According to the report, bespoke solutions will be developed for a “connected enterprise” because India is a booming market for industrial automation and there are an increasing number of partnerships taking place.

Impact of AI & Machine learning

Both artificial intelligence (AI) and machine learning have been utilised in a number of processes to identify fraud, find production irregularities, and more.

The most recent developments in AI have developed methods for dealing with categorization, estimation, and clustering issues that promise to be substantially more valuable.

The most cutting-edge deep learning approaches using artificial neural networks may generate up to $3.5 trillion to $5.8 trillion in value annually, or 40% of the value produced by all analytics techniques, according to a McKinsey & Company review of several hundred AI use cases.

Learn more about how and why industrial automation will impact our lives in the years to come.  

What is Industrial Automation?

Automation is the use of specialised methods and devices that assist human operators in carrying out a task. In other words, automation uses logical programming commands, strong technology, and replaces human involvement.

Industrial automation can be defined as the use of predetermined technologies and automatic control devices to automate the operation and control of industrial processes without the need for human intervention and to produce better results than manual labor can.

Industrial automation systems can identify manufacturing flaws in products produced on an assembly line, recall cars, and maximize machine learning for operational productivity.  

How AI helps in Industrial Automation

AI, machine learning, and industrial robotics technologies have merged into single systems for manufacturing production to give us a number of benefits.

Increased uptime, reduced programming time, and higher productivity are some of the key benefits of industrial automation.

Increased Productivity

One benefit of industrial automation is higher output with quicker and better performance. Industrial automation hastens mass production by slashing assembly times significantly while improving product quality.

As a result, it generates a substantial amount of output for given labor input. Reduced time-to-market also means that newly developed items can be in the hands of customers in a matter of hours or days.

Optimum Cost of Operation

Another major advantage of industrial automation is the integration of several industry processes with automated machinery to minimize manual cycles and effort and bring down the need for human labor. This translates to saving capital spent on employees.

• Improved Product Quality

Automation lessens the need for human interaction, which minimizes the likelihood of human error. By adaptively controlling and monitoring the industrial processes at all phases, from the conception of a product to its final form, uniformity and higher conformance of the result can be maintained.

• Goodbye to manual routine checks say hello to automated checks

Industrial automation reduces the need for manual checking of various process parameters. automation technologies keep track of industrial processes and automatically adjusts process variables to set or desired values using closed-loop control techniques.

Smart water management solutions from IoT Solutionz(an initiative by Pixel Solutionz with smart monitoring and automatic pump control services, helps you cut down on power bills, save water for later use. Read more about IoT in our daily lives: IoT Landscape in India- Present and Prospect 

• Higher Safety and Security

We have observed in the past how blatantly careless manual errors in the management or control of industrial processes, equipment, or production units resulted in significant power waste, excessive use, and even accidents.

With industrial automation, not any more. Automated machines, such as those with auto-off modes, power-saver modes, or automatic warnings for dangerous circumstances, can be controlled and handled in a more structured way.

Industrial automation 2.0 in Manufacturing Industry: Impact of AI & Machine learning

In addition to saving lives, this also conserves energy. There will always be industrial automation. Order-driven manufacturing will cut down on waste and help dynamic supply networks function more quickly and effectively.

To configure the assembly of products, manufacturers will use real-time digital platforms linking consumer and industrial data.

A speedier setup of production lines to produce newer and better items will benefit both consumers and manufacturers. Workforce skills will be greatly impacted by industrial automation. The need for highly technical skills like programming will increase.

Increased social, emotional, and higher order cognitive abilities are also a result of this. According to McKinsey & Company, even while the necessity for physical and manual skills will undoubtedly drop, they will still make up the majority of worker capabilities in many nations in 2030.

Even if network access has not yet reached the far reaches of India, mobile penetration has revolutionised the way transactions are conducted, ushering in a period of transition even though we are still behind. This is because of the Millennial generation, who demand instant gratification and switch service providers (in this case) if they are not met.

How does AI help in banking?

