Resume Screening Is The Process Of Analysing The Resumes Where The Candidates Apply For The Different Types Of Jobs Where The Company Feel The Tedious Job To Find The Appropriate Candidate Due To The Complexity In Resumes Formats Since It Has Different Styles. As A Result, Selecting Applicants For The Appropriate Job Within A Company Is A Difficult Task For Recruiters. We Can Extract The Key Information From The CV Using NLTK, Natural Language Processing (NLP) Techniques To Save Time And Effort. This System Could Work With A Large Number Of Resumes For Classifying The Right Categories Using KNN Algorithm. Furthermore, This System Attempts To Find The Accuracy And Performance Of The Proposed Methodology And Incorporate It In The IT Firms And Other Regulations For The Prevention Of Manual Screening And Establish A Safe Allocation Of Resources For The Companies. As Such Companies Emerge Even For Them Manually Going Through All Of The Resume Of Candidates Is Very Time Consuming And Tedious So These Talent Acquisition Companies Use Various Machine Learning Models To Filter Out Top Resumes According To The Job Roles, Which Reduces The Efforts For The Human Resource Team.
In This Project, We Present A Novel Web Application That Harnesses The Power Of Natural Language Processing And Deep Learning To Analyze Emotions In Textual Input And Provide Personalized Music Recommendations Based On The Detected Mood. The Main Goal Of This Project Is To Create An Interactive And Engaging Platform That Enhances User Experience By Understanding Their Emotions And Offering Appropriate Music Selections In Real-time. The Core Component Of Our System Is An Emotion-driven Chatbot, Which Utilizes The Long Short-Term Memory (LSTM) Algorithm, A Type Of Recurrent Neural Network, To Process And Interpret Textual Inputs. The LSTM Model Is Trained On A Diverse Dataset Of Text-emotion Pairs To Learn Patterns And Correlations Between Specific Emotions And Corresponding Linguistic Cues. By Employing LSTM, The Chatbot Can Accurately Identify The Underlying Emotions Expressed In User-provided Text. Upon Receiving A Textual Input From The User, The Chatbot Processes The Text And Extracts The Associated Emotions, Which Are Then Used To Recommend Songs That Align With The User's Current Mood. These Song Suggestions Are Retrieved From An Extensive Music Database, Curated To Cater To Various Emotions And Moods. The Music Recommendation Process Leverages Content-based Filtering Techniques To Ensure That The Offered Songs Resonate With The User's Emotional State.
In Today's World, People Are Having Very Tight Schedules Due To The Changes In Their Lifestyles And Work Commitments. But It Requires Regular Physical Activity To Stay Fit And Healthy. People Do Not Concentrate On Their Food Habits, Leading To Obesity. Obesity Is Becoming A Major And Common Problem In Today’s Lifestyle. This Leads People To Choose Their Diet And Do An Equal Amount Of Exercise To Stay Fit And Healthy. The Main Part Here Is People Should Have Adequate Knowledge About Their Calorie Intake And Burn, Keeping A Track Of Their Calorie Intake Is Easy As It's Available On The Product Label Or On The Internet. Keeping Track Of Calories Burnt Is A Difficult Part As There Are Very Few Devices For That. Calories Burned By An Individual Are Based On MET Charts And Formulas. The Main Agenda Of This Study Is A Prediction Of The Burnt Calories With The Help Of An XG Boost Regression Model As The ML (machine Learning) Algorithm To Show Accurate Results. The Model Is Fed With More Than 15,000 Data And Its Mean Absolute Error Is 2.7 Which Will Become Better Over Time By Feeding The XG Boost Regression Model With More Data.
Crop Yield Prediction And Crop Recommendation As Plays A Crucial Role In Agricultural Decision-making Processes, Enabling Farmers To Optimize Resource Allocation And Plan For Potential Risks. In Recent Years, Machine Learning Algorithms Have Emerged As Powerful Tools For Predicting Crop Yields Accurately. This Abstract Focuses On The Application Of The Decision Tree Algorithm To Train For Crop Yield Prediction. Once The Decision Tree Model Is Constructed, It Can Be Used To Predict Crop Yields For Unseen Data. New Input Variables, Such As Weather Forecasts Or Soil Measurements, Can Be Fed Into The Model To Obtain Yield Predictions And Crop Recommend. The Interpretability Of Decision Trees Allows Farmers To Understand Which Factors Contribute Most Significantly To Crop Yield Variations And Make Informed Decisions Accordingly. User Can Interact With The Chat Bot To Give Details Then Model Is Predicted Crop And Reply By Chat Bot.
