Nowadays, online communication is more convenient and popular than face-to-face conversation. Therefore, people prefer online communication over face-to-face meetings. Enormous people use online chatting systems to speak with their loved ones at any given time throughout the world. People create massive quantities of conversation every second because of their online engagement. People's feelings during the conversation period can be gleaned as useful information from these conversations. Text analysis and conclusion of any material as summarization can be done using sentiment analysis by natural language processing. The use of communication for customer service portals in various e-commerce platforms and crime investigations based on digital evidence is increasing the need for sentiment analysis of a conversation. Other languages, such as English, have well-developed libraries and resources for natural language processing, yet there are few studies conducted on Bangla. It is more challenging to extract sentiments from Bangla conversational data due to the language's grammatical complexity. As a result, it opens vast study opportunities. So, support vector machine, multinomial naïve Bayes, k-nearest neighbors, logistic regression, decision tree, and random forest was used. From the dataset, extracted information was labeled as positive and negative.

Varying channel conditions, dynamic traffic flows, interference and congestion are the main challenges to achieve high-throughput data delivery in multi-radio multi-channel Wireless Mesh Networks (WMNs). The performance of existing solutions are limited either for using statically computed end-to-end relay paths or myopic forwarding decisions. In this paper, we consider the problem of high-throughput traffic forwarding that involves good quality link selection, channel allocation and power control at each forwarding router. Every router chooses a set of outgoing link-channel pairs and their power allocations through a mixed-integer-non-linear programming (MINLP) solution that maximizes its sum total outgoing flow rate while keeping interference and congestion at minimum. Since, the MINLP optimization function is an NP-hard one, a Reinforcement learning based system for Link-Channel selection and Power allocation, namely RLCP, is developed. A comprehensive design of the RLCP system has been presented containing portrayal of the system state, design of a reputation metric and a mechanism to learn the control policy. We have carried out exhaustive simulations on NS-3 and found the proposed RLCP system to prove its efficacy in terms of aggregated throughput, flow fairness and packet delivery delay.

Precision agriculture harnesses data-driven techniques to optimize crop production, resource use, and sustainability. However, low-income countries like Bangladesh face a shortage of localized, high-quality datasets that reflect regional agroclimatic conditions and cropping practices. To address this gap, we present SPAS-Dataset-BD, a robust dataset compiled through a hybrid approach: secondary extraction from the Bangladesh Bureau of Statistics (BBS) 2022 Yearbook and primary on-field surveys of 223 farmers across ten diverse districts. The dataset comprises 4191 records over 73 crop types, with 12 agronomic and environmental features, including underrepresented species. Robustness is demonstrated via threshold-based missing-value handling (<5 % deletion, targeted imputation), hash-based deduplication, and cross-validation against official statistics. We illustrate potential applications, in machine learning (73-class crop classification, yield forecasting) and IoT-driven irrigation scheduling. SPAS-Dataset-BD’s scale, methodological transparency, and contextual richness make it a valuable resource for precision agriculture research and policy-making in South Asia.

IoT is a blessing in the field of information and technology. It is developing and deploying day by day. It is working for our betterment in the section of home, environment, retail, security, factory, industry, agriculture, education, energy, healthcare and so on. In the healthcare section, it has applications, technologies, benefits and also challenges with the four-step architecture. As the world is aging with its uprising population, there is not enough opportunity for all to get healthcare. I think Health is the most expensive property one can own. So in the absence of sufficient and quality healthcare, IoT can manufacture the best utilization of its capacity to play a vital role in creating a better world for all of us. This paper represents the basic concept of IoT in the healthcare field.