The Collatz Conjecture: A Simple Problem with Complex Implications

Mrs. Vidyashree H R

Assistant Professor

Department of Science

NCMS

 

Introduction

Mathematics is full of simple yet unsolved problems, and the Collatz Conjecture is one of the most intriguing. Proposed by German mathematician Lothar Collatz in 1937, this conjecture presents a seemingly simple iterative process:

1. Take any positive integer n.

2. If n is even, divide it by 2 (n → n/2).

3. If n is odd, multiply it by 3 and add 1 (n → 3n + 1).

4. Repeat the process.

The conjecture states that for all starting numbers, this sequence eventually reaches 1, no matter how large n is. Despite extensive computational verification and mathematical investigations, no one has been able to prove or disprove the conjecture in general.

Understanding the Collatz Sequence

Consider an example:

• n = 6 → 6 is even, so 6/2 = 3

• n = 3 → 3 is odd, so 3 × 3 + 1 = 10

• n = 10 → 10 is even, so 10/2 = 5

• n = 5 → 5 is odd, so 3 × 5 + 1 = 16

• n = 16 → 16/2 = 8 → 8/2 = 4 → 4/2 = 2 → 2/2 = 1

For n = 6, the sequence reaches 1 after 8 steps. The challenge lies in proving this pattern holds for all natural numbers.

Computational Evidence and Limitations

Millions of numbers have been tested using computers, and every case has eventually reached 1. Despite this empirical evidence, a rigorous mathematical proof remains elusive. The problem's difficulty arises because the sequence does not follow a predictable pattern—it sometimes rises unpredictably before eventually decreasing.

Mathematical Significance

The Collatz Conjecture belongs to a broader class of problems in dynamical systems and number theory. It reflects deep mathematical properties, including:

1. Iteration and Chaos – The sequence exhibits unpredictable jumps before converging, making it  similar to chaotic systems.

2. Modular Arithmetic – Different residues mod 2, mod 3, and higher powers reveal underlying  structure but no clear proof.

3. Computational Complexity – It serves as an example of problems that are easy to state but difficult to solve, much like the Riemann Hypothesis or Goldbach’s Conjecture.

Generalizations and Related Problems

Variants of the Collatz Conjecture have been explored, such as:

• The Syracuse Problem – Another name for the conjecture, emphasizing its unpredictable behavior.

• 5n + 1 Problem – A modified version where odd numbers are mapped using 5n + 1 instead of 3n + 1, leading to different behaviors.

• Mathematical Graphs – Researchers have analyzed Collatz sequences using directed graphs, connecting numbers based on their iterative paths.

Conclusion

The Collatz Conjecture remains an unsolved mystery in mathematics, embodying the paradox of a problem that is simple to state yet seemingly impossible to prove. Whether it will eventually be solved or remain an enigma, it continues to inspire mathematical curiosity and research.

AI IN CRYPTOGRAPHY

Written by: PALLAVI V (Final year BCA)

1.    ABSTRACT:

The integration of AI in Cryptography represents a significant advancement in the field of data security. AI enhances cryptographic systems by designing stronger encryption algorithms, detecting vulnerabilities, and optimizing cryptographic processes.AI-driven pattern recognition algorithms contribute to the development of robust encryption methods, while machine learning models and automated attacks facilitate the identification and mitigation of weaknesses in existing systems. In quantum cryptography, AI optimizes quantum key distribution and error correction, ensuring secure communication channels.AI plays a crucial role in developing post-quantum cryptographic methods that are resistant to quantum attacks. Practical applications of AI -enhanced cryptography include secure communications, financial transaction, and data protection, highlighting its importance in safeguarding the digital landscape.