Credit Scoring Is A Way Of Analyzing Statistical Data Used In Financial Organizations And Banks To Acquire A Persons Creditworthiness. The Best Owers Generally Manipulate It To Decide To Widen Or Retract Credit. The Score Plays A Significant Role In Determining The Creditworthiness Of A Person And If He/she Can Be Sanctioned A Loan Or Not. Machine Learning Techniques Help Us To Predict The Credit Score More Accurately Using Classification Algorithms. Few Base And Ensemble Classification Algorithms Were Used In This Research To Perform A Comparative Analysis. To Achieve Better Results. The Objective Of This Paper Is To Predict The Credit Score Based On Different Classifier Models And Evaluate The Performance Of Each Model Based On The Metrics. A Comparative Analysis Is Done To Identify The Best Classifier To Predict The Credit Score. The Evaluation Metrics Used For Evaluating The Model Are Recall, Precision, Fmeasure, And Accuracy. This Helps Us To Improve The Decision In Identifying The More Accurate Classifier Model. The Dataset Used For This Analysis Is The Credit Dataset From The Machine Learning Repository. Experimental Results Prove That The K-nearest Neighbor And Extratree Classifier Model Produces Better Accuracy In Ensemble SMOTE Classifiers And The 95% Better Accuracy In The Base Classifier.
In The Fast-paced And Demanding World Of Information Technology (IT), The Well-being Of Employees Is Of Paramount Importance. Recognizing And Addressing Stress In IT Employees Is Crucial For Both Individual Health And Overall Organizational Performance. This Project Endeavours To Provide A Reliable And Efficient Solution For The Early Detection Of Stress In IT Employees Through The Analysis Of Text Data. The Primary Objective Of This Study Is To Develop A Stress Detection System Using The Random Forest Algorithm, Which Has Demonstrated Exceptional Performance In The Field Of Machine Learning. These Features Encompass A Wide Range Of Text-based Attributes, Including Sentiment Analysis, Topic Modelling, And Linguistic Markers Associated With Stress And Well-being. Through A Rigorous Process Of Data Pre-processing, Feature Engineering, And Model Training, We Achieve An Impressive 99% Accuracy In Stress Detection.
Water Quality Prediction Is A Research Hotspot In The Field Of Ecological Environment, Which Is Of Great Significance To The Prevention Of Water Pollution And The Construction Of Automatic Water Quality Monitoring Network. The Accuracy Of Prediction Model Results Will Affect The Scientificity And Correctness Of Applied Engineering Projects, As Well As The Accuracy Of Water Pollution Control Measures. Firstly, The Background Of Water Quality Prediction And The Development And Research Trends Of Water Quality Models At Home And Abroad Are Systematically Introduced. Then, The Water Quality Prediction Method Based On Machine Learning Is Mainly Introduced, Focusing On Time Series Prediction Method, Regression Analysis Method, Neural Network Method And Combination Prediction Method. The Applicability And Limitations Of The Model Are Analyze Respectively. Finally, According To The Research History And Present Situation Of Water Quality Prediction Model, The Development Trend Of Water Quality Prediction Model Is Prospected.
Customer Segmentation Is A Crucial Strategy For Businesses That Want To Better Understand Their Customers And Tailor Their Marketing Efforts To Meet Their Specific Needs And Preferences. In The Case Of Product Segmentation, Businesses Can Use Customer Segmentation Techniques To Identify Groups Of Customers Will Separate 4 Category How Is Pay And What He/she Can Buy The Product To Suggest. The K-means Algorithm Works By Partitioning The Customer Data Into K Clusters, Where Each Cluster Represents A Unique Group Of Customers With Similar Attributes For A Specific Product. The Algorithm Iteratively Assigns Each Customer To A Cluster Based On The Distance Between Their Attributes And The Cluster Centroid. The Centroid Is The Average Value Of All Attributes In The Cluster, Which Represents The Center Of The Group. Customer Segmentation Using K-means Clustering For Product Segmentation Has Several Benefits, Including Improved Product Targeting, Personalized Marketing, And Better Customer Experience. By Dividing The Customer Base Into Distinct Groups, Businesses Can Tailor Their Product Offerings And Marketing Strategies To Meet The Specific Needs And Preferences Of Each Group
Demand Forecasting For Products Is A Critical Aspect Of Supply Chain Management And Business Planning. It Involves Predicting The Future Demand For A Product Based On Historical Sales Data, Market Trends, And Other Relevant Factors. Accurate Demand Forecasting Can Help Businesses Optimize Their Inventory Levels, Production Schedules, And Pricing Strategies, While Reducing Waste And Improving Customer Satisfaction. The Main Concepts And Techniques Involved In Demand Forecasting For Products. We Will Discuss The Importance Of Demand Forecasting, The Different Types Of Forecasting Methods, And The Factors That Influence Demand. We Will Also Explore The Challenges And Limitations Of Demand Forecasting, As Well As Best Practices For Improving The Accuracy And Reliability Of Forecasts.
