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In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects-voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds (575 h) if it was annotated manually. For the validation of the proposed technique, a sub-dataset-Voice, is annotated manually as well as with the proposed technique. Cohen's Kappa statistics is used to evaluate the degree of agreement between the two annotations. The high Kappa value (i.e., 0.9571%) shows the high level of agreement between the two. This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost. This research also contributes in consolidating the guidelines for the manual annotation process.

With worldwide deployment of LoRa/LoRaWAN LPWAN networks in a large variety of applications, it is crucial to improve the robustness of LoRa channel access which is largely ALOHA-like to support environments with higher node density. This article presents extensive experiments on LoRa Channel Activity Detection and Capture Effect property in order to better understand how a competition-based channel access mechanisms can be optimized for LoRa LPWAN radio technology. In the light of these experimentation results, the contribution continues by identifying design guidelines for a channel access mechanism in LoRa and by proposing a channel access method with a lightweight collision avoidance mechanism that can operate without a reliable Clear Channel Assessment procedure. The proposed channel access mechanism has been implemented and preliminary tests show promising capabilities in increasing the Packet Delivery Rate in dense configurations.

In this work, the spectral properties of distributed Bragg reflector-based photonic crystal (DBR-PhC) structures were studied for the near-infrared (NIR) range. Different structural properties were varied to study their effect on the quality of the stopband and the appearance of the resonant dips in the reflection spectra of the DBR-PhC structure. The investigated structural features included the depth of PhC holes, hole radius, and number of PhC elements in the DBR structure. The 11-layered DBR structure was designed with a 2.4/1.4 refractive index contrast of alternating layers. The study aimed to achieve optical filtering properties in the DBR-PhC structure, to simplify the structural complexity of Fabry-Pérot filters by eliminating the FP cavity and upper-DBR mirror. The proposed DBR-PhC device can be used in different optical filtering and sensing applications.