2.    KEYWORDS:

o   Designing stronger encryption algorithms

o   Cryptoanalysis

o   Quantum cryptography

o   Blockchain and cryptocurrencies

3.   INTRODUCTION:

AI in cryptography involves using artificial intelligence to make cryptographic systems more secure and efficient. cryptography is the practice of securing communication and data, has long has been a cornerstone information security. By leveraging AI and ML techniques, cryptographic systems can become more robust, adaptive, and secure. AI can help in two main ways:

     3.1   Designing stronger encryption algorithms: AI can help to create new methods that are more secure than traditional ones. such as:

• Pattern recognition: AI can analyze the vast amount of data to identify patterns that humans might miss. This can lead to the development of encryption algorithms that are harder to crack.
• Adaptive algorithms: AI can create encryption methods that adapt in real-time to potential threats.

3.2  Cryptoanalysis (Breaking encryption): AI can also be used to test the strength of encryption algorithms by trying to break them:

• Machine learning models: AI can use ML to predict weakness in encryptio algorithms. By training on large datasets, AI can learn to identify potential vulnerabilities.
• Automated attacks: AI can automate the process of attempting to break encryption, making it faster and more efficient. this helps to identify weak points that need to be strengthened.

3.3 Quantum cryptography: AI plays a role in the emerging field of quantum cryptography:

• Quantum key distribution: AI can optimize the process of distributing cryptographic keys using quantum particles, ensuring that any attempt to intercept the keys is detected.
• Error detection: quantum systems are prone to errors. AI can help in developing error correction algorithms that ensures the integrity of the cryptographic process.

3.4 Blockchain and cryptocurrencies: AI is also used in securing blockchain technologies and cryptocurrencies:

• Smart contracts: AI can analyze and verify smart contracts to ensure they are secure and free from vulnerabilities.

• Fraud detection: AI can monitor block chain transaction in real-time to detect and prevent fraudulent activities.

4.     PRACTICAL APPLICATIONS:

•  Secure communication: AI-enhanced cryptography is used in secure messaging apps, ensuring that conversations remain private.
•  Financial transactions: banks and financial institutions use AI to secure online transactions and protect against fraud.
•  Data protection: companies use AI to encrypt sensitive data, such as customers information, to prevent data breaches.

5.     CONCLUSION:

AI is revolutionizing cryptography by making it more adaptive, robust, and secure against both current and future threats. This synergy between AI and cryptography is crucial for protecting our digital world.

 

6.     REFERENCES:

i.         https://ieeexplore.ieee.org/document/7280536

ii.        https://link.springer.com/article/10.1186/s40543-024-00416-6

 

AI FASHION INFUSION

Written by : Archana J H Uppar(2nd year MCA)

In the fashion world, trial and invention are common, and contrivers frequently draw alleviation from colourful sources to produce fresh and distinctive aesthetics. currently, everything is at your doorstep, you can buy your favourite clothes wherever you are. There's no need to flash back the ending time of the shops and line over, no need. To spend the whole day in the request, moment's technological development has made it possible to do all this. AI technology is creating its impact in the fashion persistence, allowing businesses to explore data, ameliorate creativity, and grease procedures. AI algorithms are used to examine consumer details, develop individualized outgrowth suggestions, and better deals and client pleasure. In design, AI allows fashion businesses to make special and creative designs. For illustration, AI- powered productive design is used to make designs that cannot be created manually, while furnishing time operation. Generative design algorithms use machine literacy ways to explore proved data and develop new designs grounded on detailed design parameters. In manufacturing, AI allows businesses to optimize product, drop destruction, and enhance sustainability. AI- powered stock operation systems help lower the threat of overstocking or understocking, therefore enhancing working effectiveness. also, AI- powered care can notice outfit defeats before they be, reduce time-out and perfecting productivity. Using AI tools can help to verify large quantities of data and information like trends, client preferences, and request demand variation enforcing AI in fashion will further clarify the design process, replacing traditional 2D sketches with 3D technology, which is another operation of AI.

 AI allows disagreements to produce more realistic visualizations of designs.

 AI commitment in fashion reduces the time and cost of creating physical designs or models.

 AI tools enable unborn future of fashion trends and lower the threat of designing collections. With AI analytics, we can produce further client- centric designs grounded on their demands.