Blood Donation Saves Lives Every Day At Various Situations. A Blood Transfusion May Give Them The Energy To Spend Time With Family And Friends. Blood Cannot Be Manufactured It Can Only Come As A Gift From People. One Person Only Allowed 6 Pints Of Blood Donation. One Pint Of Blood Can Save Upto 33 Lives The Number Of Blood Donor Is Very Less When Compared With Other Countries. Here We Propose A New And Efficient Way To Overcome Such Outline. When We Just Touch The Button Donor The App Will Be Ask You To Enter An Individual's Details Like Name, Phone Number, Age, Date Of Birth, Blood Group, Address Etc. At The Emergency Time Of Blood Needed We Can Check For Blood Donor. Once The App User Enter The Blood Group Which He/she Needed It Will Automatically Show The Donor And You Will Find Nearby Donor. Blood Donation App Provider List Of Donor In Your City/area.
In This Research, An Inference System Of Fuzzy Mamdani System Have Been Designed For The Analysis Of Teaching Skills Of Faculties In Academic Or Educational Institutes. Nowadays, Various Academic Institutions Have Started Web-based Techniques To Collect Students’ Feedback Of Faculty Teaching Performance. The Performance Evaluation Of Faculty In Teaching Activities Performance Is A Major Key To Build A Fairer Academic Institute. Major Purpose Of Faculty Performance Is To Identify Strength & Weakness Of Professional Development Of A Teacher. This Soft Computing Technique By Using Fuzzy Mamdani Inference System (FMIS) For Evaluating Faculty Teaching Performance Will Be Useful For Management Of Organization For Evaluate Faculty Abilities With Student Outcomes.
Agriculture Plays A Pivotal Role In Ensuring Food Security And Sustaining Livelihoods Globally. Soil Quality Is A Fundamental Determinant Of Agricultural Productivity, And Precise Soil Analysis Is Essential For Optimizing Crop Selection And Cultivation Practices. This Paper Presents An Innovative Approach To Soil Analysis And Crop Recommendation Using Long Short-Term Memory (LSTM) Algorithms With Soil Image Data. Traditionally, Soil Analysis Involves Time-consuming And Costly Laboratory Tests, Making It Challenging For Farmers To Access Real-time Information About Their Soil Quality. In This Study, We Propose A Non-invasive And Efficient Method That Leverages Soil Image Data Collected Through Remote Sensing And Drone Technology. These Images Capture Crucial Information About Soil Properties, Such As Texture, Moisture, And Nutrient Levels.
Technology Has Boosted The Existence Of Humankind The Quality Of Life They Live. Every Day We Are Planning To Create Something New And Different. We Have A Solution For Every Other Problem We Have Machines To Support Our Lives And Make Us Somewhat Complete In The Banking Sector Candidate Gets Proofs/ Backup Before Approval Of The Loan Amount. The Application Approved Or Not Approved Depends Upon The Historical Data Of The Candidate By The System. Every Day Lots Of People Applying For The Loan In The Banking Sector But Bank Would Have Limited Funds. In This Case, The Right Prediction Would Be Very Beneficial Using Some Classes-function Algorithm. An Example The Logistic Regression, Random Forest Classifier, Support Vector Machine Classifier, Etc. A Bank's Profit And Loss Depend On The Amount Of The Loans That Is Whether The Client Or Customer Is Paying Back The Loan. Recovery Of Loans Is The Most Important For The Banking Sector. The Improvement Process Plays An Important Role In The Banking Sector. The Historical Data Of Candidates Was Used To Build A Machine Learning Model Using Different Classification Algorithms. The Main Objective Of This Paper Is To Predict Whether A New Applicant Granted The Loan Or Not Using Machine Learning Models Trained On The Historical Data Set.
