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IoT in Transportation: Revolutionizing Mobility with Connected Vehicles and Smart Infrastructure

Ishani Mohanty
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0
IoT in Transportation: Revolutionizing Mobility with Connected Vehicles and Smart Infrastructure

Transportation is just one of the many industries that the IoT is changing. IoT is transforming mobility and improving the safety, efficiency, and environmental friendliness of transportation networks through linked cars and smart infrastructure. Here are some examples of how IoT will influence transportation in the future.

Automotive Networks: Creating the Future

The most notable example of how IoT is affecting transportation is with connected cars. These cars collect and exchange data in real time using sensors, GPS, and communication technology. In addition to improving efficiency and safety, this connectivity improves driving in several ways.

Enhanced Safety: By communicating with infrastructure and one another (V2V and V2I), connected cars can notify other drivers of impending dangers such abrupt stops or road closures. Both traffic flow and accident rates can be greatly improved by doing this.

Predictive Maintenance: IoT-enabled sensors track the condition and performance of vehicles, sending out early alerts when maintenance problems arise before they cause malfunctions. This prolongs the vehicle’s lifespan and lowers the need for unforeseen maintenance.

Routes That are Optimized: Vehicles can select the most fuel-efficient routes and save time and fuel thanks to real-time data on accidents, roadworks, and traffic conditions.

Smart Infrastructure: Constructing the Future’s Intelligent Highways

IoT encompasses more than simply connected cars; it also entails building intelligent infrastructure to facilitate and improve mobility. This is the way that intelligent infrastructure is changing transportation:

Intelligent Traffic Management: Intelligent traffic management reduces waiting times at intersections by minimizing congestion using smart traffic lights and sensors that gather data on traffic flow and modify signal timings accordingly.

Smart Parking Solutions: IoT-enabled parking sensors and meters give drivers real-time information on open spots, cutting down on the amount of time they spend looking for a place to park.

Dynamic Roadway Information: By giving drivers access to up-to-date information on traffic, weather, and road closures, digital signage and smart highways enhance traffic control.

Benefits to the Environment: Greener Transportation

Another important way that IoT is contributing to the sustainability of transportation is:

Efficient Fuel Use: Connected vehicles reduce emissions and fuel consumption by optimizing routes and cutting down on idle time, which helps to create a cleaner environment.

Integration of Electric Vehicles: IoT technologies facilitate the expansion of electric vehicles (EVs) by offering information on the availability of charging stations and streamlining the charging process, hence increasing the use and accessibility of EVs.

Smart Urban Planning: Information gathered from IoT devices can be utilized to plan and execute more environmentally friendly mobility options, like enhanced public transportation and bike-sharing schemes.

Obstacles and Prospects for the Future

IoT has many advantages, but there are also issues that must be resolved. These include:

Data Security and Privacy: It is critical to protect the security and privacy of the enormous amount of data being collected. Strong cybersecurity defenses are required to fend off illegal access and data breaches.

Interoperability: Different platforms and technologies need to work together harmoniously for IoT systems to operate properly. To fully realize the potential of smart transportation, it is imperative to ensure compatibility between diverse IoT devices and systems.

Infrastructure Investment: A substantial amount of money is needed to implement smart infrastructure. To fully enjoy the advantages of new technologies, governments and private sector participants must work together to finance and implement them.

Conclusion

Future transportation will be smarter, more efficient, and sustainable thanks to the IoT. The opportunities to improve mobility and the transportation experience are almost endless as long as technology keeps developing.

Why Inclusive Design is More Important Than Ever in Web Accessibility

Vaishnavi K V
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0
Why Inclusive Design is More Important Than Ever in Web Accessibility

Creating an accessible web is no longer a choice—it’s a necessity. Web accessibility ensures that users with disabilities can easily navigate and interact with websites, providing an inclusive experience for all. From visual impairments to cognitive challenges, modern web design must cater to every user. As regulations tighten, and user expectations grow, embracing web accessibility is vital for businesses aiming to stay competitive and compliant.

The Case for Inclusive Design

In today’s digital-first world, inclusive design goes beyond aesthetics. It’s about making the web usable for everyone. Companies that prioritize web accessibility not only support people with disabilities but also improve their brand’s reputation. An inclusive website helps all users, even those without disabilities, to enjoy smoother navigation and faster interactions.

Furthermore, web accessibility can enhance SEO by creating cleaner, more structured content. This increases visibility while adhering to legal requirements like the Americans with Disabilities Act (ADA).