 AI- powered tools can assay fabric parcels to AI tools can track product lines, optimize the manufacturing process, and reduce the time it takes to make garments.

 Using AI- powered systems, fashion brands can epitomize the shopping experience by offering virtual passions.

 Virtual fitting is another innovative way to help guests choose the right size and fit of clothes.

 Chatbots can give24/7 support, reducing the need for guests to stay for help disputes to choose the stylish new attires.

What's the Future Hold for AI Fashion? AI's fine computing power offers speed, which helps the fashion force chain. Algorithmic advancements also mean to use AI for accurately describing knowledge of future. Its capacity to hold and read big quantities of data will allow retail brands and fashion places to determine client requirements and want.

AI EMOTION RECOGNITION

Written by : Tejaswini (2nd year MCA)

AI Emotion Recognition

AI emotion recognition is a very active current field of computer vision research that involves facial emotion detection and the automatic assessment of sentiment from visual data. Human- machine interaction is an important area of research where artificially intelligent systems with visual perception aim to gain an understanding of human interaction.

What is Emotion AI?

Emotion AI, also called Affective Computing, is a rapidly growing branch of Artificial Intelligence that allows computers to analyze and understand human nonverbal signs such as facial expressions, body language, gestures, and voice tones to assess their emotional state. Hence, visual Emotion AI analyses face appearances in images and videos using computer vision technology to analyze an individual’s emotional status.

Visual AI Emotion Recognition

Emotion recognition is the task of machines trying to analyze, interpret and classify human emotion through the analysis of facial features. Among all the high-level vision tasks, Visual Emotion Analysis (VEA) is one of the most challenging tasks for the existing affective gap between low-level pixels and high-level emotions. Against all odds, visual emotion analysis is still promising as understanding human emotions is a crucial step towards strong artificial intelligence. With the rapid development of Convolutional Neural Networks (CNNs), deep learning became the new method of choice for emotion analysis tasks.

How AI-Emotion Analysis works

On a high level, an AI emotion application or vision system includes the following steps:

Step #1: Acquire the image frame from a camera feed (IP, CCTV, USB camera).

Step #2: Preprocessing of the image (cropping, resizing, rotating, color correction).

Step #3: Extract the important features with a CNN model

Step #4: Perform emotion classification

The basis of emotion recognition with AI is based on three sequential steps:

1. Face Detection in Images and Video Frames

2. Image Preprocessing

3. Emotion Classification AI Model

Applications of AI Emotion Recognition and Sentiment Analysis

There is a growing demand for AI emotion analysis in the AI and computer vision market. While it is not currently popular in large-scale use, solving visual emotion analysis tasks is expected to greatly impact real-world applications.

1. Opinion Mining

Opinion mining, or sentiment analysis, aims to extract people’s opinions, attitudes, and emotions from data. Conventional sentiment analysis concentrates primarily on textual content (for example, online user comments). However, visual sentiment analysis is beginning to receive attention since visual content such as images and videos became popular for self-expression in social networks. Opinion mining is an important strategy of smart advertising.

2. Medical Sentiment Analysis

Medical sentiment concerns the patient’s health status, medical conditions, and treatment. Its analysis and extraction have multiple applications in mental disease treatment, remote medical services, and human-computer interaction.

3.Emotional Relationship Recognition

Recent research developed an approach to recognize the emotional state of people to perform pairwise emotional relationship recognition. The challenge is to characterize the emotional relationship between two interacting characters using AI-based video analytics. 

FIBONACCI SERIES IN LOGO DESIGN

Written by : Indushree B R (2nd year MCA)

The world of logo design where creativity presents with mathematical principles to create iconic visual representations. One such mathematical marvel that significantly influences logo design is the Fibonacci series. Root in nature’s pattern and embraced by artists, architects, and designers, the Fibonacci series and its golden ratio hold a profound impact on the aesthetics and composition of logos.