We Propose To Implement A House Price Prediction Model. It’s A Machine Learning Model Which Integrates Data Science And Web Development. Housing Prices Fluctuate On A Daily Basis And Are Sometimes Exaggerated Rather Than Based On Worth. The Major Focus Of This Project Is On Predicting Home Prices Using Genuine Factors. Here, We Intend To Base An Evaluation On Every Basic Criterion That Is Taken Into Account When Establishing The Pricing. The Goal Of This Project Is To Learn Python And Get Experience In Data Analytics, Machine Learning, And AI.
Customer Segmentation Is A Crucial Strategy For Businesses That Want To Better Understand Their Customers And Tailor Their Marketing Efforts To Meet Their Specific Needs And Preferences. In The Case Of Product Segmentation, Businesses Can Use Customer Segmentation Techniques To Identify Groups Of Customers Will Separate 4 Category How Is Pay And What He/she Can Buy The Product To Suggest. The K-means Algorithm Works By Partitioning The Customer Data Into K Clusters, Where Each Cluster Represents A Unique Group Of Customers With Similar Attributes For A Specific Product. The Algorithm Iteratively Assigns Each Customer To A Cluster Based On The Distance Between Their Attributes And The Cluster Centroid. The Centroid Is The Average Value Of All Attributes In The Cluster, Which Represents The Center Of The Group. Customer Segmentation Using K-means Clustering For Product Segmentation Has Several Benefits, Including Improved Product Targeting, Personalized Marketing, And Better Customer Experience. By Dividing The Customer Base Into Distinct Groups, Businesses Can Tailor Their Product Offerings And Marketing Strategies To Meet The Specific Needs And Preferences Of Each Group.
Crop Yield Prediction And Crop Recommendation As Plays A Crucial Role In Agricultural Decision-making Processes, Enabling Farmers To Optimize Resource Allocation And Plan For Potential Risks. In Recent Years, Machine Learning Algorithms Have Emerged As Powerful Tools For Predicting Crop Yields Accurately. This Abstract Focuses On The Application Of The Decision Tree Algorithm To Train For Crop Yield Prediction. Once The Decision Tree Model Is Constructed, It Can Be Used To Predict Crop Yields For Unseen Data. New Input Variables, Such As Weather Forecasts Or Soil Measurements, Can Be Fed Into The Model To Obtain Yield Predictions And Crop Recommend. The Interpretability Of Decision Trees Allows Farmers To Understand Which Factors Contribute Most Significantly To Crop Yield Variations And Make Informed Decisions Accordingly. User To Give Details Then Model Is Predicted Crop .
Cryptocurrencies Are A Digital Way Of Money In Which All Transactions Are Held Electronically. It Is A Soft Currency Which Doesn’t Exist In The Form Of Hard Notes Physically. Here, We Are Emphasizing The Difference Of Fiat Currency Which Is Decentralized That Without Any Third-party Intervention All Virtual Currency Users Can Get The Services. However, Getting Services Of These Cryptocurrencies Impacts On International Relations And Trade, Due To Its High Price Volatility. There Are Several Virtual Currencies Such As Bitcoin, Ripple, Ethereum, Ethereum Classic, Lite Coin, Etc. In Our Study, We Especially Focused On A Popular Cryptocurrency, I.e., Bitcoin. From Many Types Of Virtual Currencies, Bitcoin Has A Great Acceptance By Different Bodies Such As Investors, Researchers, Traders, And Policy-makers. To The Best Of Our Knowledge, Our Target Is To Implement The Efficient Deep Learning-based Prediction Models Specifically Long Short-term Memory (LSTM) And Gated Recurrent Unit (GRU) To Handle The Price Volatility Of Bitcoin And To Obtain High Accuracy. Our Study Involves Comparing These Two Time Series Deep Learning Techniques And Proved The Efficacy In Forecasting The Price Of Bitcoin.
