Effective treatment relies on a timely diagnosis, which is critical in the case of thyroid disorder—one of the chronic endocrine disorders alongside diabetes and obesity—with profound health concerns. Thyroid disorders occur due to the malfunctioning of the thyroid gland, which may result in an imbalanced metabolic rate due to inappropriate hormone levels synthesis. An overactive gland results in hyperthyroidism, whereas an underactive or sluggish thyroid lead to hypothyroidism. Both disorders, if not detected and managed timely, can lead to severe health complications. Early identification is crucial to delay or avoid debilitating complications and achieve a better quality of life through the right medical interventions and precise hormonal readjustments. The proposed hybrid algorithm method finds the best features for finding thyroid disease uses performance measures such as accuracy, F1-score, precision, and recall. The research demonstrates promising results with an accuracy of 98.91 % and an F1-score of 94.83, showcasing the robustness of the proposed algorithms on a benchmark dataset. The findings hold potential to improve clinical decision-making processes. This study advances medical diagnostics by combining machine learning algorithms with nature-inspired optimization techniques to detect thyroid illnesses in their early stages. • This article proposes a novel hybrid algorithm that combines the Cuttlefish Optimization Algorithm (CFA) and Simulated Annealing (SA) to find the best features for finding thyroid disease. • The study uses machine-learning models for classification. • The integration of machine learning and nature-inspired optimization significantly enhances the diagnostic capabilities of healthcare systems, enabling prompt diagnosis and treatment planning for thyroid disorders.

With the growing pressure to reduce environmental pollution, the combination of Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence (AI) offers a viable path forward for modernizing emission monitoring. This research investigates the revolutionary potential of UAV-based AI-powered data analysis for emissions monitoring, with a particular emphasis on the comparison of XGBoost and CatBoost algorithms. Real-time monitoring, identification, and analysis of pollutants can now be done with remarkable accuracy and efficiency thanks to UAVs outfitted with modern sensors and AI algorithms. This novel methodology has significant advantages over typical monitoring approaches, including increased spatial coverage, lower costs, and higher data quality. Furthermore, AI integration provides predictive analytics and anomaly identification, allowing for proactive environmental risk mitigation interventions. Despite its enormous promise, challenges such as regulatory frameworks, privacy concerns, and technological restrictions must be solved before widespread usage. Overall, the combination of UAVs and AI, as well as the comparison of XGBoost and CatBoost, hold enormous promise for revolutionizing emission monitoring by providing a scalable and versatile solution to protect environmental quality and human health.

This manuscript provides a comprehensive exploration of quantum computing and communication’s foundational elements, employing Qiskit—an open-source quantum computing framework. It delves into the simulation of fundamental quantum blocks and the intricacies of two-qubit entanglement, presenting findings through graphical analyses. The discussion extends to quantum logic gates, including X, Y, Z and CNOT. This study not only examines the construction and application of quantum circuits. The simulation with Qiskit programming presents the basic quantum circuits and its outcomes after measurement.

Today we live in an era where knowledge is the greatest asset. Knowledge is no doubt the most valuable and precious entity in this universe, and the story has become more interesting when we talk about the reusability of knowledge. This is considered that a fundamental source of extra benefit for businesses is knowledge and of course the reusability of the knowledge. Organizations that are willing to achieve sustainable, modest additional benefits must emphasize their knowledge and focus on the concept of reusability. The need to include the reuse and sharing of knowledge in the public or in the administration is not only the result of increasing competitive pressures but also the necessity for quality development and price drop for the knowledge environment. A three-dimensional model of knowledge management in information and communication technology might be one of the finest tools to analyze the flow of knowledge at the administrative level in the higher education organization in a country like India. The concept of reusability can be elaborated in terms of tacit and explicit knowledge in a management background when we are considering the data of higher educational institutions. This research aims to discuss key topics that should convince knowledge building to reuse knowledge in the exact arena of public administration.