Fibonacci spiral: Guiding design elements The Fibonacci spiral, derived from the Fibonacci sequence, is a fundamental geometric shape in design. By overlaying this spiral onto a logo, designers can align elements along its curve, providing a sense of natural progression. The spirals proportions adhere to the golden ratio, offering an effective tool to organize and arrange design elements. Iconic Logos Applying Fibonacci Principles

Apple Inc:

 Apple’s iconic logo, a partially bitten apple, incorporates Fibonacci proportions to achieve a balanced and visually pleasing design. The circular shape and bite mark follow the principles of the golden ratio.

Twitter:

 Twitter’s logo, the bird icon, is crafted using the Fibonacci sequence for proportional scaling, resulting in a harmonious and recognizable design.

Integrating the Fibonacci series and the golden ratio into logo design facilitates the creation of visually captivating and harmonious logos. From establishing proportions to arranging elements, the Fibonacci sequence offers real-time applications that enhance logo aesthetics. By understanding and implementing these mathematical principles, designers can create logo. 

THE ROLE OF AI IN SMART CARS

Written by : Tejaraj S (2nd year MCA)

INTRODUCTION

The automotive industry is at the forefront of a technological revolution, with smart cars becoming increasingly prevalent on our roads. At the heart of this transformation is Artificial Intelligence (AI), playing a pivotal role in enhancing the safety, efficiency, and overall driving experience. This article explores the intricate ways in which AI is integrated into smart cars, shaping the future of transportation. Among the various artificial intelligence examples in daily life, one is Autonomous Vehicles. Autonomous vehicles have always been in the limelight; recently, Elon Musk’s TESLA dominates tech talks. Autonomous Vehicles (AV) are well equipped with multiple sensors that help them better understand their surroundings. These sensors generate a huge amount of data that needs to be processed to make sense of the complicated data. Companies involved in manufacturing these sensors depend heavily upon AI and its algorithms to process huge amounts of data and validate the driving systems.AI has been paving the way for more advanced development in the autonomous driving industry.AI provides all the power that is required for self-driving cars to operate. Developers process many complex data using Machine Learning, neural networks, and image recognition technology to develop self- driving cars .The neural networks are supposed to identify the patterns in data and transfer them to ML algorithms. The transferred data includes images from cameras on self-driving analyzing which the neural networks are able to trace out traffic lights, pedestrians, etc., of the current environment.

I. Autonomous Driving: The AI Revolution on Wheels

The most conspicuous manifestation of AI in smart cars is the development of autonomous driving capabilities. AI algorithms, particularly those using machine learning and computer vision, enable vehicles to perceive their surroundings and make decisions in real-time. Sensors, cameras, and radar systems gather data about the environment, and AI processes this information to navigate, detect obstacles, and make split-second decisions to ensure safe and efficient travel.

II. Advanced Driver Assistance Systems (ADAS)

AI powers a range of Advanced Driver Assistance Systems that enhance driver safety and reduce the likelihood of accidents. These systems include adaptive cruise control, lane departure warnings, collision avoidance, and parking assistance. By continuously monitoring the driving environment and driver behaviour, AI can assist in critical situations and provide alerts or interventions to prevent collisions.

III. Natural Language Processing (NLP) and In-Car Virtual Assistants

Smart cars are becoming more interactive and user-friendly through the incorporation of Natural Language Processing (NLP). AI-powered virtual assistants respond to voice commands, allowing drivers to control various functions such as navigation, music, and temperature without taking their hands off the wheel. This integration of AI not only enhances convenience but also contributes to safer driving by minimizing distractions.

IV. Predictive Maintenance for Optimal Performance

AI plays a crucial role in predictive maintenance, ensuring that smart cars operate at peak performance. Machine learning algorithms analyse data from sensors and the vehicle's internal systems to predict when components might fail or require maintenance. This proactive approach reduces the likelihood of unexpected breakdowns, minimizes repair costs, and extends the overall lifespan of the vehicle.