At Present Social Network Sites Are Part Of The Life For Most Of The People. Every Day Several People Are Creating Their Profiles On The Social Network Platforms And They Are Interacting With Others Independent Of The User’s Location And Time. The Social Network Sites Not Only Providing Advantages To The Users And Also Provide Security Issues To The Users As Well Their Information. To Analyze, Who Are Encouraging Threats In Social Network We Need To Classify The Social Networks Profiles Of The Users. From The Classification, We Can Get The Genuine Profiles And Fake Profiles On The Social Networks. Traditionally, We Have Different Classification Methods For Detecting The Fake Profiles On The Social Networks. But, We Need To Improve The Accuracy Rate Of The Fake Profile Detection In The Social Networks. In This Paper We Are Proposing Machine Learning And Natural Language Processing (NLP) Techniques To Improve The Accuracy Rate Of The Fake Profiles Detection. We Can Use The Support Vector Machine (SVM) And Naïve Bayes Algorithm.
Crop Yield Prediction And Crop Recommendation As Plays A Crucial Role In Agricultural Decision-making Processes, Enabling Farmers To Optimize Resource Allocation And Plan For Potential Risks. In Recent Years, Machine Learning Algorithms Have Emerged As Powerful Tools For Predicting Crop Yields Accurately. This Abstract Focuses On The Application Of The Decision Tree Algorithm To Train For Crop Yield Prediction. Once The Decision Tree Model Is Constructed, It Can Be Used To Predict Crop Yields For Unseen Data. New Input Variables, Such As Weather Forecasts Or Soil Measurements, Can Be Fed Into The Model To Obtain Yield Predictions And Crop Recommend. The Interpretability Of Decision Trees Allows Farmers To Understand Which Factors Contribute Most Significantly To Crop Yield Variations And Make Informed Decisions Accordingly. User To Give Details Then Model Is Predicted Crop .
Floods Are Very Harmful For Nature, Which Are Very Complex To Model. The Flood Prediction Model Will Give Risk Reduction & It Minimizes The Future Loss Of Human Life. On 18 May 2016 A South Indian State Kerala Was Affected By Flood. Machine Learning Is A Method Which Provides Intelligence To Predict The Result In Future. The Performance Comparison Of ML Models Is Based On The Speed, Time And Accuracy Of The Result. There Exist A Lot Of Machine Algorithms Which Generate Models With More Accuracy. For Flood Prediction Classification Algorithms Like Decision Tree And Linear Regression Are Used In This Research. This Paper Will Present The Dataset Of Kerala Flood 2016 Which Is Provided By Government.
Data Mining Is The Process Of Extracting Useful Unknown Knowledge From Large Datasets. Frequentitemset Mining Is The Fundamental Task Of Data Mining That Aims At Discovering Interesting Itemsets That Frequently Appear Together In A Dataset. However, Mining Infrequent (rare) Itemsets May Be More Interesting In Many Real-life Applications Such As Predicting Telecommunication Equipment Failures, Genetics, Medical Diagnosis, Or Anomaly Detection. In This Paper, We Survey Up-to-date Methods Of Rare Itemset Mining. The Main Goal Of This Survey Is To Provide A Comprehensive Overview Of The State-of-the-art Algorithms Of Rare Itemset Mining And Its Applications. The Main Contributions Of This Survey Can Be Summarized As Follows. In The First Part, We Define The Task Of Rare Itemset Mining By Explaining Key Concepts And Terminology, Motivation Examples, And Comparisons With Underlying Concepts. Then, We Highlight The State-of-art Methods For Rare Itemsets Mining. Furthermore, We Present Variations Of The Task Of Rare Itemset Mining To Discuss Limitations Of Traditional Rare Itemset Mining Algorithms. After That, We Highlight The Fundamental Applications Of Rare Itemset Mining. In The Last, We Point Out Research Opportunities And Challenges For Rare Itemset Mining For Future Research.
