Early diabetes diagnosis allows patients to begin treatment on time, reducing or eliminating the risk of serious consequences. In this paper, we propose the Neutrosophic-Adaptive Neuro-Fuzzy Inference System (N-ANFIS) for the classification of diabetes. It is an extension of the generic ANFIS model. Neutrosophic logic is capable of handling the uncertain and imprecise information of the traditional fuzzy set. The suggested method begins with the conversion of crisp values to neutrosophic sets using a trapezoidal and triangular neutrosophic membership function. These values are fed into an inferential system, which compares the most impacted value to a diagnosis. The result demonstrates that the suggested model has successfully dealt with vague information. For practical implementation, a single-value neutrosophic number has been used; it is a special case of the neutrosophic set. To highlight the promising potential of the suggested technique, an experimental investigation of the well-known Pima Indian diabetes dataset is presented. The results of our trials show that the proposed technique attained a high degree of accuracy and produced a generic model capable of effectively classifying previously unknown data. It can also surpass some of the most advanced classification algorithms based on machine learning and fuzzy systems.

Addressing scheduling problems with the best graph coloring algorithm has always been very challenging. However, the university timetable scheduling problem can be formulated as a graph coloring problem where courses are represented as vertices and the presence of common students or teachers of the corresponding courses can be represented as edges. After that, the problem stands to color the vertices with lowest possible colors. In order to accomplish this task, the paper presents a comparative study of the use of graph coloring in university timetable scheduling, where five graph coloring algorithms were used: First Fit, Welsh Powell, Largest Degree Ordering, Incidence Degree Ordering, and DSATUR. We have taken the Military Institute of Science and Technology, Bangladesh as a test case. The results show that the Welsh-Powell algorithm and the DSATUR algorithm are the most effective in generating optimal schedules. The study also provides insights into the limitations and advantages of using graph coloring in timetable scheduling and suggests directions for future research with the use of these algorithms.

Timing-critical path analysis is one of the most significant terms for the VLSI designer. For the formal verification of any kinds of digital chip, static timing analysis (STA) plays a vital role to check the potentiality and viability of the design procedures. This indicates the timing status between setup and holding times required with respect to the active edge of the clock. STA can also be used to identify time sensitive paths, simulate path delays, and assess Register transfer level (RTL) dependability. Four types of Static Random Access Memory (SRAM) controllers in this paper are used to handle with the complexities of digital circuit timing analysis at the logic level. Different STA parameters such as slack, clock skew, data latency, and multiple clock frequencies are investigated here in their node-to-node path analysis for diverse SRAM controllers. Using phase lock loop (ALTPLL), single clock and dual clock are used to get the response of these controllers. For four SRAM controllers, the timing analysis shows that no data violation exists for single and dual clock with 50 MHz and 100 MHz frequencies. Result also shows that the slack for 100MHz is greater than that of 50MHz. Moreover, the clock skew value in our proposed design is lower than in the other three controllers because number of paths, number of states are reduced, and the slack value is higher than in 1st and 2nd controllers. In timing path analysis, slack time determines that the design is working at the desired frequency. Although 100MHz is faster than 50MHz, our proposed SRAM controller meets the timing requirements for 100MHz including the reduction of node to node data delay. Due to this reason, the proposed controller performs well compared to others in terms slack and clock skew.

All electronic devices in our cutting-edge technology world must be networked together via the Internet if users want to have remote access to them. As a result, it may raise a variety of serious security issues. This study suggests a remote access home automation security system that incorporates utilizing the Internet of Things (IoT), and Artificial Intelligence (AI) for ensuring the security of the house. For a highly efficient security system, Face recognition has been used to maneuver the door access. In case of power outage or for any technical issues, an alternative security PIN has been added which is only accessible by the owner. Moreover, individuals are able to monitor and control the door access along with other attributes of the house using an application. In this work, Face detection is performed using the Haar Cascade classifier, while face recognition is performed using the Local Binary Pattern Histogram (LBPH). 95.7% accuracy in recognizing faces has been achieved after evaluating the proposed system.

Non-functional requirements define the quality attribute of a software application, which are necessary to identify in the early stage of software development life cycle. Researchers proposed automatic software Non-functional requirement classification using several Machine Learning (ML) algorithms with a combination of various vectorization techniques. However, using the best combination in Non-functional requirement classification still needs to be clarified. In this paper, we examined whether different combinations of feature extraction techniques and ML algorithms varied in the non-functional requirements classification performance. We also reported the best approach for classifying Non-functional requirements. We conducted the comparative analysis on a publicly available PROMISE_exp dataset containing labelled functional and Non-functional requirements. Initially, we normalized the textual requirements from the dataset; then extracted features through Bag of Words (BoW), Term Frequency and Inverse Document Frequency (TF-IDF), Hashing and Chi-Squared vectorization methods. Finally, we executed the 15 most popular ML algorithms to classify the requirements. The novelty of this work is the empirical analysis to find out the best combination of ML classifier with appropriate vectorization technique, which helps developers to detect Non-functional requirements early and take precise steps. We found that the linear support vector classifier and TF-IDF combination outperform any combinations with an F1-score of 81.5%.