Of Both Pothole Detection And Dimension Estimation. The Proposed System Also Helps In Reducing The Time Required For Road Maintenance. The System Uses A Custom Made Dataset Consisting Of Images Of Water-logged And Dry Potholes Of Various Shapes And Sizes.
In This Paper, Our Main Aim Is To Create A Medicinal Plant Identification System Using Deep Learning Concept. This System Will Classify The Medicinal Plant Species With High Accuracy. Identification And Classification Of Medicinal Plants Are Essential For Better Treatment. In This System We Are Going To Use Five Different Indian Medicinal Plant Species Namely Alstonia, Chinar, And Jamun (Naval)l. We Utilize Dataset Contains 58,280 Images, Includes Approximately 10,000 Images For Each Species. We Use Leaf Texture, Shape, And Color, Physiological Or Morphological As The Features Set Of The Data. The Data Are Collected By Us. We Use CNN Architecture To Train Our Data And Develop The System With High Accuracy. Several Model Architectures Were Trained, With The Best Performance Reaching A 95.67% Success Rate In Identifying The Corresponding Medicinal Plant. The Significantly High Success Rate Makes The Model A Very Useful Advisory Or Early Warning Tool.
The Leather Industry Plays A Crucial Role In Fashion, Manufacturing, And Luxury Goods, Where The Quality Of Leather Products Is Of Paramount Importance. Ensuring The Absence Of Defects In Leather Is A Fundamental Aspect Of Maintaining Product Quality. In This Context, We Present A Cutting-edge Approach To Leather Defect Detection Using Deep Learning Techniques. Our Research Leverages The Power Of Deep Neural Networks, Specifically Convolutional Neural Networks (CNNs), To Develop A Robust And Efficient System For Identifying And Classifying Defects In Leather Hides. We Employ State-of-the-art Image Analysis Methods To Process High-resolution Images Of Leather Surfaces, Enabling The Automated Detection Of Various Types Of Defects, Including Scars, Blemishes, Wrinkles, And Discolorations. This Paper Outlines The Design, Training, And Evaluation Of The Deep Learning Model, Highlighting Its Ability To Accurately And Rapidly Detect Defects With High Precision And Recall. The Proposed System Holds The Potential To Revolutionize Quality Control Processes Within The Leather Industry, Improving Product Quality, Reducing Waste, And Enhancing Overall Efficiency. As The Demand For High-quality Leather Products Continues To Grow, Our Work Demonstrates The Potential Of Deep Learning To Advance Quality Control And Defect Detection In This Industry, Ultimately Contributing To The Production Of Flawless Leather Goods.
The World Is Advancing Towards An Autonomous Environment At A Great Pace And It Has Become A Need Of An Hour, Especially During The Current Pandemic Situation. The Pandemic Has Hindered The Functioning Of Many Sectors, One Of Them Being Road Development And Maintenance. Creating A Safe Working Environment For Workers Is A Major Concern Of Road Maintenance During Such Difficult Times. This Can Be Achieved To Some Extent With The Help Of An Autonomous System That Will Aim At Reducing Human Dependency. In This Paper, One Of Such Systems, A Pothole Detection And Dimension Estimation, Is Proposed. The Proposed System Uses A Deep Learning Based Algorithm YOLO (You Only Look Once) For Pothole Detection. Further, An Image Processing Based Triangular Similarity Measure Is Used For Pothole Dimension Estimation. The Proposed System Provides Reasonably Accurate Results Of Both Pothole Detection And Dimension Estimation. The Proposed System Also Helps In Reducing The Time Required For Road Maintenance. The System Uses A Custom Made Dataset Consisting Of Images Of Water-logged And Dry Potholes Of Various Shapes And Sizes.
Images Captured In Low-light Conditions Are Often Disturbed By Low-light, Blur And Noise. Most Of The Conventional Image Enhancement Methods Are Less Robust Without Considering The Effectiveness Of The Blur And Noise. To Enhance Image Equality Under The Complex Environment, We Propose A Novel Image Enhancement Method Based On Joint Generative Adversarial Network (GAN) And Image Quality Assessment (IQA) Techniques. GAN Can Be Well Used For Image Enhancement In Low-light Case, But It Is Not Robust In Blur And Noise Case. IQA Method Uses CNN To Evaluate Each Enhanced Image Quality Based On Some Scores That Correlates Well With The Human Perception. The Scores Can Guide The GAN Learning For Further Enhancing The Image Quality. Instead Of L2-term Loss Function, We Define A Multi-term Loss Function For Its Minimization To Create A Good Image Estimate. Experimental Results Demonstrate The Proposed Method Is More Effective Than Current State-of-art Methods In Terms Of The Quantitative And Qualitative Evaluation.
