The Interactive Chess Board Game Is Unlike Games In Its Ordinary Way. This Board Game Together With Tangible Movements Of All Pieces Is Considered To Be Users Attraction. Therefore, The New Chessboard With An Automatic Moving Mechanism For Every Piece Is Chosen. Initially, We Have Designed And Developed An Aluminum Core Structure For Positioning X And Y-axis. Furthermore, A Controllable Magnet Is Deliberated For Holding And Moving An Individual Chess Piece According To Player Manipulations. Purpose Of This Interactive Chess Board Is Applying Technology To Board Game For Excitement, Interest, Amazement, And Attraction. Arduino Microcontroller Is Used For Controlling Every Step Of Piece Movement. The Microcontroller Receives Control Information Through The User Interface And Then Moves The Chess Piece To The Destination On The Board. The Position Calculation Is Brought To Identify The Chess Piece And Drive Accurately The Stepper Motors In X And Y-axis.
In This Paper We Introduce An AI Bot To Enhance The Skills Of The Player And The AI Bot Uses The Algorithms Further Discussed In This Paper. Player Can Follow The Simultaneously Running AI Bot To Play The Game Effectively. In This We Use The Classic Snake Game, For That We Present Different Algorithms Or Methods For AI Bot. It Includes Three Searching Algorithms Related To Artificial Intelligence, Best First Search, A* Search And Improved A* Search With Forward Checking, And Two Baseline Methods Random Move And Almighty Move.
In A Generation Led By Millennials, Technologies Are Becoming Redundant Each Year. The Organizations Are Competing On A Global Scale And Newer And Innovative Strategies Are Introduced In The Field Of Marketing To Reach Out To The Potential Buyers. Real Estate, Being One Of The Biggest Business Sectors, Needs More Efficiently Targeted Marketing Campaigns As This Is A Very Nice And Unexplored Field In The Indian Scenario. Real Estate Projects Are Highly Priced Products Which Cannot Be Sold Efficiently Without A Well Strategized Marketing Campaign So As To Reach Out To The Exact Targeted Market. The Unsold Inventories In Various Metro Cities Range From 15-60%. The Stipulated Real Estate Sector Growth Trends By Government Do Not Go Hand In Hand With The On-ground Realities Of The Piled-up Inventories. The Marketing Strategies Have Not Evolved With The Digitization Boom, And Still The Real Estate Marketing Techniques Are Conventional And Financially Heavy. Through This Study, Efforts Have Been Made To Pin Point The Existing Supply-demand Problems In The Cities Of Ahmedabad And Mumbai In The Affordable And HIG Housing Sector Specifically. Also, Suitable Solutions For Marketing Campaigns Have Been Proposed Considering The Current Market Realities For Both Cities.
The Basic Nonverbal Interaction That Is Now Evolving In The Upcoming Generation Is Eye Gaze. This Eye Blink System Builds A Bridge For Communication Of People Affected With Disabilities. The Operation Is So Simple That With The Eyes Blinking At The Control Keys That Are Built In The Screen . This Type Of System Can Synthesize Speech, Control His Environment, And Give A Major Development Of Confidence In The Individual . Our Paper Mainly Enforces The Virtual Keyboard That Not Only Has The Built In Phrases But Also Can Provide The Voice Notification/ Speech Assistance For The People Who Are Speech Disabled. To Achieve This We Have Used Our Pc/laptop Camera Which Is Built In And It Recognizes The Face And Parts Of The Face. This Makes The Process Of Detecting The Face Much Easier Than Anything. The Eye Blink Serves As The Alternative For A Mouse Click On The Virtual Interface. As Already Mentioned, Our Ultimate Achievement Is To Provide A Nonverbal Communication And Hence The Physically Disabled People Should Get A Mode Of Communication Along With A Voice Assistant. This Type Of Innovation Is A Golden Fortune For The People Who Lost Their Voice And Affected To Paralytic Disorders. We Have Further Explained With The Respective Flowcharts And With Each Juncture
The Present-day World Has Become All Dependent On Cyberspace For Every Aspect Of Daily Living. The Use Of Cyberspace Is Rising With Each Passing Day. The World Is Spending More Time On The Internet Than Ever Before. As A Result, The Risks Of Cyber Threats And Cybercrimes Are Increasing. The Term 'cyber Threat' Is Referred To As The Illegal Activity Performed Using The Internet. Cybercriminals Are Changing Their Techniques With Time To Pass Through The Wall Of Protection. Conventional Techniques Are Not Capable Of Detecting Zero-day Attacks And Sophisticated Attacks. Thus Far, Heaps Of Machine Learning Techniques Have Been Developed To Detect The Cybercrimes And Battle Against Cyber Threats. The Objective Of This Research Work Is To Present The Evaluation Of Some Of The Widely Used Machine Learning Techniques Used To Detect Some Of The Most Threatening Cyber Threats To The Cyberspace. Three Primary Machine Learning Techniques Are Mainly Investigated, Including Deep Belief Network, Decision Tree And Support Vector Machine. We Have Presented A Brief Exploration To Gauge The Performance Of These Machine Learning Techniques In The Spam Detection, Intrusion Detection And Malware Detection Based On Frequently Used And Benchmark Datasets.
Machine Learning, A Branch Of Artificial Intelligence, Can Be Described Simply As Systems That Learn From Data In Order To Make Predictions Or To Act, Autonomously Or Semi-autonomously, In Response To What It Has Learned. Unlike Pre-programmed Solutions Or Business-rules-engines, Machine Learning Can Eliminate The Need For Someone To Continuously Code Or Analyze Data Themselves To Solve A Problem. While There Are A Variety Of Applications Of Machine Learning, And The More Advanced “deep Learning”, Most Have Been Focused On Machine Learning That Trains A Computer To Perform Human-like Tasks, Such As Recognizing Speech, Identifying Images (or Objects And Events Portrayed Therein) And In Making Predictions. In This Paper, We Will Explore The Use Of Machine Learning As An Approach To Helping With Upstream Activities In Data Management Including Classification And Feature Identification, As Well As Discuss Implications For Data Quality, Data Governance And Master Data Management.
The Need For A Method To Create A Collaborative Machine Learning Model Which Can Utilize Data From Different Clients, Each With Privacy Constraints, Has Recently Emerged. This Is Due To Privacy Restrictions, Such As General Data Protection Regulation, Together With The Fact That Machine Learning Models In General Needs Large Size Data To Perform Well. Google Introduced Federated Learning In 2016 With The Aim To Address This Problem. Federated Learning Can Further Be Divided Into Horizontal And Vertical Federated Learning, Depending On How The Data Is Structured At The Different Clients. Vertical Federated Learning Is Applicable When Many Different Features Is Obtained On Distributed Computation Nodes, Where They Can Not Be Shared In Between. The Aim Of This Thesis Is To Identify The Current State Of The Art Methods In Vertical Federated Learning, Implement The Most Interesting Ones And Compare The Results In Order To Draw Conclusions Of The Benefits And Drawbacks Of The Different Methods. From The Results Of The Experiments, A Method Called FedBCD Shows Very Promising Results Where It Achieves Massive Improvements In The Number Of Communication Rounds Needed For Convergence, At The Cost Of More Computations At The Clients. A Comparison Between Synchronous And Asynchronous Approaches Shows Slightly Better Results For The Synchronous Approach In Scenarios With No Delay. Delay Refers To Slower Performance In One Of The Workers, Either Due To Lower Computational Resources Or Due To Communication Issues.
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