Honey bees are one of the treasures in the world. An increase of waveform communication leads to good information exchange of mankind. In the biological view, it causes a lot of side effects and lifestyle changes in other living organisms. The drastic changes are causing the natural imbalance in the ecosystem and become a global issue. There are significant reasons for bee colony collapse disorder (CCD) like pesticides, disease and climate change. Recent studies reveal that a cell phone tower and mobile phone handset are also causing side effects to honey bees due to radiation emission. Most of the researchers concentrated on biological and behavioral changes in a honey bee due to radiation effects. For that, the real-time radiation levels have experimented but the different technical perspectives such as radiation emission levels, handset radiation emission measures and multi-sources of radiation are needed to be considered during research. This study aimed to provide possible research extensions of colony collapse disordercaused by cell tower and mobile handsets.

Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures.

Cloud computing plays a significant role in Information Technology (IT) industry to deliver scalable resources as a service. One of the most important factor to increase the performance of the cloud server is maximizing the resource utilization in task scheduling. The main advantage of this scheduling is to maximize the performance and minimize the time loss. Various researchers examined numerous scheduling methods to achieve Quality of Service (QoS) and to reduce execution time. However, it had disadvantages in terms of low throughput and high response time. Hence, this study aimed to schedule the task efficiently and to eliminate the faults in scheduling the tasks to the Virtual Machines (VMs). For this purpose, the research proposed novel Particle Swarm Optimization-Bandwidth Aware divisible Task (PSO-BATS) scheduling with Multi-Layered Regression Host Employment (MLRHE) to sort out the issues of task scheduling and ease the scheduling operation by load balancing. The proposed efficient scheduling provides benefits to both cloud users and servers. The performance evaluation is undertaken with respect to cost, Performance Improvement Rate (PIR) and makespan which revealed the efficiency of the proposed method. Additionally, comparative analysis is undertaken which confirmed the performance of the introduced system than conventional system for scheduling tasks with high flexibility.

Electronic health records provide details about the patient's medications and medical history records. Health information draws attackers' attention since it holds important records. The delivery of the wrong medication or operation is the outcome of the loss of electronic health records. Less security measures are provided by healthcare systems for patient safety information. With the support of particular hospitals, traditional digital health records (EHRs) manage medical information one patient record at a time, which leads to the uncomfortable exchange of records. Cloud-based EHRs are able to share information more easily than traditional EHRs. For cloud-based EHRs, however, a cloud service centre and key generation centre present a specific problem. The proposed effort focuses on developing a new EHR paradigm that can address the centralized issue with cloud-based EHRs. Applying emerging block chain technologies to EHRs is the solution (denoted as block chain-based EHRs for convenience). First, in a block chain scenario, specify the system paradigm of block chain-based EHRs. Additionally, the authentication problem can be crucial for EHRs. On the other hand, the present authentication procedures for block chain-based EHRs have security issues of their own. Additionally, a suggestion for a block chain-based EHR authentication technique is presented here. Our remedy is a collusion-resistant role-based signature system with many signatories that can fend off an attack. Additionally, the suggested method is presumably secure and offers more effective signature and verification processes than current authentication systems in the paradigm of random oracles. The recommended study also focuses on how patients file insurance claims. It helps people get insurance from the authorized insurance sector.

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