Image encryption involves applying cryptographic approaches to convert the content of an image into illegible or encrypted format, reassuring that illegal users cannot simply interpret access actual visual details. Commonly employed models comprise symmetric key algorithms for data, necessitating a secret decryption. This study introduces new Chaotic Encryption Algorithm with Improved Bonobo Optimizer and DNA Coding (CIEAIBO-DNAC) enhanced security. The presented CIEAIBO-DNAC technique...

Microscopic imaging aids disease diagnosis by describing quantitative cell morphology and tissue size. However, the high spatial resolution of these images poses significant challenges for manual evaluation. This project proposes using computer-aided analysis methods to address challenges, enabling rapid precise clinical diagnosis, course analysis, prognostic prediction. research introduces advanced deep learning frameworks such as squeeze-and-excitation dilated dense convolution blocks...

The latest advancements in computer vision and deep learning (DL) techniques pave the way to design novel tools for detection monitoring of forest fires. In this view, paper presents an intelligent wild fire alarming system using (IWFFDA-DL) model. proposed IWFFDA-DL technique aims identify fires at earlier stages through integrated sensors. includes Integrated sensor (ISS) combining array sensors that acts as major input source helps forecast fire. Then, attention based convolution neural...

According to the International Diabetes Federation (IDF), roughly 33% of individuals affected by diabetes exhibit diagnoses encompassing diverse severity diabetic retinopathy. In year 2020, approximately 463 million adults within age bracket 20 79 were documented as sufferers on a global scale. Projections suggest rise 700 2045. Proposed automated retinopathy detection methods aim reduce ophthalmologist workload. The study presents Robust Fuzzy Local Information K-Means Clustering algorithm,...

The significant losses that banks and other financial organizations suffered due to new bank account (NBA) fraud are alarming as the number of online banking service users increases. inherent skewness rarity NBA instances have been a major challenge machine learning (ML) models happen when non-fraud outweigh instances, which leads ML overlook erroneously consider instances. Such errors can erode confidence trust customers. Existing studies patterns instead potential risk features while...

Early detection of Parkinson's Disease (PD) using the PD patients' voice changes would avoid intervention before identification physical symptoms. Various machine learning algorithms were developed to detect detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due subject overlap. To overcome issues, this proposed work apply graph long short term memory (GLSTM) model classify dynamic features patient speech signal. The has been further...

[Context and Motivation] Before eliciting gathering requirements for a software project, it is considered pivotal to know about concerned stakeholders. It becomes hard elicit the actual system without identifying relevant stakeholders, leading project failure. Despite paramount importance of stakeholder identification in requirement elicitation, has been given less attention engineering literature. [Method] For this purpose, we conducted thorough Systematic Literature Review (SLR) on (SI)...

In recent years, Blockchain technology has been highly valued and disruptive. Several researches have presented a merge between blockchain current application i.e. medical, supply chain, e-commerce. Although architecture does not standard yet, IBM, MS, AWS offer BaaS (Blockchain as Service). addition to the public chains Ethereum, NEO, Cardeno; there are some differences several ledgers in terms of development architecture. This paper introduces main factors that affect integration...

This article primarily focuses on the performance evaluation of a new methodology, imputation by feature importance (IBFI), to serve its imputed dataset in further regression scenarios when dealing with soil radon gas concentration (SRGC) time-series data. The data have been collected spanning over fourteen(14) months period, which included four seismic events, and used for experimentation. (IBFI) has experimented obtained results are found more efficient missing patterns investigated time...

Mobile edge computing (MEC) provides effective cloud services and functionality at the device, to improve quality of service (QoS) end users by offloading high computation tasks. Currently, introduction deep learning (DL) hardware technologies paves a method in detecting current traffic status, data offloading, cyberattacks MEC. This study introduces an artificial intelligence with metaheuristic based technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC incorporates...