V. Traffic Management and Route Optimization

In a connected transportation ecosystem, AI contributes to efficient traffic management and route optimization. Smart cars can communicate with each other and with traffic infrastructure, sharing real-time data about road conditions, traffic congestion, and potential hazards. AI algorithms process this information to suggest optimal routes, reducing travel time and enhancing overall traffic flow.

VI. Over the Air (OTA) Software Updates

AI facilitates Over-the-Air software updates, allowing smart cars to receive updates and improvements remotely. This not only keeps the vehicle's software current but also enables manufacturers to address security vulnerabilities, add new features, and enhance performance without requiring physical visits to service centres.

CONCLUSION 

The integration of Artificial Intelligence into smart cars is reshaping the automotive landscape, making vehicles safer, more efficient, and technologically advanced. As AI technologies continue to evolve, we can expect further innovations in smart car capabilities. However, challenges such as data security, regulatory frameworks, and public acceptance of autonomous driving still need to be addressed. The collaborative efforts of automotive manufacturers, technology developers, and regulatory bodies will be crucial in ensuring the seamless integration of AI into smart cars, driving us into a future of safer and smarter transportation .

AI IN VIDEO GAMES - ARTIFICIAL INTELLIGENCE USED IN VIDEO

Written by : Sanjana B C (2nd year MCA)

AI and ML have been used in video games for a long time to provide a self-improving and challenging experience to gamers. Regarded as one of the best examples of Artificial Intelligence in everyday life, these can be easily experienced by players in the form of:

1. AI-controlled Non-Playable Characters (NPCs) that react dynamically to player actions

2. Procedural Content Generation for generating automated newer game environment when combined with Machine Learning

3. Player-experience modeling for dynamically balancing gameplay difficulty based on player skill Machine Learning, on the other hand, is often used in video games to generate background music by using Artificial Neural Networks. DOTA 2, a famous MOBA game, uses Deep Learning in the form of open-AI five.

VIDEO GAME TERMINOLOGY 

The gameplay experience varies radically between video games, but many common elements exist. Most games will launch into a title screen and give the player a chance to review options such as the number of players before starting a game.  In some games, intermediate points between levels will offer save point where the player can create a saved game on storage media to restart the game should they lose all their lives or need to stop the game and restart at a later time. These also may be in the form of a passage that can be written down and reentered at the title screen.

PLATFORM 

 PC game

 Home console

 Handheld console

 Arcade video game

 Virtual reality

 Mobile game

 Cloud gaming

 Input device

 video game can use several types of input devices to translate human actions to a game. 

 Most common are the use of game controllers like gamepads and joysticks for most consoles, and as accessories for personal computer systems along keyboard and mouse controls.

  Digital cameras and motion detection can capture movements of the player as input into the game, which can, in some cases, effectively eliminate the control, and on other systems such as virtual reality, are used to enhance immersion into the game.

 Display and output

 The game's output can range from fixed displays using LED or LCD elements, text-based game, two-dimensional and three- dimensional, graphics, and augmented reality displays.

 Features such as color depth, refresh rate, frame rate, and screen resolution are a combination of the limitations of the game platform and display device and the program efficiency of the game itself.

 The game's output can range from fixed displays using LED or LCD elements, text-based games, two-dimensional and three- dimensional graphics, and augmented reality displays.

 The game's graphics are often accompanied by sound produced by internal speakers on the game platform or external speakers attached to the platform, as directed by the game's programming.

 This is most commonly haptic technology built into the game controller, such as causing the controller to shake in the player's hands to simulate a shaking earthquake occurring in game.

THREATS IN ROBOTICS

Written  by: Joshna K S (1st year MCA)

ABSTRACT

Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust. With growing dependency on physical robots that work in close proximity to humans, the security of these systems is becoming increasingly important to prevent cyber-attacks that could lead to privacy invasion, critical operations sabotage, and bodily harm. The current shortfall of professionals who can defend such systems demands development and integration of such a curriculum. This course description includes details about seven self-contained and adaptive modules on “AI security threats against pervasive robotic systems”.

