The use of Internet of things (IoT)-based physical sensors to perceive the environment is a prevalent and global approach. However, one major problem is the reliability of physical sensors’ nodes, which creates difficulty in a real-time system to identify whether the physical sensor is transmitting correct values or malfunctioning due to external disturbances affecting the system, such as noise. In this paper, the use of Long Short-Term Memory (LSTM)-based neural networks is proposed as an alternate approach to address this problem. The proposed solution is tested for a smart irrigation system, where a physical sensor is replaced by a neural sensor. The Smart Irrigation System (SIS) contains several physical sensors, which transmit temperature, humidity, and soil moisture data to calculate the transpiration in a particular field. The real-world values are taken from an agriculture field, located in a field of lemons near the Ghadap Sindh province of Pakistan. The LM35 sensor is used for temperature, DHT-22 for humidity, and we designed a customized sensor in our lab for the acquisition of moisture values. The results of the experiment show that the proposed deep learning-based neural sensor predicts the real-time values with high accuracy, especially the temperature values. The humidity and moisture values are also in an acceptable range. Our results highlight the possibility of using a neural network, referred to as a neural sensor here, to complement the functioning of a physical sensor deployed in an agriculture field in order to make smart irrigation systems more reliable.

This study proposes an innovative approach to decision-making under uncertainty, using Pakistan’s urban transportation issues—particularly in cities like Karachi, Lahore, and Islamabad—as a case study. These cities face severe traffic congestion, demanding more effective strategies for infrastructure planning. We introduce a Linear Diophantine Fuzzy Z-Numbers (LDFZN) framework that captures membership and non-membership grades alongside the degree of reliability, addressing key limitations of traditional fuzzy systems by simultaneously considering uncertainty and confidence. Within this framework, we develop three decision-making methods: an LDFZN Dombi Weighted Averaging operator that aggregates expert opinions while accounting for their reliability; an adapted VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method for multi-criteria compromise solutions under LDFZN settings; and a modified Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) tailored for LDFZN-based scenarios. These tools are applied to real-world transportation challenges in Pakistan, demonstrating their effectiveness in managing uncertainty and expert-based confidence levels. The results outperform conventional models in decision robustness and clarity under uncertain conditions. This work contributes significantly to the theoretical and practical advancement of fuzzy mathematics, extending uncertainty modeling and providing practical solutions not only for transportation but also for various fields requiring informed decision-making with imprecise and unreliable data.

Fuzzy topological indices are getting attention these days due to their vast applications in daily life. In crisp case, topological indices are beneficial in chemical graph theory but as far as fuzzy graph theory is concerned, fuzzy topological indices are useful in identifying human trafficking, and multi-criteria decision-making environments. In this paper, we have computed the fuzzy topological indices such as the first and second fuzzy Zagreb indices, Randic index, and Harmonic index for the $ Pz_{n} $ pizza graph. We have found generalized results for the above-mentioned structure.

Fuzzy graph theory was invented by Rosenfeld. It is the extension of the work of L.A. Zadeh on fuzzy sets. Rosenfeld extracted the fuzzy-related concepts using the graph theoretic concepts. Topological indices for crisp theory have already been discussed in the literature but these days, topological index-related fuzzy graphs are much popular. Fuzzy graphs are being used as an application in different fields of sciences such as broadcast, communications, producing, social network, man-made reasoning, data hypothesis, and neural systems. In this paper, we have computed some fuzzy topological indices such as first and second Zagreb indices, Randic index, and harmonic index of fuzzy chemical graph named phenylene.

In practise, intuitionistic fuzzy numbers (IFNs) are particularly useful for describing ambiguous data. We look at multicriteria decision-making (MCDM) problems with a prioritising relationship between the parameters. The concept of a priority degree is presented. The aggregation operators (AOs) are formed by assigning nonnegative real numbers to stringent priority levels, known as priority degrees. As a result, we construct “intuitionistic fuzzy prioritized averaging operator with priority degrees” and “intuitionistic fuzzy prioritized geometric operator with priority degrees,” which are both prioritized operators. The attributes of the existing method are frequently compared to those of other current approaches, stressing the superiority of the provided work over other methods now in use. In addition, the impact of priority degrees on the overall result is thoroughly investigated. Furthermore, in the intuitionistic fuzzy set (IFS) context, a decision-making strategy is proposed based on these operators. To highlight the efficacy of the proposed approach, an illustrative example relating to the selection of the best choice is considered.

In this research, we introduce a novel approach to multi-attribute decision making by defining the Linear Diophantine Fuzzy Z-number set and generalizing the Bonferroni mean operator to this framework. Fuzzy Z-numbers are known for their ability to handle imprecise information by considering both a restriction (fuzzy number) and a reliability (degree of confidence). By extending these to linear Diophantine fuzzy sets, we incorporate more complex relationships between attributes, allowing for a deeper exploration of decision scenarios that involve uncertainty and interdependencies. The proposed generalized Bonferroni mean operator captures interactions between multiple attributes, providing a more robust aggregation mechanism in decision-making processes. We have applied this methodology to a real-world problem in textile engineering, selecting the best fabrics for designing new cricket uniforms. Textile selection involves multiple conflicting criteria such as durability, comfort, moisture absorption, and aesthetics. Our method handles these complexities efficiently to allow decision-makers to compare fabrics in an uncertain environment while taking trade-offs between different criteria into account. It can potentially lead to skills applications that match a variety of manufacturing industries, where one of the key factors for function and competitiveness is the efficient use of materials. The designed framework is capable of improving decision-making and providing a more sophisticated framework for evaluation of alternatives under uncertainty which has implications in both fuzzy logic theory and textile engineering fields.