Data Streams Are Usually Non-stationary With Continually Changing Their Underlying Structure. Solving Of Predictive Or Classification Tasks On Such Data Must Consider This Aspect. Traditional Machine Learning Models Applied On The Drifting Data May Become Invalid In The Case When A Concept Change Appears. To Tackle This Problem, We Must Utilize Special Adaptive Learning Models, Which Utilize Various Tools Able To Reflect The Drifting Data. One Of The Most Popular Groups Of Such Methods Are Adaptive Ensembles. This Paper Describes The Work Focused On The Design And Implementation Of A Novel Adaptive Ensemble Learning Model, Which Is Based On The Construction Of A Robust Ensemble Consisting Of A Heterogeneous Set Of Its Members. We ‘USED K-NN, NAIVE BAYES’and Hoeffding Trees As Base Learners And Implemented An Update Mechanism, Which Considers Dynamic Class-weighting And Q Statistics Diversity Calculation To Ensure The Diversity Of The Ensemble. The Model Was Experimentally Evaluated On The Streaming Datasets, And The Effects Of The Diversity Calculation Were Analyzed.
The Neural Network Architecture Is Proposed As A Promising Approach To Increase The Accuracy Of The 2m Temperature Forecast Given By The COSMO Regional Model. This Architecture Allows Predicting Errors Of The Atmospheric Model Forecasts With Their Further Corrections. Experiments Are Conducted With Different Histories Of Regional Model Errors. The Number Of Epochs After Which Network Overfitting Happens Is Determined. It Is Shown That The Proposed Architecture Makes It Possible To Achieve An Improvement Of A 2m Temperature Forecast In Approximately 50% Of Cases.
Epidemics Affect People’s Daily Consumption Activities, For Example, By Causing Them To Shop Less, Travel Less, Consume Less And Invest Less. The Reduction Of A Large Number Of Economic Activities Leads To The Suppression Of Social Demand And The Reduction Of Consumption Level, Which Further Affects The GDP Of Various Countries Around The World. It Is Necessary To Investigate And Analyze The Impact Of The Epidemic On GDP In Order To Control And Analyze The Economic Situation Under The Impact Of The Epidemic. In This Paper, We Take The Impact Of COVID-19 On The GDP Of Each Country As A Regression Problem, And Propose To Forecast GDP Through Feature Engineering Combined With Aaboost Model. The Model Was Tested On More Than 50,000 Data Records From More Than 200 Countries Provided By The Kaggle Platform To Prove The Validity. The Experiment Shows That Adaboost Has Stronger Robustness Compared With Other Methods, Such As Random Forest, SVR. Adaboost Improves The MSE Of Random Forest By 2.39 And SVR By 0.38.
Hospital Readmissions Pose Additional Costs And Discomfort For The Patient And Their Occurrences Are Indicative Of Deficient Health Service Quality, Hence Efforts Are Generally Made By Medical Professionals In Order To Prevent Them. These Endeavors Are Especially Critical In The Case Of Chronic Conditions, Such As Diabetes. Recent Developments In Machine Learning Have Been Successful At Predicting Readmissions From The Medical History Of The Diabetic Patient. However, These Approaches Rely On A Large Number Of Clinical Variables Thereby Requiring Deep Learning Techniques. This Article Presents The Application Of Simpler Machine Learning Models Achieving Superior Prediction Performance While Making Computations More Tractable. Index Terms—diabetes, Hospital Readmission, Neural Network, Random Forest, Logistic Regression.
Hotel Booking Cancellation Is Provided A Substantial Effects On Demand Management Decisions In The Hospitality Industry. The Goal Of This Work Is To Investigate The Effects Of Different Machine Learning Methods In Hotel Booking Cancellation Process. In This Work, We Gathered A Hotel Booking Cancellation Dataset FromKaggle Data Repository. Then, Different Feature Transformation Techniques Were Implemented Into Primary Dataset And Generate Transformed Datasets. Further, We Reduced Insignificant Variables Using Feature Selection Methods. Therefore, Various Classifiers Were Employed Into Primary And Generated Subsets. The Effects Of The Machine Learning Methods Were Evaluated And Explored The Best Approaches In This Step. Among All Of These Methods, We Found That XGBoost Is The Most Frequent Method To Analyze These Datasets. Besides, Individual Classifiers Are Generated The Highest Result For Information Gain Feature Selection Method. This Analysis Can Be Used As The Complementary Tool To Investigate Hotel Booking Cancellation Dataset More Effectively.