Topics include:

1) Introduction

2) Inference attacks and associated security strategies;

3) Ethics of AI, robotics, and cybersecurity.

4) Conclusion.

KEYWORDS - Cybersecurity, Robotics, Artificial Intelligence, Adversarial Artificial Intelligence.

INTRODUCTION 

Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive in our daily lives and transformed operations. Modern households, construction sites, warehouses, hospitals, precision agriculture, military, emergency workers, and more, use different sets of robots to provide workflow augmentation and mobility assistance. Robotic technologies have experienced mass adoption in the consumer space, industry, and critical infrastructures bringing in concerns related to security, safety, accuracy and trust. Inference attacks and associated security strategies: After understanding training attacks on AI components of robotic systems, students next study inference attacks on robotic systems. These inference attacks include model stealing, model evasion, and model inversion. Students first study and visualize the weights and layers of a neural network and understand how these contribute to computing the robot output, such as identified objects or movement commands when carrying out inference. Students gain familiarity with how these AI models are commonly deployed on robotic systems and how attackers can potentially steal or exfiltrate private information or intellectual property via their inference APIs. Students learn about adversarial techniques such as Simple Transformation of the input, Common Corruption, or Adversarial Examples (carefully perturbing the input to achieve desired output) which an attacker may use as a model evasion tactic to prevent correct output computation. Students next learn about model stealing attacks where the attackers are able to build a shadow model whose fidelity matches that of the victim by exploiting the robot’s inference engine. Finally, students learn about model inversion attacks where by querying the robot’s inference engine strategically, an adversary could extract potentially private information embedded in the training data. In a series of case studies are conducted by the students to understand how model inference attacks work. Students study several well-known model inference attack cases such as the Cylance model evasion and the GPT-2 model replication. With the Cylance model evasion incident, students study how attackers can use logging data to understand the inner workings of the model, and then reverse-engineer the model to understand which attributes can be adjusted to cause an incorrect inference. With the GPT-2 model replication incident, students’ study how attackers were able to make use of public documentation about GPT-2 to recover a functionally equivalent “shadow” model. Students are also be assigned the task of identifying and researching other examples of model inference attacks on an AI system as well as the security repercussions of those attacks. Ethics of 

AI , ROBOTICS AND CYBER SECURITY :

The intended goal of this module is to help students understand that AI, robotics, and their security will have a significant impact on the development of humanity. These have prompted fundamental questions about privacy and surveillance, manipulation of behaviour, opacity of AI systems, bias, human-robot interaction, employment, and machine ethics. Each of the fundamental questions will be discussed with the students along with various examples and case studies. Special attention will be paid to the concept of AI trustworthiness which, in turn, depends on the ability to assess and demonstrate the fairness (including broad accessibility and utility), transparency, explain ability, impartiality, inclusivity, and accountability of such systems. Students are made to understand specific national and international laws and regulations that can come into play while building secure robotic systems. Concrete examples linking the aforementioned fundamental questions with existing laws are discussed. requires students to investigate and research a robotic cyber incident. Here they are tasked with explaining the cyber incident, research relevant legal statutes, and give their opinions. Afterwards, we impress upon the students the importance of human-robot interaction (HRI) studies. These studies enable robotic developers to understand user needs with respect to data collection and processing, information manipulation, trust, blame, informational privacy, and security. We explain to the students how to set up a scientifically sound privacy and security HRI study, collect data, gather inferences, and use the results to make informed decisions while developing robots and its AI subsystems. will entail students executing a comprehensive privacy and security HRI study on a robotic system of their choice. Students create a security/privacy related HRI experiment, an Institutional Review Board (IRB) proposal, along with the necessary pre- and post-surveys.

CONCLUSION

In this article, we provide an outline of a course with an aim of inculcating a culture of preparedness against AI security threats to pervasive robotic systems. We firmly believe that such a course when introduced at various universities and educational institutions will produce graduates and future workforce which will be well versed and equipped to prevent, detect and mitigate against sophisticated cyberattacks.