Hence, the banking industry is facing tremendous competition for customer retention. And, to retain the numero uno position you need to be a differentiator. Positioning the bank with the right product will not earn a profit; it is important that you serve the customer with a smile swiftly. In order to do that; it’s time that the banks are now focusing on leveraging Usage of Big Data Analytics Artificial Intelligence and Machine learning to better customer experience

Big Data Analytics

The economy is deluged with huge sets of information; both structured and unstructured. Traditional database and software techniques have their limitations and it is here to address the 4 Vs: Volume; Velocity; Variety & Veracity that the field of Big Data emerged. Today, big data will be used for predictive analytics; to spot business trends; use behavior analytics for understanding consumer patterns,s, and help increase business manifold.

Now, the next question is you have acquired customers but how do you ensure seamless and accurate customer service? This is important from the aspect of customer retention OR else your business will again be a leaking bucket. Today, banks have started using chatbots but again it has its restrictive usage.

Future of banking and AI

It only works by answering one question at a time. The other limitation is predetermined questions that are fed into chatbots and that makes interaction very monotonous. Today with Artificial Intelligence and Machine Learning banking industry is set to think big. It is the time that Virtual Agents is expected to take customer service several notches higher up.

There will be different ways by which these virtual agents will enhance the experience starting from analyzing reports; providing tailor-made answers; working out the advisory role to providing customer support services with relevant solutions. It is a technology and cognitive approach that will enhance the banking experience in a different way. Be the differentiator being the first mover.

It has changed the world in more ways than we could imagine until a few years back. The changes and advancements in various domains will continue and will shape our future. A study reveals that around $6 trillion is estimated to be spent on the Internet of Things (IoT) solutions in the next five years. At present, each and every industry is impacted by it. The Internet of Things has already impacted our lives significantly.

Future trends in IoT in various domains:

• Retail companies:

Retail companies are investing heavily in IoT as they understand the importance of data-driven analytics and also to further improve customer experience. Customers, on the other hand, are enjoying the new experiences made possible because of IoT. The popular application cases in Retail sector are as below:

1. Use of Beacons to send notifications to the consumers about any coupon or sale.
2. Re-inventing trial rooms using virtual and smart mirrors.
3. Smart shirt monitors (like Heart Rate Polo Shirts) to monitor the heart and pulse rate.

• Manufacturing industries:

The manufacturing industry is making use of smart machines to improve the overall manufacturing process and to produce better goods. Industrial automation is now a familiar term which is the use of control systems, such as computers or robots, and information technologies for handling different processes and machineries in an industry to replace a human being. It enhances quality, flexibility, efficiency and productivity. The popular application cases are as below:

1. Application of RFID (radio frequency identification) tags with Wi-Fi infrastructure to track real-time productivity.
2. Application of smart tools to manage manufacturing processes like measuring, drilling, tightening etc. to improve production efficiency.

• Heath sector:

IoT is reshaping healthcare as well. Sensors are collecting data and at the same time, the data can be visible to doctors. This is helping doctors closely to monitor crucial patients from far away. The popular application cases are as below:

1. IoT enabled clinical trial solutions to evaluate specific outcomes.
2. Wearable devices to monitor pulse, heart, walking, biking, and other health data.
3. Sensor-enabled mobile devices to remotely monitor patients with chronic illness.

• Advanced internet facilities:

We are welcoming the era of 5G network which will allow the collection, management, and analysis of data in real-time. We are not far from the time when there won’t be any buffering in the internet speed and we won’t have to wait even for a minute to access information and services. As an increasingly digital-first nation, India is likely to witness a robust NB-IoT network being deployed in 2019. With the incorporation of ICT and IoT, we are heading towards the establishment of a smart city which will enhance operational efficiency, access to information, thereby uplifting the overall quality of life.

It recognizes different objects present in a picture and makes a bounding box around them. YOLO outlines object detection as a regression problem rather than a grouping issue.

Application of YOLO in Real Life

YOLO brings a unified neural network architecture to the table, single architecture which does bounding box prediction and furthermore gives class probabilities.

In YOLO a single convNet all the while predicts various bounding boxes and furthermore the class probabilities for those boxes.

This permits YOLO to improve. YOLO is quick and it reasons about the picture universally while making predictions model, it makes less than half the number of background errors compared to Fast R-CNN. There are many variants of YOLO available like YOLOv3, tiny YOLO, etc.

IMPORTANCE OF YOLO MODEL IN DETECTION:- Object detection is one of the traditional issues in computer vision where you work to perceive what and where — explicitly what objects are inside a given picture and furthermore where they are in the picture.

The issue of object detection is more unpredictable than classification, which additionally can recognize objects yet doesn’t show where the objects are situated in the picture.