This Paper Reports On The Smart Automated Irrigation System With Disease Detection. The System Design Includes Soil Moisture Sensors, Temperature Sensors, Leaf Wetness Sensors Deployed In Agriculture Field, The Sensed Data From Sensors Will Be Compared With Pre-determined Threshold Values Of Various Soil And Specific Crops. The Deployed Sensors Data Are Fed To The Arduino Uno Processor Which Is Linked To The Data Center Wirelessly Via GSM Module. The Data Received By The Data Center Is Stored To Perform Data Analysis Using Data Mining Technique Such As Markov Model To Detect The Possible Disease For That Condition. Finally, The Analysis Results And Observed Physical Parameters Are Transmitted To Android Smart Phone And Displayed On User Interface. The User Interface In Smart Phone Allows Remote User To Control Irrigation System By Switching, On And Off, The Motor Pump By The Arduino Based On The Commands From The Android Smart Phone.
Agriculture Is The Key Point For Survival. For Agriculture, Rainfall Is Most Important. These Days Rainfall Prediction Has Become A Major Problem. Prediction Of Rainfall Gives Awareness To People And Know In Advance About Rainfall To Take Certain Precautions To Protect Their Crop From Rainfall. Many Techniques Came Into Existence To Predict Rainfall. Machine Learning Algorithms Are Mostly Useful In Predicting Rainfall. Some Of The Major Machine Learning Algorithms Are ARIMA Model(Auto-Regressive Integrate D Moving Average), Artificial Neural Network, Logistic Regression, Support Vector Machine And Self Organizing Map. Two Commonly Used Models Predict Seasonal Rainfall Such As Linear And Non-Linear Models. The First Models Are ARIMA Model. While Using Artificial Neural Network(ANN) Predicting Rainfall Can Be Done Using Back Propagation NN, Cascade NN Or Layer Recurrent Network. Artificial NN Is Same As Biological Neural Networks.
Abstract—In This Paper, We Propose An Android Based Restaurant Automation System. The Main Aim Of The Project Is To Make The Restaurant Management Easier. Recently In Most Of The Restaurants, The Ordering And Delivery Of Food Items Are Doing Manually, The Disadvantages Are Huge Time Consumption, And In Some Cases The Customers Arent Delivered The Right Item At Right Time These Cause Many Problems. Hence We Thought Of Automating This Procedure Using Modern Electronic Technology. Here The Individual Tables In The Restaurant Are Provided With A Touch Screen, Represent Each Individual Digital Menu, And It Facilitates The Ordering. The Customer Can See All The Available Food Items With Its Cost In The Digital Menu And Can Select The Item. The Order From Each Table Is Received In The Kitchen Wirelessly By Bluetooth. The Electronic Menu System Helps The People To Select The Food From The Rolling Screen Of Android Touch Screen And To See The Cost And Recent Availability Of Food Items, And Showing Table Number Also. By Using A Thermal Printer Taking Bill From The Kitchen And The Hotel Staff Can Read The Items From Each Table. If The Food Is Ready In The Kitchen It Can Be Indicated To Corresponding Customers Table By An LED Glow. Index Terms—Restaurant Automation, Electronic Food Ordering System, Android Based Food Ordering, Touch Technology Based Food Ordering
Online-to-offline (O2O) Commerce Connecting Service Providers And Individuals To Address Daily Human Needs Is Quickly Expanding. In Particular, On-demand Food, Whereby Food Orders Are Placed Online By Customers And Delivered By Couriers, Is Becoming Popular. This Novel Urban Food Application Requires Highly Efficient And Scalable Real-time Delivery Services. However, It Is Difficult To Recruit Enough Couriers And Route Them To Facilitate Such Food Ordering Systems. This Article Presents An Online Crowd Sourced Delivery Approach For On-demand Food. Facilitated By Internet-of-Things And 3G/4G/5G Technologies, Public Riders Can Be Attracted To Act As Crowd Sourced Workers Delivering Food By Means Of Shared Bicycles Or Electric Motorbikes. An Online Dynamic Optimization Framework Comprising Order Collection, Solution Generation, And Sequential Delivery Processes Is Presented. A Hybrid Metaheuristic Solution Process Integrating The Adaptive Large Neighborhood Search And Tabu Search Approaches Is Developed To Assign Food Delivery Tasks And Generate High-quality Delivery Routes In A Real-time Manner. The Crowdsourced Riders Are Dynamically Shared Among Different Food Providers. Simulated Small-scale And Real-world Large-scale On-demand Food Delivery Instances Are Used To Evaluate The Performance Of The Proposed Approach. The Results Indicate That The Presented Crowdsourced Food Delivery Approach Outperforms Traditional Urban Logistics.
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