Detection Of Diseases In Plants Is A Significant Task That Has To Be Done In Agriculture. This Is Something On Which The Economy Profoundly Depends. Infection Discovery In Plants Is A Significant Job In The Agribusiness Field, As Having Diseases In Plants Is Very Common. To Recognize The Diseases In Leaves, A Continuous Observation Of The Plants Is Required. This Observation Or Continuous Monitoring Of The Plants Takes A Lot Of Human Effort And It Is Time-consuming Too. To Make It Simply Some Sort Of Programmed Strategy Is Required To Observe The Plants. Program Based Identification Of Diseases In Plants Makes Easier To Detect The Damaged Leaves And Reduces Human Efforts And Time-saving. The Proposed Algorithm Distinguishing Sickness In Plants And Classify Them More Accurately As Compared To Existing Techniques.
Dogs Are One Of The Most Common Domestic Animals. Due To A Large Number Of Dogs, There Are Several Issues Such As Population Control, Decrease Outbreak Such As Rabies, Vaccination Control, And Legal Ownership. At Present, There Are Over 180 Dog Breeds. Each Dog Breed Has Specific Characteristics And Health Conditions. In Order To Provide Appropriate Treatments And Training, It Is Essential To Identify Individuals And Their Breeds. The Paper Presents The Classification Methods For Dog Breed Classification Using Two Image Processing Approaches 1) Conventional Based Approaches By Local Binary Pattern (LBP) And Histogram Of Oriented Gradient (HOG) 2) The Deep Learning Based Approach By Using Convolutional Neural Networks (CNN) With Transfer Learning. The Result Shows That Our Retrained CNN Model Performs Better In Classifying A Dog Breeds. It Achieves 96.75% Accuracy Compared With 79.25% Using The HOG Descriptor.
Detection Of Diseases In Plants Is A Significant Task That Has To Be Done In Agriculture. This Is Something On Which The Economy Profoundly Depends. Infection Discovery In Plants Is A Significant Job In The Agribusiness Field, As Having Diseases In Plants Is Very Common. To Recognize The Diseases In Leaves, A Continuous Observation Of The Plants Is Required. This Observation Or Continuous Monitoring Of The Plants Takes A Lot Of Human Effort And It Is Time-consuming Too. To Make It Simply Some Sort Of Programmed Strategy Is Required To Observe The Plants. Program Based Identification Of Diseases In Plants Makes Easier To Detect The Damaged Leaves And Reduces Human Efforts And Time-saving. The Proposed Algorithm Distinguishing Sickness In Plants And Classify Them More Accurately As Compared To Existing Techniques.
Historical Photographs Record The True Face Of A Moment In The Development Of Human History; They Have Authenticity, Vividness, And Unique Values. However, Due To Various Factors, Aging And Damage Will Occur. With The Development Of Computer Technology, The Restoration Technology Is More Used In Photo Restoration And Virtual Restoration Of Cultural Relics. This Paper First Analyzes The Principle Of Repairing Photo Archives Based On Computer Technology, And Then Uses The Combination Of Statistics And Computer Image Processing Technology To Detect And Repair The Scratches In Historical Photographs. And The Paper Establishes A Model Repair Framework, Which Provides A New Idea For The Repair Of Such Historical Photos. The Experimental Results Show That The Method Has A Significant Repair Effect.
One Of The Most Rapidly Spreading Cancers Among Various Other Types Of Cancers Known To Humans Is Skin Cancer. Melanoma Is The Worst And The Most Dangerous Type Of Skin Cancer That Appears Usually On The Skin Surface And Then Extends Deeper Into The Layers Of Skin. However, If Diagnosed At An Early Stage; The Survival Rate Of Melanoma Patients Is 96% With Simple And Economical Treatments. The Conventional Method Of Diagnosing Melanoma Involves Expert Dermatologists, Equipment, And Biopsies. To Avoid The Expensive Diagnosis, And To Assist Dermatologists, The Field Of Machine Learning Has Proven To Provide State Of The Art Solutions For Skin Cancer Detection At An Earlier Stage With High Accuracy. In This Paper, A Method For Skin Lesion Classification And Segmentation As Benign Or Malignant Is Proposed Using Image Processing And Machine Learning. A Novel Method Of Contrast Stretching Of Dermoscopic Images Based On The Methods Of Mean Values And Standard Deviation Of Pixels Is Proposed. Then The OTSU Thresholding Algorithm Is Applied For Image Segmentation.
Over The Last Decades, The Incidence Of Skin Cancer, Melanoma And Non-melanoma, Has Increased At A Continuous Rate. In Particular For Melanoma, The Deadliest Type Of Skin Cancer, Early Detection Is Important To Increase Patient Prognosis. Recently, Deep Neural Networks (DNNs) Have Become Viable To Deal With Skin Cancer Detection. In This Work, We Present A Smartphone-based Application To Assist On Skin Cancer Detection. This Application Is Based On A Convolutional Neural Network (CNN) Trained On Clinical Images And Patients Demographics, Both Collected From Smartphones. Also, As Skin Cancer Datasets Are Imbalanced, We Present An Approach, Based On The Mutation Operator Of Differential Evolution (DE) Algorithm, To Balance Data.In This Sense, Beyond Provides A flexible Tool To Assist Doctors On Skin Cancer Screening Phase, The Method Obtains Promising Results With A Balanced Accuracy Of 85% And A Recall Of 96%. Index Terms Skin Cancer Detection, Smartphone Application, Deep Learning, Convolutional Neural Network.
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