Likewise, the classification doesn’t deal with pictures containing more than one object. YOLO utilizes an entirely different methodology. YOLO is a clever convolutional neural network (CNN) for doing object detection in real time.

The algorithm applies a single neural network to the full picture, and afterward divides the picture into areas and predicts bounding boxes and probabilities for every locale. These bounding boxes are weighted by the predicted probabilities.

YOLO is well known on the grounds that it accomplishes high accuracy while likewise having the option to run in real-time. The algorithm “only looks once” at the picture as it requires just one forward propagation pass through the neural network to make forecasts.

After non-max suppression (which ensures the object detection algorithm just detects each object once), it at that point yields recognized objects along with the bounding boxes. With YOLO, a solitary CNN all the while predicts different bounding boxes and class probabilities for those boxes.

YOLO trains on full pictures and straightforwardly improves detection performance. This model has various advantages over other object detection strategies.

• YOLO is amazingly quick
• YOLO sees the whole picture during training and test time so it certainly encodes contextual data about classes as well as their appearance.
• YOLO learns to generalized representations of objects so when trained on natural pictures and tested on the artwork, the algorithm beats other top detection methods.

APPLICATION OF THE YOLO MODEL:-

• VEHICLE DETECTION:-

Different kinds of vehicles i.e. cars, trucks, bikes, buses, trains, boats, bicycles, and flights are detected by the Yolo model in an image and in real-time both. When any of the above vehicles is detected a bounding box is created around that vehicle and the probability of detection is also shown.

The type of vehicle is also shown above the bounding box.

• ANIMAL DETECTION:-

We may use the Yolo model for different types of animal detection in the forest. Yolo model is capable of detecting horses, sheep, cows, elephants, bears and zebra, and giraffes from images and real-time camera feed and recorded videos.

It is also capable of detecting cats, dogs,s, and birds.

• FRUIT, VEGETABLE, AND FOOD ITEMS DETECTION:-

Banana, apple, orange, sandwich, broccoli, carrot, hot dog, pizza, and cake all are detected by Yolo from real-time camera feed, images, and recorded video.

• PERSON DETECTION:-

Detecting individuals can be a significant application across numerous industries. Normal use cases incorporate security applications that track who’s traveling every which way, and who’s coming and going just as safety systems intended to keep individuals out of damage’s way.

In computer vision, we utilize a method called object detection to identify the presence of individuals in a picture. Much of the time, individuals are the only thing an object detection model is fit for detecting.

Likewise, this strategy varies facial recognition in that it doesn’t identify a particular individual, yet just detects when a human is in the frame. 

• OBJECT DETECTION:-

Object detection is the technique of finding and characterizing a variable number of objects on a picture. The significant difference is the “variable” part. Conversely, with issues like classification, the yield of object detection is variable in length, since the number of objects detected may change from picture to picture.

Utilizing the Yolo model we can detect various objects, for example – traffic signals, fire hydrants, stop signs, parking meters, bench, luggage, umbrella, purse, tie, bags, snowboards, sports balls, kites, ash, mitt, skateboard, tennis racket, bottle, wine glass, cup, fork, blade, spoon, bowl, TV screen and so forth thus numerous objects.

Conclusion: So from the above discussion we may say, the application of Yolo Model in real life can enormously profit numerous organizations as well as we know Yolo is one of the most promising models, and it will generate immense impact in retail, industrial and commercial areas.

At Pixel Solutionz, our research team has developed the most modern Yolo-based applications for corporate clients. So for banks, educational institutes, or for any other commercial, industrial or retail application Yolo-based model, do not hesitate to contact us.