Key Trends in Web Accessibility Compliance

AI-Powered Accessibility Tools

AI is making strides in web accessibility. Tools that automatically check and fix accessibility issues are becoming increasingly popular. These tools help ensure that images, videos, and forms are accessible to screen readers and other assistive technologies.

Also Read: Benefits and Implementation of Dark Mode in Modern Web Design

Voice Search Compatibility

With voice search on the rise, designing for speech-enabled interfaces is crucial. Voice recognition offers hands-free browsing, making the web more accessible to users with mobility or visual impairments.

Keyboard-Only Navigation

Many users rely on keyboards rather than a mouse to navigate websites. Designing for keyboard accessibility ensures users can interact with every element, from links to forms, without using a mouse.

The Bottom Line

Embracing web accessibility isn’t just about compliance. It’s about creating equal opportunities for all users to engage with your content. As technology evolves, inclusive design will continue to be a major factor in delivering superior user experiences. Now is the time to integrate accessibility into your web platforms—because an inclusive web is a better web for everyone.

Just AI: Ensuring Equity in Environmental Solutions

Samita Nayak
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0
Just AI: Ensuring Equity in Environmental Solutions

Artificial Intelligence or AI can change our relationship with the natural world and our reaction to environmental problems in ways never previously thought possible. However, if designed and introduced crudely, AI also has the potential to solidify current environmental injustices and create new ones. To ensure that all communities benefit fairly from AI, environmental justice must form part of its design and delivery.

ALSO READ: Cognitive Computing vs. AI: What’s the Difference?

Understanding Environmental Justice

It is an environmental justice principle that all people, regardless of their race, ethnicity, income, or geographic location, have equal rights to a healthy and safe environment: clean air, water, and land; and protection from environmental harm. Environmental disparities more often than not fall on the most helpless in society, perhaps the ones enjoying fewer resources and political clout to defend themselves.

Role of AI in Environmental Solution

AI has the potential to be very useful in the mitigation of environmental damage. For example, it can be applied to air and water quality monitoring, the prediction of natural disasters, and the development of sustainable forms of energy. However, all benefits from AI are not equitably distributed. If AI is developed and deployed without consideration for environmental justice, it will merely perpetuate existing inequalities.

Challenges and Opportunities

The lack of diversity in the AI field has been one of the major challenges to environmental justice being able to be assured through AI. This is because it can result in the development of AI systems that are biased against certain communities.

For example, facial recognition systems may have low performance on persons of color, due to discriminatory outcomes. Of course, the first step is to diversify the people who work on AI and ensure that AI systems are developed and tested with a diversified set of data.

Another challenge is that AI might be used in order to surveil and control marginalized communities. Examples include AI-powered drones patrolling demonstrations or tracking the movements of individual people. To prevent this, serious ethical guidelines concerning the usage of AI need to be enacted, while AI systems should fall under oversight and accountability.

At the same time, AI will provide huge opportunities for the betterment of environmental justice. We can build a just and sustainable future for all by equitably, inclusively, and transparently developing AI systems.

Conclusion

Equity, inclusivity, and transparency are key elements that constitute environmental justice within AI. Taking up the challenges and opportunities, we can use AI to execute just and sustainable outcomes for all.

Combatting Deepfake Technology: The Next Frontier in Cyber Defense

Jijo George
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Combatting Deepfake Technology: The Next Frontier in Cyber Defense

Deepfake technology is sending waves across digital landscape. Using AI, it creates realistic fake videos and audio. These complex manipulations are used for fraud, misinformation campaigns and even cyber attacks. As the technology advances, defending against deepfakes becomes a priority for cybersecurity experts. Let us dive in!

The Rise of Deepfakes

Deepfakes use deep learning techniques to generate convincing fake images, videos, and audio. Recent data shows a 900% increase in deepfake videos online from 2019 to 2024. The quality has improved dramatically, making detection harder. Cybercriminals exploit deepfakes for various malicious purposes, from corporate espionage to social engineering attacks.

Cyber Threat Landscape Involving Deepfakes

Deepfakes have transformed the cyber threat landscape. They are used for impersonation attacks, identity theft, and financial scams. In a recent case, attackers used deepfaked voice recordings to impersonate a CEO. This led to a fraudulent transaction worth $243,000. Another case involved a fake video of a government official making controversial statements. This resulted in social unrest and stock market fluctuations.