In agriculture, rice plant disease diagnosis has become a challenging issue, and early identification of this can avoid huge loss incurred from less crop productivity. Some the recently-developed computer vision Deep Learning (DL) approaches be commonly employed in designing effective models for detection classification processes. With motivation, current research work devises an Efficient based Fusion Model Rice Plant Disease (EDLFM-RPD) classification. The aim proposed EDLFM-RPD technique...

Gastrointestinal (GI) cancer comprises esophageal, gastric, colon and rectal tumors. The diagnosis of GI often relies on medical imaging modalities namely magnetic resonance (MRI), histopathological slides, endoscopy, computed tomography (CT) scans. This provides particular details about the size, location, characteristics high death rate for patients shows that it is possible to increase analysis a more personalized therapy strategy which leads improved prognosis few side effects although...

The Internet of Things (IoT) is considered the next-gen connection network and ubiquitous since it based on Internet. Intrusion Detection System (IDS) determines intrusion performance terminal equipment IoT communication procedures from environments after taking equivalent defence measures identified behaviour. In this background, current study develops an Enhanced Metaheuristics with Machine Learning enabled Cyberattack Classification (EMML-CADC) model in environment. aim presented...

Due to the advanced developments of Internet and information technologies, a massive quantity electronic data in biomedical sector has been exponentially increased. To handle huge amount data, automated multi-document text summarization becomes an effective robust approach accessing increased technical medical literature through multiple source documents by retaining significantly informative data. So, acts as vital role alleviate issue precise updated information. This paper presents Deep...

In recent times, global cities have been transforming from traditional to sustainable smart cities. text sentiment analysis (SA), many people face critical issues namely urban traffic management, living quality, information security, energy usage, safety, etc. Artificial intelligence (AI)-based applications play important roles in dealing with these crucial challenges SA. such scenarios, the classification of COVID-19-related tweets for SA includes using natural language processing (NLP) and...

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, mapping, yield prediction. Food using RS images (RSI) is a significant application technology in agriculture. It involves use to identify and classify different types food crops grown specific area. This information can be valuable monitoring, estimation, land management. Meeting...

The present spreading out of the Internet Things (IoT) originated realization millions IoT devices connected to Internet. With increase allied devices, gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, control resolve QoS issues for communications. However, it becomes challenging systems tackle diverse multimedia‐enabled settings including healthcare, traffic videos, automation,...

The present spreading out of big data found the realization AI and machine learning. With rise learning, idea improving accuracy enhancing efficacy applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances context applications. existing architectures have various challenges, where privacy foremost challenge for vulnerable road users (VRUs). key reason failure control pedestrians flawed handling users. user are at risk...

As AI models are increasingly deployed in critical applications, ensuring the consistent performance of when exposed to unusual situations such as out-of-distribution (OOD) or perturbed data, is important. Therefore, this paper investigates uncertainty various deep neural networks, including ResNet-50, VGG16, DenseNet121, AlexNet, and GoogleNet, dealing with data. Our approach includes three experiments. First, we used pretrained classify OOD images generated via DALL-E assess their...

Abstract The increasing use of algorithms in predictive policing has raised concerns regarding the potential amplification societal biases. This study adopts a two-phase approach, encompassing systematic review and mitigation age-related biases policing. Our identifies variety fairness strategies existing literature, such as domain knowledge, likelihood function penalties, counterfactual reasoning, demographic segmentation, with primary focus on racial However, this also highlights...

In recent years, the rapid progress of Internet Things (IoT) solutions has offered an immense opportunity for collection and dissemination health records in a central data platform. Electrocardiogram (ECG), fast, easy, non-invasive method, is generally employed evaluation heart conditions that lead to ailments identification diseases. The deployment IoT devices arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, early recognition abnormal...

This study addressed algorithmic bias in predictive policing, focusing on the Chicago Police Department's Strategic Subject List (SSL) dataset. We specifically focused identifying and mitigating age-related biases, a notably underexplored area prior research. Our research introduced Conditional Score Recalibration as mitigation strategy alongside well-established Class Balancing technique. involved reassessing adjusting risk scores for individuals initially assigned moderately...