Digital Workplace implies a virtual equivalent to the physical workplace, through more personalized customer interactions and a multi-device, collaborative employee environment. The Digital Workplace requires strong planning and management due to its fundamental role in people’s productivity, engagement and working health. Definition of Digital Workplace The term “Digital Workplace” dates back to 2009 when Paul Miller, CEO, and founder of Digital Workplace Group (DWG), used the term in his lexicon. It meant an understanding that exploration into how technology would affect both the workplace and the nature of work would be necessary. A few years later, Miller s book The Digital Workplace: How technology is liberating work was published, which focused on the evolution of the workplace and how work happens. In his book, Miller cited three defining elements of all Digital Workplaces: digital presence, governance, and speed and efficiency. In 2012 Deloitte spoke on similar lines and defined it as one that “… encompasses all the technologies people use to get work done in today’s workplace–both the ones in operation and the ones yet to be implemented. It ranges from your HR applications and core business applications to e-mail, instant messaging and enterprise social media tools and virtual meeting tools.” Gartner’s definition of the Digital Workplace came out in 2014 and they defined it as “an ongoing, deliberate approach to delivering a more consumer-like computing environment that is better able to facilitate innovative and flexible working practices.” As we probe deeper into the subject matter, we find that all three definitions point to one thing: improved efficiency and workforce mobility through the intelligent use of people, processes, and technology. To put it in a simpler way, we may say that the Digital Workplace encompasses the following:

• Putting people first – the impact on employees is what makes the Digital Workplace important
• Technology layer – advances in technology are driving changes in the Digital Workplace
• Management and Design – proactively developing a Digital Workplace means addressing it as a whole and 360-degree co-ordination between technology, process, and people

  The Digital Workplace provides an organization with five services or capabilities:

1. Communication and employee engagement
2. Collaboration
3. Finding and sharing of information and knowledge
4. Business applications (process-specific tools and employee self-service)
5. Agile working – the ability to be productive any time and place

To work well, these needs must be supported by five management activities:

1. Strategic planning
2. Governance and operational management
3. Proactive support for adoption
4. High-quality user experience
5. Robust, secure, and flexible technology

  Email, intranets, and web conferencing are the principal components of the Digital Workplace. Also, what matters is the employer’s emphasis on bringing together the resources from an employee’s point of view. Traditional business systems like SAP, PeopleSoft, databases, and CRM are all part of the non-physical workplace. But these are mostly accepted part of the fabric of most businesses; however, they are part of what should be considered within a Digital Workplace strategy, at least from an alignment point of view.  

Benefits of Digital Workplace

• The Digital Workplace will bring about increased interaction, higher employee engagement, better digital employee experience, and improves efficiency.
• The Digital Workplace will ensure employee empowerment through advanced tools and technologies, leading to better performance at work, increased interaction, and opening up existing silos. Booking meeting rooms, juggling various calendars, distributing agendas and handouts–all will happen without friction.
• The Digital Workplace will improve enterprise mobility by collecting data. It will make use of collected data to deliver measurable results and actionable insights.

  Closing thoughts: In the coming years, the Digital Workplace will continue to evolve. With the exponential growth of mobile and BYOD (Bring Your Own Device), the physical workplace will see a transformation too. Current technologies will be exchanged for new ones, and new technology will be adopted internally. Though creating an effective Digital Workplace may prove a bit challenging for some organizations, the long-term benefits will please them all. Businesses adapting to the changing environment will be better positioned in areas of innovation, productivity, and collaboration. Also, well-executed Digital Workplaces will help organizations attract the right employees to remain competitive in an almost exclusively digital world.   Have questions? Get in touch! We would love to hear from you.

Traffic AI works by gathering information from connected traffic frameworks, which give contributions about live traffic or long stretches of recorded traffic conduct. So as to comprehend this unstructured information, it utilizes AI models to process, examine, and find out about the traffic frameworks. The AI at that point utilizes these bits of knowledge to tackle traffic issues.

PROBLEMS OF CONVENTIONAL TRAFFIC MONITORING SYSTEM:- Conventional traffic monitoring systems are mainly controlled by traffic signals. Disadvantages of traffic signals are- They defer the traffic by halting the vehicles at the convergence during top hours. During signal breakdown, there are severe and widespread traffic challenges during peak hours. While numerous individuals understand that traffic lights can decrease the number of angle collisions at a convergence, few understand that signs can likewise cause an increase in different kinds of accidents.

For instance, it has been all around reported that different kinds of accidents, eminently rear-end collisions, normally increment when a sign is introduced. Typically, traffic engineers are eager to compromise an increment in rear-end collisions for a decline in the more extreme edge mishaps; be that as it may, when there is no point accident issue at a crossing point, there is nothing to compromise, and the establishment of traffic lights can really cause decay in the general security at the intersections.

