AIMLSYSTEMS

For autonomous driving, safer travel, and fleet management, Vehicle-to-Vehicle (V2V) communication protocols are an emerging area of research and development. State-of-the-art techniques include machine learning (ML) and reinforcement learning (RL) to ...

Domain specific hardware architectures and hardware accelerators have been a vital part of modern system design. Especially for math intensive applications involving tasks related to machine perception, incorporating hardware accelerators that work in ...

Electric vehicle (EV) fleets are well suited for last-mile deliveries both from sustainability and operational cost perspectives. To ensure economic parity with non-EV options, even captive chargers for EV fleets need to be managed intelligently. ...

Product images are the most impressing medium of customer interaction on the product detail pages of e-commerce websites. Millions of products are onboarded on to webstore catalogues daily and maintaining a high quality bar for a product’s set of ...

We showcase a novel solution to a recommendation system problem where we face a perpetual soft item cold start issue. Our system aims to recommend demanded products to prospective sellers for listing in Amazon stores. These products always have only ...

We model the read-workload experienced by an email server as a superposition of reads performed by different software clients at non-deterministic times, each modeled as a dependent point process. The probability of a read event occurring on an email ...

Wildfires can cause significant damage to forests and endanger wildlife. Detecting these forest fires at the initial stages helps the authorities in preventing them from spreading further. In this paper, we first propose a novel technique, termed ...

In this work, we propose a Deep Learning (DL) based error correction system termed as DEC. It predicts the transmitted symbols at the receiver using the received soft symbols and channel state information (CSI) of the transmission link. Hence, the ...

Convolutional Neural Networks (CNNs) have grown in popularity and usage tremendously over the last few years, spanning across different task such as computer vision tasks, natural language processing, video recognition, and recommender systems. Despite ...

Deep Learning architectures used in computer vision, natural language and speech processing, unsupervised clustering, etc. have become highly complex and application-specific in recent times. Despite existing automated feature engineering techniques, ...

We see two trends emerging due to exponential increase in AI research- rise in adoption of AI based models in enterprise applications and development of different types of hardware accelerators with varying memory and computing architectures for ...

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning ...

It is required to detect distribution shift in order to prevent a machine learning model from performance degradation, and human-mediated data analysis from erroneous conclusions. For the purpose of comparing between unknown distributions of high-...

Deep Neural Networks (DNNs) are generally designed as sequentially cascaded differentiable blocks/layers with a prediction module connected only to its last layer. DNNs can be attached with prediction modules at multiple points along the backbone where ...

Images captured through smartphone cameras often suffer from degradation, blur being one of the major ones, posing a challenge in processing these images for downstream tasks. In this paper we propose low-compute lightweight patch-wise features for ...

The deeper and wider architectures of recent convolutional neural networks (CNN) are responsible for superior performance in computer vision tasks. However, they also come with an enormous model size and heavy computational cost. Filter pruning (FP) is ...

Non-orthogonal multiple access (NOMA) technology aided with successive interference cancellation (SIC) is expected to achieve multi-fold improvements in the network capacity. However, the SIC in practice is prone to imperfections and this degrades the ...

Online 3D bin packing is a challenging real-time combinatorial optimisation problem that involves packing of parcels (typically rigid cuboids) arriving on a conveyor into a larger bin for further shipment. Recent automation methods have introduced ...

Deep Neural Networks (DNN) have made machine learning accessible to a wide set of practitioners working with field deployment of analytics algorithms over sensor data. Along with it, focus on data privacy, low latency inference, and sustainability has ...

Decision forests have proved to be useful in machine learning tasks. gcForest is a model that leverages ensembles of decision forests for classification. It combines several decision forests and by adding properties and layered architecture in such a ...

Businesses are increasingly reliant on Machine Learning models to manage user experiences. It becomes important to not only focus on building robust and state-of-the-art models but also continuously monitor and evaluate them. Continuous monitoring ...

Many e-commerce companies use machine learning to make customer experience better. Even within a single company, there will be generally many independent services running, each specializing in some aspect of customer experience. Since machine learning ...

Hyperlocal e-commerce companies in India deliver food and groceries in around 20-40 minutes, and more recently, some companies focus on sub-ten-minute delivery targets. Such "instant" delivery platforms referred to as quick (q)-commerce onboard GPS ...

Quality assurance is required for the wide use of artificial intelligence (AI) systems in industry and society, including mission-critical areas such as medical or disaster management domains. However, the quality evaluation methods of machine learning ...

Often computer/mobile users call everything that disturbs/corrupts their system a VIRUS without being aware of what it means or accomplishes. This tutorial systematically introduces the different malware varieties, their distinctive properties, ...

Telecommunications networks operate on enormous amount of time-series data, and often exhibit anomalous trends in their behaviour. This is caused due to increased latency and reduced throughput in the network which inevitably leads to poor customer ...

Resource-constrained platforms such as micro-controllers are the workhorses in embedded systems, being deployed to capture data from sensors and send the collected data to cloud for processing. Recently, a great interest is seen in the research ...

Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to ...

Adoption of cloud computing by enterprises has exploded in the last decade and most of the applications used by enterprise users have moved to the cloud. These applications include collaboration software(e.g., Word, Excel), instant messaging (e.g., ...