Online feeds are streamed continuously in batches with varied polarities at varying times. The system handling the online feeds must be trained to classify all the varying polarities occurring dynamically. The polarity classification system designed for the online feeds must address two significant challenges: i) stability-plasticity, ii) category-proliferation. The challenges faced in the polarity classification of online feeds can be addressed using the technique of incremental learning, which serves to learn new classes dynamically and also retains the previously learned knowledge. This paper proposes a new incremental learning methodology, ILOF (Incremental Learning of Online Feeds) to classify the feeds by adopting Deep Learning Techniques such as RNN (Recurrent Neural Networks) and LSTM (Long Short Term Memory) and also ELM (Extreme Learning Machine) for addressing the above stated problems. The proposed method creates a separate model for each batch using ELM and incrementally learns from the trained batches. The training of each batch avoids the retraining of old feeds, thus saving training time and memory space. The trained feeds can be discarded when new batch of feeds arrives. Experiments are carried out using the standard datasets comprising of long feeds (IMDB, Sentiment140) and short feeds (Twitter, WhatsApp, and Twitter airline sentiment) and the proposed method showed positive results in terms of better performance and accuracy.

Considering the growth of the IoT network, there is a demand for a decentralized solution. Incorporating the blockchain technology will eliminate the challenges faced in centralized solutions, such as i) high infrastructure, ii) maintenance cost, iii) lack of transparency, iv) privacy, and v) data tampering. Blockchain-based IoT network allows businesses to access and share the IoT data within their organization without a central authority. Data in the blockchain are stored as blocks, which should be validated and added to the chain, for this consensus mechanism plays a significant role. However, existing methods are not designed for IoT applications and lack features like i) decentralization, ii) scalability, iii) throughput, iv) faster convergence, and v) network overhead. Moreover, current blockchain frameworks failed to support resource-constrained IoT applications. In this paper, we proposed a new consensus method (WoG) and a lightweight blockchain framework (iLEDGER), mainly for resource-constrained IoT applications in a permissioned environment. The proposed work is tested in an application that tracks the assets using IoT devices (Raspberry Pi 4 and RFID). Furthermore, the proposed consensus method is analyzed against benign failures, and performance parameters such as CPU usage, memory usage, throughput, transaction execution time, and block generation time are compared with state-of-the-art methods.

Incremental Learning, a machine learning methodology, trains the continuously arriving input data and extends the model's knowledge. When it comes to unlabeled data streams, incremental learning task becomes more challenging. Our newly proposed incremental learning methodology, Data Augmented Incremental Learning (DAIL), learns the ever-increasing real-time streams with reduced memory resources and time. Initially, the unlabeled batches of data streams are clustered using the proposed clustering algorithm, Clustering based on Autoencoder and Gaussian Model (CLAG). Later, DAIL creates an updated incremental model for the labelled clusters using data augmentation. DAIL avoids the retraining of old samples and retains only the most recently updated incremental model holding all old class information. The use of data augmentation in DAIL combines the similar clusters generated with different data batches. A series of experiments verified the significant performance of CLAG and DAIL, producing scalable and efficient incremental model.