Deepfake-powered phishing is also rising. Attackers use deepfake videos of trusted figures to manipulate employees or executives. This is called “vishing” (voice phishing) or “spear-phishing.”

Current Detection and Mitigation Techniques

Combatting deepfakes requires sophisticated detection and mitigation strategies. Current methods include AI-based detection tools, digital watermarking, and blockchain-based authentication.

  • AI-Based Detection Tools: These tools analyze inconsistencies in facial movements, lighting, and shadows. They use machine learning to detect anomalies in videos. However, as deepfake algorithms improve, these tools need constant updates.
  • Digital Watermarking: This technique embeds hidden data into media files, acting as a digital fingerprint. It helps verify the authenticity of content. However, watermarks can be tampered with, requiring additional layers of security.
  • Blockchain-Based Authentication: Blockchain technology ensures data integrity. By creating an immutable ledger, it helps verify the source and history of media content. This technique is robust but has scalability and implementation challenges.

Advanced Approaches to Combat Deepfakes

Advanced methods are being developed to counter deepfakes more effectively. These approaches focus on real-time detection, multi-factor authentication, and behavioral analysis.

  • Real-Time Deepfake Detection: Real-time AI models can analyze video streams and flag potential deepfakes instantly. Companies like Microsoft and Adobe are developing tools to detect deepfakes in real-time. This approach can help in social media and news outlets to filter fake content before it spreads.
  • Multi-Factor Authentication (MFA) with Biometrics: Traditional authentication methods are increasingly vulnerable to deepfake attacks. MFA using biometrics like facial recognition and voiceprints adds an extra security layer. Biometric data can be cross-referenced with existing records, making deepfake impersonations harder.
  • Behavioral Analysis and Contextual AI: This approach uses AI to analyze behavioral patterns and context to detect anomalies. For example, if a deepfake video shows a CEO making unusual statements, the system can cross-check this behavior against historical data. This technique helps identify deepfakes that bypass visual and audio checks.

The Role of Regulations and Industry Collaboration

Regulations and industry collaboration are critical in combating deepfakes. Governments worldwide are enacting laws against deepfake creation and distribution. The U.S. introduced the DEEPFAKES Accountability Act, mandating digital watermarks on AI-generated content. The EU has similar regulations under the Digital Services Act.

Collaboration between tech companies, governments, and academia is essential. Joint initiatives like the Deepfake Detection Challenge by Facebook and Microsoft promote the development of better detection tools. Sharing threat intelligence and datasets can accelerate innovation in deepfake defense technologies.

The Future of Deepfake Technology and Cyber Defense

As deepfakes evolve, so must cyber defense strategies. AI and machine learning advancements will fuel both deepfake creation and detection capabilities. The future will see a race between deepfake developers and cybersecurity experts.

We can expect more AI-powered tools for deepfake detection integrated with security systems. New techniques like Federated Learning will allow data sharing without compromising privacy, enhancing detection models. Additionally, leveraging quantum computing may offer breakthroughs in deepfake detection speed and accuracy.

The Importance of Awareness and Education

While technology is crucial, awareness and education are equally important. Organizations must train employees to recognize potential deepfake scams. Regular updates on emerging threats and internal policies can reduce susceptibility to deepfake-based attacks.

Public awareness campaigns can also help in identifying and reporting deepfake content. Social media platforms and news agencies must educate users about identifying fake content. Collaborative efforts can make it harder for deepfake content to gain traction.

Conclusion

Deepfakes are a growing threat in the cybersecurity landscape. Their potential to disrupt businesses, manipulate markets, and influence public opinion is vast. Combating deepfake technology requires a multi-layered approach combining advanced AI, industry collaboration, regulations, and public awareness.

As deepfake technology continues to advance, staying ahead requires constant vigilance, innovation, and proactive defense strategies. The fight against deepfakes is the next frontier in cybersecurity, and the stakes have never been higher.

Also read: Phishing Attacks Targeting Hospitals and Patients

The Impact of AI on Workplace Communication: Enhancing Collaboration or Creating Distance?

Ishani Mohanty
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0
The Impact of AI on Workplace Communication: Enhancing Collaboration or Creating Distance?

In today’s workplaces, AI is a revolutionary force that is changing the way teams interact and work together. Although AI tools promise efficiency and innovation, there are concerns about whether they improve employee collaboration or unintentionally drive a barrier between them. Let’s examine the double effects of AI on communication in the workplace.

