The-Growing- Problem- of- SMS- Spam.pptx

Seminar on Growing Problem of SMS Spam

1. What is SMS Spam ? Unwanted Texts SMS spam refers to the practice of sending unsolicited text messages, often for the purpose of advertising or fraud. Spammers use techniques like spoofing phone numbers to make messages appear to come from legitimate sources. Deceptive Tactics

2. Overview The project addresses the growing need to filter spam messages efficiently using advanced AI techniques. Leveraging machine learning and Long Short-Term Memory (LSTM) networks, the goal is to enhance the accuracy of spam classification in SMS .

3. The Impact of SMS Spam Privacy Invasion SMS spam violates personal privacy and can be disruptive, especially for those who rely on their phones for work or emergencies. Financial Burden Receiving too many spam texts can lead to increased data usage and costs for consumers.

4. Steps Involved Data Collection and preprocessing Feature engineering Model design Evaluation Deployment

5. Algorithms Naive Bayes: Simple probabilistic classifier for text data. Support Vector Machines: Separates data into classes using hyperplanes Naive Bayes : Simple probabilistic classifier for text data. Support Vector Machines : Separates data into classes using hyperplanes . Naive Bayes: Simple probabilistic classifier for text data. Support Vector Machines: Separates data into classes using hyperplanes

6. LSTM (Long Short-Term Memory): Recurrent Neural Network (RNN) designed to retain long-term dependencies. Ideal for sequential data like text messages. Captures word context using memory cells and gates. Word Embeddings: Convert words into dense vector representations for be LSTM (Long Short-Term Memory) : LSTM (Long Short-Term Memory): Recurrent Neural Network (RNN) designed to retain long-term dependencies. Ideal for sequential data like text messages. Captures word context using memory cells and gates. Word Embeddings: Convert words into dense vector representations for be Recurrent Neural Network (RNN) designed to retain long-term dependencies. Ideal for sequential data like text messages. Captures word context using memory cells and gates.

7. Tools and Technologies Programming Language : Python Libraries : TensorFlow/ Keras , Scikit-learn, NLTK, Pandas, NumPy Dataset : SMS Spam Collection dataset Platform : Google Colab

8. Execution 1 2

9. Conclusion While SMS spam remains a persistent problem, being vigilant and taking proactive measures can help reduce its impact. By working together to report and combat this issue, we can create a safer and more secure mobile experience for everyone.

10. Thank You