Ideaz

These days, smartphones have become very necessary, but they also bombard us with endless notifications. Food delivery ads, random sales offers, game promotions, all compete for our attention. This barrage of alerts makes it easy to miss critical notifications like OTPs, warnings, delivery updates or important texts and mails. This results in one always being distracted and stressed.

One of the current solutions is “Do Not Disturb” mode. It applies restrictions on notifications and calls and users can also enable app-by-app notification controls. But this only partly address the issue as its too manual and time-consuming resulting in users being too intimidated to even start keeping settings. None of these methods understand context or prioritize anything automatically. This is the gap in the current market.

The solution is to build a Smart Notification Filter powered by AI. The filter would categorize incoming notifications into three lists: Urgent (e.g., OTPs, reminders, calls), Useful (delivery updates, calendar events), and Promotional (ads, sales, spam). The system will learn from user behaviour like what they click, what they clear, and at what times they interact with them. For example, if I always ignore food coupons but instantly open my college mails, the filter will adjust and push college mails to the top while hiding promotional spam.

Beneficiaries:

• Users get peace of mind, less distractions, and less risk of missing urgent notifications.
• Companies/OS makers (like Apple, Google, or smartphone brands) benefit by offering a premium feature, improving user experience thereby increasing customer loyalty.
• Society benefits indirectly through healthier and more focused habits.

This problem matters to me because I experience it everyday, receiving hundreds of notifications and sometimes even missing important academic related mails due to it.

Technical Details: The system can use Natural Language Processing (NLP) to analyze notification text, sender identity, and past user actions. It is lightweight on-device machine learning models and it ensures privacy while still learning user preferences. Notifications can then be displayed in a priority inbox style, with urgent ones highlighted and promotional ones batched for later review.

Problem Statement

Misinformation and fake news spread rapidly on social media and digital platforms, often outpacing fact-checking efforts. Current solutions—like manual moderation and third-party fact-checking—are too slow and cannot scale to the massive volume of online content. As a result, misinformation shapes public opinion, creates panic, and even influences elections or public health decisions.

Solution

A neural network–based fake news detection system can analyze text, images, and even videos to identify misleading or manipulated content in real time. Transformer-based natural language processing (NLP) models (like BERT or GPT fine-tuned for classification) can evaluate context, sentiment, and source reliability. For multimedia, convolutional neural networks (CNNs) and deepfake detection algorithms can flag manipulated images or videos. The system would cross-reference news with verified sources, assign credibility scores, and alert users before they share harmful content.

Why Is It Unique?

Unlike existing keyword-based or rule-based filters, neural networks can learn subtle patterns in language and media manipulation that humans or simple algorithms might miss. It also adapts continuously, learning from new misinformation tactics. Furthermore, combining multi-modal analysis (text + images + video) makes it more robust than single-source detection systems.

Who Benefits from This Idea?

Social media users → gain protection from misinformation.

Governments and policymakers → can reduce panic and propaganda.

Journalists and educators → get tools to promote verified information.

Society as a whole → benefits from a healthier, more trustworthy digital environment.

Why Does This Idea Matter?

Misinformation isn’t just an online annoyance—it has real-world consequences. From false medical advice during pandemics to fake political narratives, misinformation can harm lives, destabilize societies, and erode trust in institutions. With billions of people online, we need scalable, AI-driven systems to ensure truth spreads faster than lies. Neural networks offer the speed, adaptability, and intelligence to make this possible.