Boosting Cooperation: The Advantages of AI-Powered Communication

AI has greatly increased workplace communication, mostly through collaboration and procedure simplification. Here are some significant ways AI improves communication:

Automated Routine Tasks: AI can take care of repetitive duties like email sorting, meeting scheduling, and reminders, freeing staff members to concentrate on more important work. Routine conversation takes less time with tools like AI-driven chatbots that can respond instantly to typical questions.

Real-Time Translation and Transcription: Communication can be hampered by language difficulties in international organizations. AI-driven translation and transcription instruments facilitate smooth cross-linguistic communication across teams, promoting cooperation in heterogeneous settings.

Tailored Communication: AI can provide tailored interactions by analyzing communication patterns and preferences. AI can, for instance, recommend the ideal times to get in touch with coworkers depending on their availability, guaranteeing more efficient communication.

Improved Data Sharing: AI systems can combine data from many sources, increasing the usability and accessibility of information. Collaboration across departments and well-informed decision-making are encouraged by this improved data exchange.

Distancing: The Possible Drawbacks of AI in Communication

Although AI has many advantages, it can also cause a rift in the workplace. The following are some issues that businesses should think about.

Less Human Interaction: Automating communication duties may result in fewer in-person or phone conversations, which are essential for fostering a culture of trust and goodwill among team members. An excessive dependence on AI may cause communication to seem impersonal, which could have an impact on team chemistry.

Misinterpretation of Tone and Context: AI systems frequently have trouble comprehending the nuances, tone, and context of spoken language. This might result in misconceptions, particularly in delicate or sophisticated talks where empathy and human judgment are essential.

Overwhelming Information Overload: AI can provide enormous volumes of data and insights, which, if improperly managed, could overwhelm staff members. Effective communication may be hampered by the constant barrage of AI-generated messages and recommendations, which might produce noise rather than clarity.

Privacy Issues: AI systems frequently need access to enormous volumes of individual and company data. This gives rise to worries about data protection and the possibility of abuse, which can breed mistrust and obstruct honest dialogue.

Conclusion

AI must transform workplace communication by providing instruments that improve teamwork and expedite procedures. It also runs the risk of dividing people and degrading interpersonal relationships. Organizations may fully utilize AI while preserving a connected and cooperative workplace by finding a balance between AI and human communication.

How to Boost Your Business with Data Analytics Training

Vaishnavi K V
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How to Boost Your Business with Data Analytics Training

In today’s digital era, companies are increasingly turning to corporate data analytics training to stay ahead of the curve. With businesses generating more data than ever, the need for skilled professionals who can transform raw data into actionable insights is critical. This training equips teams with the skills needed to make data-driven decisions that can propel business growth and innovation.

Why Corporate Data Analytics Training Matters

Corporate data analytics training offers more than just learning to use analytics tools; it empowers employees to understand and leverage data effectively. By developing a strong foundation in data analysis, employees can identify trends, forecast future outcomes, and make strategic decisions based on concrete evidence. This capability not only enhances operational efficiency but also drives competitive advantage.

Key Components of Effective Training

A well-structured corporate data analytics training program covers several essential components.

Understanding Data Fundamentals

Trainees learn about different types of data, data sources, and how to clean and prepare data for analysis. This foundational knowledge is crucial for accurate and meaningful insights.

Analytics Tools and Techniques

Training programs often include hands-on experience with tools such as SQL, Excel, and advanced analytics software. Participants gain practical skills in data manipulation, visualization, and statistical analysis.

Also Read: Self-Service Data Analytics Tools for Everyone

Interpreting Data and Making Decisions

Beyond just analyzing data, effective training helps professionals interpret results and apply them to business scenarios. This involves creating actionable insights that drive decision-making processes.

Data Security and Compliance

Understanding data privacy laws and best practices is also a critical aspect of corporate data analytics training. Ensuring that data handling complies with regulations protects the company from potential legal issues.

Benefits of Investing in Data Analytics Training

Investing in corporate data analytics training yields numerous benefits. Companies experience improved decision-making, enhanced strategic planning, and better resource management. Trained employees can identify new business opportunities and improve operational efficiencies, leading to increased profitability.

Conclusion

Corporate data analytics training is a powerful investment that can transform your organization. By equipping your team with the skills to analyze and interpret data effectively, you position your business for long-term success. Embrace the power of data analytics and unlock new opportunities for growth and innovation.