RECOGNIZING ONLINE SPAM MAKING USE OF AI

Written by: Kavya Reddy (1st year MCA)

ABSTRACT

Nowadays, emails are used in almost every field, from business to education. Emails have two subcategories, i.e., ham and spam. Email spam, also called junk emails or unwanted emails, is a type of email that can be used to harm any user by wasting his/her time, computing resources, and stealing valuable information. The ratio of spam emails is increasing rapidly day by day. Spam detection and filtration are significant and enormous problems for email and IoT service providers nowadays. Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches. Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest. This paper surveys the machine learning techniques used for spam filtering techniques used in email and IoT platforms by classifying them into suitable categories. A comprehensive comparison of these techniques is also made based on accuracy, precision, recall, etc. In the end, comprehensive insights and future research directions are also discussed.

INTRODUCTION

In the era of information technology, information sharing has become very easy and fast. Many platforms are available for users to share information anywhere across the world. Among all information sharing mediums, email is the simplest, cheapest, and the most rapid method of information sharing worldwide. But, due to their simplicity, emails are vulnerable to different kinds of attacks, and the most common and dangerous one is spam. No one wants to receive emails not related to their interest because they waste receivers’ time and resources. Besides, these emails can have malicious content hidden in the form of attachments or URLs that may lead to the host system’s security breaches. Spam is any irrelevant and unwanted message or email sent by the attacker to a significant number of recipients by using emails or any other medium of information sharing. So, it requires an immense demand for the security of the email system. Spam emails may carry viruses, rats, and Trojans. Attackers mostly use this technique for luring users towards online services. They may send spam emails that contain attachments with the multiple-file extension, packed URLs that lead the user to malicious and spamming websites and end up with some sort of data or financial fraud and identify theft. Many email providers allow their users to make keywords base rules that automatically filter emails. Still, this approach is not very useful because it is difficult, and users do not want to customize their emails, due to which spammers attack their email accounts.

METHODOLOGY

In machine learning, spam filtering protocols use instance-based or memory-based learning methods to identify and classify incoming spam emails based on their resemblance to stored training examples of spam emails. See also email virus, ingress filtering, egress filtering, filter, firewall and phishing. The reason to do this is simple: by detecting unsolicited and unwanted emails, we can prevent spam messages from creeping into the user's inbox, thereby improving user experience.

User Management:

The user who is using this for the very first time must register, by using the website the user or the individual should get registered into it, by registering this will help to maintain separate account for each user. Registration of the user is must before they log in. The user will login to the main page with his registered name and password. Once the user successfully login the authorized page will be displayed otherwise that shows the error messages. Login is compulsory.

Login: The user will login to the main page with his registered name and password. Once the user successfully login the authorized page will be displayed otherwise that shows the error messages. Login is compulsory

Inbox: This page will store all of the mails received by user. All the received Mails will be listed sorted in order of date. Input: the inbox page will accept all the incoming emails sent to an individual. Output: the receiver can open and read the email received to their address.

Sent: This folder stores all the mails sent from the user. Input: here the sender will compose an email and send to the recipient.

Trash: This folder will store all of mails deleted by the user

Input: select and Delete all the unwanted emails.

Output: all the deleted emails are added in the trash bin. Trash bin stores all the deleted emails.

CONCLUSION

Email has been the most important medium of communication nowadays, through internet connectivity any message can be delivered to all aver the world. More than 270 billion emails are exchanged daily, about 57% of these are just spam emails. Spam emails, also known as non-self, are undesired commercial or malicious emails, which affects or hacks personal information like bank ,related to money or anything that causes destruction to single individual or a corporation or a group of people. Besides advertising, these may contain links to phishing or malware hosting websites set up to steal confidential information. Spam is a serious issue that is not just annoying to the end-users but also financially damaging and a security risk. Hence this system is designed in such a way that it detects unsolicited and unwanted emails and prevents them hence helping in reducing the spam message which would be of great benefit to individuals as well as to the company .

REFERENCE

https://www.researchgate.net/publication/342113653