The utilization of drones with their specific features, such as energy efficiency, dynamic structure, and mobility in Flying Ad-hoc Networks (FANET), has found extensive applications in various fields like disaster management, rescue operations, medical services, and military operations. However, the efficiency and effectiveness of FANET communication can be hindered by challenges related to complex and dynamic environments. The need for optimal path selection among drones is crucial to ensure proficient message transmission, energy efficiency, and secure communication. To address these challenges, an Improved Honey-Badger Optimization-Based Communication Approach (IHBO_CA) is proposed and implemented for optimal path selection among drones in FANET. IHBO_CA leverages a combination of a sinusoidal chaotic map and the honey-badger optimization algorithm to enhance the performance of communication within FANETs. The research findings indicate that IHBO_CA offers significant improvements in the efficiency and effectiveness of communication among drones in FANETs. This improvement is crucial in applications where reliable and energy-efficient communication is essential. The implementation of IHBO_CA using MATLAB 2021 showed promising results, highlighting its superiority over other communication protocols, such as OLSR, MP-OLSR, ACO, PSO, and HBA in terms of energy expenditure, overhead, time complexity, packet delivery ratio, and delay.

The power and competence of drones are exploited in numerous areas of applications like surveillance, healthcare, and disaster management in Flying Ad-hoc Network (FANET). The incongruous routing and incompetent flight period are major issues in drones due to minimum stability and a small amount of battery capability. Then, the communication is unproductive and ineffectual between drones, which decreases the performance of FANET by increasing the cost of message delivery. Therefore, a Chaotic Black Hole Optimization-based Routing (Chaotic BHOR) technique is implemented to improve the communication efficiency and security among drones by reducing the energy cost and enhancing the key contribution over FANET. The MATLAB 2021a tool is used to implement the Chaotic BHOR, and outcomes illustrate the superior effectiveness of Chaotic BHOR on the basis of end-to-end delay, packet delivery ratio, and throughput and power expenses against previous techniques like OLSR, MP-OLSR, and ML-OLSR-PMS.

FANET (flying ad-hoc network) has provided broad area for research and deployment due to efficient use of the capabilities of drones and UAVs (unmanned ariel vehicles) in several military and rescue applications. Drones have high mobility in 3D (3 dimensional) environment and low battery power, which produce various problems such as small journey time and infertile routing. The optimal routing for communication will assist to resolve these problems and provide the energy efficient and secure data transmission over FANET. Hence, in this paper, we proposed a whale optimization algorithm based optimized link state routing (WOA-OLSR) over FANET to provide optimal routing for energy efficient and secure FANET. The efficiency of OLSR is enhanced by using WOA and evaluated performance shows the better efficiency of WOA-OLSR in terms of some parameters such as a packet delivery ratio, end to end delay, energy utilization, throughput, and time complexity against the previous approaches OLSR, MP-OLSR, P-OLSR, ML-OLSR-FIFO and ML-OLSR-PMS.

Now-a-days ireless Body Area Network (WBAN) is considered to be new era technique in which patient’s health record are monitored remotely by using wearable sensors from anywhere in the world. In such high-level communication, there is need of security services are required to protect the data being used by healthcare professionals and patients from intruders or attackers. Therefore, many researchers are showing their keen interest for security enhancement of WBAN architecture for secure communication. In this dissertation work, different security and privacy techniques are reviewed and analysed WBAN/IoT challenges as well their limitations based on the latest standards and publications. This research also covers the state-of-art security measures and research in WBAN. This research presents an ElGamal cryptosystem and biometric information authentication scheme for WBAN/IOT applications. This work observed that most of the authentication protocols using hash function and ElGamal cryptosystem for cloud-based applications are affected by security attacks and are unable to hide the actual identities of the end users during login session. Therefore, this work has introduced a secure biometric ElGamal-based authentication as well as data sharing schemes. The result analysis shows that the proposed work is better with respect to existing work with respect to execution time and cost as well as security level.

