In cloud computing, task scheduling has become more and more important for maximizing resource allocation, cutting down on energy use, and enhancing system performance. However, traditional algorithms often fall short in dynamic environments, leading to performance inefficiencies. For cloud computing energy efficiency, this research suggests a hybrid task scheduling method that combines Transformer-based fine-tuning and Cuckoo Search (HCS) optimization. The integration of bio-inspired optimization (HCS) and Modified BERT-Based Transformer allows for dynamic task reallocation, enhancing both energy consumption and resource utilization while maintaining high Service Level Agreement (SLA) compliance. Fine-tuning with Transformer models enables adjustments to improve scheduling efficiency and minimize energy wastage. The results show that the proposed method reduced energy consumption to 0.430 J (from 0.440 J) while maintaining 100% SLA compliance and task completion rate. Resource utilization improved to 88.2%, with energy efficiency of 0.0043 J/task. This hybrid approach offers significant advantages over traditional methods, such as BENBO + Bi-LSTM (0.687 J), PSO-PGA (0.617 J), and GAECS (0.537 J), achieving a reduced energy consumption of 0.440 J for 15 PM with 20 VMs, making it an ideal solution for scalable cloud computing environments.

The rapid pace of industrialization and urbanization has led to severe degradation in air and water quality across the globe. Traditional environmental monitoring systems are often reactive, fragmented, and slow in responding to pollution incidents. This research presents a novel, integrated machine learning-driven framework that leverages real-time data from Internet of Things (IoT) sensors and remote sensing platforms to monitor, predict, and assess environmental pollution levels. The system utilizes a hybrid architecture combining spatial (satellite) and local (sensor-based) data sources to feed predictive models such as Random Forest, XGBoost, and LSTM for real-time assessment of air and water quality parameters, including PM2.5, PM10, NO₂, SO₂, pH, and turbidity. A cloud-based processing pipeline is employed to collect, preprocess, and analyze streaming data, while geospatial analysis is used to generate pollution heatmaps. Experimental evaluations conducted on multi-city datasets from the Central Pollution Control Board (CPCB), Sentinel-5P, and open-source IoT deployments demonstrate a prediction accuracy of over 92% and timely alerts for environmental threshold violations. The results confirm the potential of this hybrid approach in enabling proactive environmental management and policy-making through sustainable data-driven insights

The D2D(Device to Device communication) in (PAN) wireless personal area network the devices are directly communication with each without any intermediate devices or network devices like Router. They can directly communicate with devices and share the resources without Router. Even though the optimization of resource allocation between devices in WPANS is a challenging task to optimize the Network efficiency, Throughput, Scalability and Latency to balance the Quality of Service(QoS) in 5G communication network. In this proposed research the improved load-balanced resource optimization to maximize the communication between devices in a WPAN network. The main idea of implementation is the devices in the D2D of 5G network is not overloaded. The workload will be balanced to every device. It will be managed the balancing utilization of resources like power and bandwidth to improve performance of Device-to-Device 5G network. This mode reduces the cost and maximize the systems performance. The proposed method Improved Load-Based Resource Optimization(ILBROM) is showing better performance results for improve network parameters like Network efficiency, Throughput, Scalability, Latency and improve QoS when comparison with existing models are (TSRO)traffic-shifting-based resource optimization,(JRO)joint resource optimization, (MTO) Multirate throughput optimization, (LBROM ) load-based resource optimization model.

In Distributed Database System (DBS) and multitasking system, the occurrence of deadlocks is one of the most serious problems. If a site request for a resource that is already in the another site which is waiting for another resource then the scenario is called as distributed deadlock. Different distributed environments require a suitable deadlock detection algorithm to detect deadlocks. Different distributed environments needs to maintain their platforms by avoiding deadlocks. To achieve this environment, it is required to fed with optimized deadlock detection and avoidance algorithms. In this article, different deadlock detection algorithms that uses Wait For Graph and resolution algorithms to trace out deadlocks were discussed. An optimization technique is used for resolving deadlock in an efficient manner. A comparison between different deadlock detection algorithms based on different parameters like, delay time, message size, number of messages and whether the algorithm detects false deadlocks or not were performed. Based on the comparisons, a few deadlock detection algorithms were suggested for the distributed environment.