Ethical Considerations in Intelligent Automation

Samita Nayak
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Ethical Considerations in Intelligent Automation

As intelligent automation (IA) finds greater absorption across businesses, the ethical issues surrounding it assume critical dimensions. Combining AI with machine learning and automation, IA promises to revolutionize business operations by upping productivity and further enriching customer experiences. At the same time, this transformational technology also raises significant ethical questions that must be carefully examined to ensure its responsible implementation.

ALSO READ: The Rise of Automation-Specific Threats

Privacy and Data Security

Among the top ethical considerations in intelligent automation, privacy ranks high. The generally applicable fact that IA systems require a great deal of data to perform effectively may affect an individual’s right to privacy. Organizations shall handle the collection, storage, and processing of such data with complete transparency about their intent and adherence to relevant regulations regarding privacy, such as GDPR. The sensitive data has to be made safe from leakage and unauthorized access to retain the trust and protect the rights of individuals.

Bias and Fairness

The intelligent automation systems are only as unbiased as the data they are trained on. The IA system projects and further aggravates the biased underlying data. Examples include recruiting processes, whereby an IA system may eventually be prejudicially set against a particular demographic group based on biased training data. Organizations must, therefore, make active efforts toward the eradication of bias in data and the design of IA systems with the view of fairness and equity in mind if this risk is to be mitigated.

Job Displacement and Economic Impact

Automation of tasks previously performed by humans does raise concerns about job displacement and broader economic impacts. While intelligent automation enables higher efficiency and cost savings, there is also a risk of displacing workers, especially in jobs that are repetitive. The social implications of IA have to be considered, and organizations need to develop strategies that take care of workers affected by automation through re-skilling programs, and initiatives on creating new job opportunities, among others.

Transparency and Accountability

As IA systems become increasingly complex, there will be a greater need for transparency regarding decisions made. The stakeholders involved are employees, customers, and regulators who have to understand not only how the IA systems work but also why they decide on one particular solution and not another.

Besides this, the necessary provisions should also be made by organizations regarding accountability mechanisms to handle adverse consequences from the use of IA. That means clearly defining who is responsible for decisions made by the automatic system and that mechanisms are in place to deal with errors or unintended outcomes which may result.

To Conclude

While the prospects presented by intelligent automation are bright, ethical issues of some sort cannot be completely ignored. It will be possible for organizations to responsibly use intelligent automation by paying proper attention to privacy, eradicating bias, tackling job displacement, and ascertaining transparency and accountability. As IA continues to evolve, responsible progress will come from a core ethos of ethics that makes sure technology serves humanity for the greater good.

Cloud Data Lakes vs. Data Warehouses: Which Is the Best?

Jijo George
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0
Cloud Data Lakes vs. Data Warehouses: Which Is the Best?

In cloud computing, data lakes and data warehouses are essential. These systems help manage vast amounts of data, both structured and unstructured. Choosing between them impacts performance, scalability, and cost. This blog explores the differences in data lakes and data warehouses using the latest data from 2024.

Understanding the Basics of Data Lakes and Data Warehouses

Data Lakes store raw data in its native format. They handle structured, semi-structured, and unstructured data. Data lakes are great for data scientists needing advanced analytics. However, they are complex to manage and require robust data governance.

Data Warehouses store structured data optimized for high-speed querying and reporting. Data must be cleaned and structured before storage. This makes data warehouses efficient for analysis but often more costly.

Comparing the Performance Metrics

Query Speed and Throughput

Data lakes are strong in processing large volumes of unstructured data. Platforms like Apache Hadoop or Amazon S3 with AWS Athena excel here. However, querying structured data can be slower due to the lack of pre-defined schemas. Tools like Apache Parquet improve performance but need careful tuning.

Cloud-based data warehouses, like Amazon Redshift, Google BigQuery, and Snowflake, excel in querying structured data. They use columnar storage and indexing, which reduces query latency. In tests, Snowflake and BigQuery outperformed data lakes in complex queries.

Scalability and Elasticity

Data lakes scale well, handling petabytes of data without degrading performance. However, scaling query performance can be challenging, especially with unstructured data. Cloud-native solutions like Azure Data Lake have improved scalability but managing resources is still complex.

Data warehouses also scale well, especially with compute resources. Platforms like Redshift and BigQuery automatically adjust compute power based on query complexity. This elasticity is a major advantage, ensuring consistent performance.