Traffic signals should not be considered a “cure-all” for traffic congestion, and the primary goal of all traffic engineers is to attain the safest and most efficient traffic flow feasible. In addition to an increase in accident frequency, unjustified traffic signals can also cause excessive delays, disobedience of signals, and diversion of traffic to inadequate alternate routes. Traffic lights ought not to be viewed as a “cure-all” for traffic congestion, and the essential objective of all traffic engineers is to achieve the most secure and most productive traffic stream attainably. Notwithstanding an addition in accident recurrence, uncalled-for traffic lights can likewise cause exorbitant postponements, disobedience of signs, and redirection of traffic to inadequate alternate routes.

HOW AI CAN OVERCOME THE PROBLEMS OF CONVENTIONAL TRAFFIC MONITORING:-

• Eliminating Driver-Inflicted Car Crashes

Driver blunder is the main source of street car accidents. It ought not to come as an amazement, for people are too rigid not to drive while distracted, inebriated, high, or tired. The undeniable answer for such hazardous driver conduct is to pass the controlling wheel to something unequipped for getting occupied, inebriated, high, and languid while driving the manner in which people do. Henceforth the requirement for vehicle automation. A few technologists believe that we are still decades from seeing Level-5 self-autonomous vehicles (AVs) on the streets, yet the tech has just been supporting drivers by means of cutting-edge driver assistance systems.

A definitive objective of AV designers, notwithstanding, is to place AI in the driver’s seat from the get-go. On paper, it bodes well since PCs can be preferred drivers over experienced human drivers themselves. Self-driving vehicles are furnished with front-line cameras, sensors, and radars to see the surrounding factors and other street users so as to anticipate the erratic manners we can never do with our constrained and inferior senses. These advanced vehicles and trucks likewise respond to hazards all the more rapidly. Furthermore, vehicle-to-vehicle correspondence could let them trade data promptly and caution each other about close-by dangers.

• Determining Dangerous Routes

AI-powered vehicles may run on gas and electricity, yet they are fueled by information. Smart vehicles and trucks continually collect data that can deliver noteworthy bits of knowledge with legitimate analytics. Transport interests can utilize such discoveries to identify which stretches of streets are naturally perilous because of some explanation so as to design more secure routes for drivers.

• Streamlining Traffic Patterns

Artificial intelligence and big data analytics can enable specialists to control the progression of vehicular traffic in various regions. Traffic chiefs will have the option to prevent congestion in urban areas or possibly limit gridlocks during busy times. All things considered, they would serve open vehicle suburbanites all the more proficiently. Moreover, AI plays a critical part in foreseeing the ways of vulnerable street users, for example, pedestrians and cyclists. Artificial intelligence-driven traffic management can mean various versatile choices, a diminished number of accidents and losses, and insignificant greenhouse gas outflow.

• Reacting to Emergencies

Some vehicle models use AI to more readily address emergencies. The tech brought forth auto features includes that can recognize health conditions like a heart attack, demand for health services and give pertinent information like vehicle location. Such abilities are especially helpful for truck drivers who work around evening and night time.

• Identifying Driver Weaknesses

Using AI, with all other innovations like face recognition, the managers can monitor driver performance in real-time. This luxury will not only enable the decision-makers to send relief-drivers according to the situation but also diminish bad driving habits. The availability of such information allows transport industry/organizations to provide better training to most of the drivers.

Conclusions: So in this blog we came to know so many things about Traffic Monitoring with AI. As the world is moving towards automation these Traffic Monitoring with AI will be the technology to look forward. The cost, timing and accuracy of the overall traffic system with AI will be much better than the conventional systems. We at Pixel Solutionz are providing end to end solution of Traffic Monitoring with AI. To avail our service doesn’t hesitate to contact us.

• SUPERVISED MACHINE LEARNING FOR ANOMALY DETECTION:

This technique requires a labelled training set that contains both typical and anomalous samples for building the predictive model. Hypothetically, supervised methods are accepted to give a preferred identification rate over unsupervised techniques. Supervised neural systems, the definition of training model, support vector machine learning, k-nearest neighbours, Bayesian systems and decision trees. K-Nearest neighbour (k-NN) is one of the most conventional nonparametric techniques that are utilized in supervised learning for anomaly recognition.

It ascertains the approximate separations between various points on the input vectors and afterwards relegates the unlabelled highlight to the class of its K-nearest neighbours. The Bayesian system is another well-known model that can encode probabilistic connections among factors intrigue. This method is commonly utilized for anomaly identification in the mix with statistical plans. These supervised techniques have a few advantages, including the capacity of encoding interdependencies among factors and of predicting events, alongside the capacity to fuse both earlier knowledge and information.