Various routing convention for FANETs have been made in the consistent past years which are subject for data exchanging, way engenderment and crash avoiding in organize. In the event that the centers are extraordinary and with a rushed and randomized transportability, by then a probabilistic heuristic technique might be gotten a handle on. These issues ought to be confined by using suitable organizing between UAV through update approach, so a novel optimization approach for competent dynamic routing in FANET may be adopted by using the properties of UAV like interest, hugeness, cost and keys obligation of UAV communicating with one another.

Wireless Body Area Network (WBAN) is a new trend in the technology that provides remote mechanism to monitor and collect patient’s health record data using wearable sensors. It is widely recognized that a high level of system security and privacy play a key role in protecting these data when being used by the healthcare professionals and during storage to ensure that patient’s records are kept safe from intruder’s danger. It is therefore of great interest to discuss security and privacy issues in WBANs. In this paper, we reviewed WBAN communication architecture, security and privacy requirements and security threats and the primary challenges in WBANs to these systems based on the latest standards and publications. This paper also covers the state-of-art security measures and research in WBAN.

Cloud computing is an enormous area which shares huge amount of data over cloud services and it has been increasing with its on-demand technology. Since, with these versatile cloud services, when the delicate data stored within the cloud storage servers, there are some difficulties which has to be managed like its Security Issues, Data Privacy, Data Confidentiality, Data Sharing and its integrity over the cloud servers dynamically. Also, the authenticity and data access control should be maintained in this wide environment. Thus, Attribute based Encryption (ABE) is a significant version of cryptographic technique in the cloud computing environment. Data integrity, one of the most burning challenges in secure cloud storage. Data auditing protocols enable a verifier to efficiently check the integrity of the files without downloading the entire file from the cloud. In this paper cloud data integrity checking is performed by introducing attribute-based cloud data auditing where users can upload files to cloud through some set of attributes and specify auditor to check the integrity of data files. Existing protocols are mostly based on public key infrastructure or an exact identity, which lacks ?exibility of key management. In this research work Cloud data integrity checking is performed by introducing attribute-based cloud data auditing where users can upload files to cloud through some set of attributes and specify auditor to check the integrity of data files. Variable attributes are used to generate the private key and their performance is evaluated under variable attribute list.

SQL Injection Attack (SQLIA) is a technique that helps the attackers to direct enters into the database in an unauthorized way and reach the highest or most decisive point in extracting or updating sensitive information from any organizations database. In this paper, we studied the scenario of the different types of attacks with descriptions and examples of how attacks of that type could be performed and their detection & prevention schemes. It also contains strengths and weaknesses of various SQL injection attacks. It is known to all that SQL injection attacks easily prevented by applying more secure schemes in login phase and after login phase. Therefore, we implement our proposed scheme called SQLENCP, the SQL injection prevention by encryption & hashing techniques, to handle the SQLIA and prevent them. Although, the proposed implemented system is unable to handle all the SQL injection attacks, but it can prevent tautology attacks, union based query attacks & illegal structured query attacks.

The increased degree of connectivity and the increasing amount of data has led many providers and in particular data centers to employ larger infrastructures with dynamic load and access balancing. This lead to the demand of cloud computing. But there are some security concerns when we handle and share data in the cloud computing environment. In this paper we propose a new cloud computing environment where we approach a trusted cloud environment which is controlled by both the client and the cloud environment admin. Our approach is mainly divided into two parts. First part is controlled by the normal user which gets permission by the cloud environment for performing operation and for loading data. Second part shows a secure trusted computing for the cloud, if the admin of the cloud want to read and update the data then it take permission from the client environment. This provides a way to hide the data and normal user and can protect their data from the cloud provider. This provides a two way security protocol which helps both the cloud and the normal user. For the above concept we apply RSA and MD 5 algorithm. When the cloud user upload the data in the cloud environment, the data is uploaded in encrypted form using RSA algorithm and the cloud admin can decrypt using their own private key. For updating the data in the cloud environment admin request the user for a secure key. Cloud user sends a secure key with a message digest tag for updating data. If any outsiders perform a change in the key, the tag bit is also changed indicating the key is not secure and correct.