Data Processing and Transformation

Data lakes store raw data, but processing it into usable formats requires significant computational resources. Tools like Apache Spark help, but ETL (Extract, Transform, Load) processes can be slow compared to structured environments.

Data warehouses are optimized for efficient data transformation. With structured data ingestion, ETL processes are simpler, leading to faster processing times. Snowflake’s Snowpipe, for example, enhances real-time data processing.

Cost Metrics

Storage Costs

Data lakes offer low-cost storage, with platforms like Amazon S3 and Azure Blob Storage being very affordable. However, frequent data retrieval can offset these savings, especially with large datasets.

Data warehouses typically have higher storage costs due to the need for data preprocessing. However, columnar storage and data compression help mitigate these costs. Costs are also tied to the amount of data processed, which can be high for large-scale analytics.

Compute Costs

Compute costs in data lakes are generally lower for simple data storage. However, running complex analytics on raw data can be expensive. Frameworks like Apache Spark add to these costs when used extensively.

Data warehouses often incur higher compute costs, especially with complex queries. Platforms like Snowflake offer per-second billing, providing cost flexibility. Still, the overall compute expenses can be significant.

Operational Costs

Managing a data lake can be costly, especially in terms of data governance and security. The complexity of maintaining a data lake requires specialized skills, leading to higher operational costs.

Data warehouses generally have lower operational costs. They come with built-in management tools, reducing administrative overhead. However, initial setup and ongoing tuning can still be expensive.

Hybrid Approach for the Win

Given the trade-offs, many organizations are adopting hybrid architectures. A hybrid approach uses a data lake for raw, unstructured data and a data warehouse for structured data. This allows for cost-effective storage with high-speed analytics where needed.

Recent advancements in cloud services have made hybrid approaches more viable. Amazon’s Lake Formation integrates with Redshift for seamless data movement. Similarly, Google’s BigQuery Omni enables querying across multi-cloud environments, combining the flexibility of a data lake with the performance of a data warehouse.

Also read: How to Choose the Right SaaS Provider for Your Business

5 Best Free Photoshop Alternatives You Need to See

Aiswarya MR
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0
5 Best Free Photoshop Alternatives You Need to See

Tired of everything being a subscription to edit photos? Need to edit photos without subscribing or downloading an app? The cost and impatience around this can be high. Adobe Photoshop may be the industry standard. But there are plenty of free alternatives for it out there. Before looking at some of those in detail, let us first understand what the best photo editing software has to offer.  

What are the features of the best free photo editing software? 

  • Not Just Providing Basic Features: Adjusting brightness, contrast, cropping are some of the basic features available in all editing apps. What sets the best apart are advanced features like curves, clone/healing brushes, blending modes, RAW support, and more. 
  • Easy Usage: The basic layout of Photoshop has remained the same for almost 30 years now. This is because it is intuitive, and it works. The best free photo editor apps also aim for the same easy usage. 
  • Adding Text and Design Elements: Today’s tools are required to include trending fonts and templates, graphic elements and so on to make creating social media content easier and faster. 

Based on the above criteria and other usual metrics, the following apps are considered to be some of the best in the field of free photo editing: 

Best alternative for maintaining brand consistency across remote teams

Canva is targeted at beginner designers, but it does have features that most pro editors have. But its best strength is achieving brand consistency on the free version. You can do this by just keeping your logo files and brand colors images in Uploads or a shared Google Drive folder imported to Canvas.  

Best alternative for artistic effects 

Colorcinch is not only for adding special effects. You can add graphic layers and text as well. Colorcinch is good for effects as it does not overdo it. There are subtle overlays, like sun flares, light leaks, and textures, and dramatic ones, like falling snow and fog. Each of these categories has at least one free option. 

Best alternative for an authentic Photoshop experience

Pixlr E and Pixlr X – Pixlr E is the advanced version and Pixlr X is the one for beginners. Pixlr E is a convincing replica of Photoshop. Layers management is one of its highlights. You get separate layers with the same blending modes and opacity settings as in Photoshop.  

Best alternative for advanced photo editing

Polarr has a clean and minimal design. It has combined features of both Lightroom and Photoshop. For more advanced features like gradient, color, text, liquify, and brush mask effects, you have to get the Pro version though.  

Best alternative for retouching and curves control

Fotor has sophisticated options like editing curve, grain, and noise level, all for free. Some effects though are paid features, like their AI-powered “1-tap enhance” and the background and object removers. 

Also read: Biometric Authentication: The Future of Cyber Security

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