• UNSUPERVISED MACHINE LEARNING FOR ANOMALY DETECTION

These methods don’t need training data. They depend on two essential presumptions. To begin with, they assume that the vast majority of the system associations are normal traffic and just a limited quantity of rate is abnormal. Second, they foresee that malicious traffic is statistically unique in relation to typical traffic.

Using these two assumptions, information gatherings of comparable cases that show up every now and again are thought to be normal traffic and those data groups that are inconsistent are viewed as malicious. The most widely recognized unsupervised calculations are self-organizing maps (SOM), K- means, C- means, expectation-maximization meta-algorithm (EM), adaptive resonance theory (ART), and one-class support vector machine. One famous method is a self-organizing map (SOM).

The primary goal of the SOM is to decrease the element of data visualization. Machine learning methods are currently accepting significant consideration among anomaly detection analysts to address the shortcomings of information base detection strategies. Anomaly detection can adequately help in getting the fraud, finding bizarre activity in large and complex Big Data sets. This can end up being helpful in territories, for example, banking security, natural sciences, medicine, and marketing, which are inclined to malicious exercises. With the machine, a learning association can strengthen search and increase the adequacy of their advanced business activities. APPLICATION OF ANOMALY DETECTION:-

• ANOMALY DETECTION IN HEALTH CARE:

Unfavourable occasions in healthcare and clinical blunders bring about a huge number of accidental deaths and more than 1,000,000 excess injuries every year. Anomaly detection in medicine is a significant assignment, particularly in the zone of radiation oncology where blunders are rare, however, can be incredibly hazardous, and even destructive To stay away from Medical blunders in radiation disease therapy, cautious attention should be made to guarantee precise usage of the expected treatment plan. Here we portray the work that brought about a significant valuable predictive analytics tool for automatic detection of catastrophic blunders in cancer radiotherapy, including significant protection for patient security.

Here a strategy for Dynamic Modelling and Prediction of Radiotherapy Treatment Deviations from Intended Plans (Smart Tool) has been formulated to naturally recognize and highlight likely mistakes in a radiotherapy treatment plan, in view of the information from a few thousand prostate cancer treatments that were utilized to build the model. Smart Tool decides whether the treatment boundaries are valid, against a formerly manufactured Predictive Model of a Medical Error (PMME). Smart Tool speaks with a radiotherapy treatment management system, checking all the treatment parameters in the background preceding execution, and after the human expert, QA is finished.

Any anomalous treatment parameters are recognized by utilizing an inventive intelligent algorithm in a totally automatic and unsupervised way, and it hails the operator by highlighting the suspect parameter(s) for human mediation. Besides, the framework is self-learning and continually developing, and the model is dynamically refreshed with the new treatment information.

• ANOMALY DETECTION IN THE MANUFACTURING SECTOR:-

Anomaly detection is getting progressively important to upgrade reliability and strength in the Industry 4.0 system. Here are various strategies for anomaly detection on manufacturing machines considering their fluctuation, both in operation and in wear conditions. We can see the type of accessible information, including any abnormality or not, is of significance for the algorithmic decision, talking about both statistical machine learning methods and control charts Finally to develop strategies for automatic anomaly detection, which get a review close to one on the information. The created strategies are structured not to depend on a persistent re-calibration and hand-tuning by the machine user, in this manner permitting their arrangement in an underway domain vigorously and effectively.

• ANOMALY DETECTION FOR AN E-COMMERCE PRICING SYSTEM:-

Online retailers execute an exceptionally huge number of price updates when contrasted with physical stores. Indeed, even a couple of misestimated things can have a noteworthy business effect and result in lost client trust. Early identification of anomalies in an automated real-time fashion is a significant piece of such an apprising system. The system here analyses the performance of different methodologies on a test set utilizing real-world retail information and completely sent this methodology into production. This system is able to find the most important anomalies with high precision.

CONCLUSION: Anomaly Detection has become one of the lethal weapons to detect fault detection, fraud detection and many other discrepancies in data. Several organizations are using Anomaly Detection for cost-effective and hazard-free business operations. We at Pixel Solutionz are providing end-to-end solutions for Anomaly Detection with AI. To avail of our service don’t hesitate to contact us.