Diffusion models have achieved remarkable success in image generation, image super-resolution, and text-to-image synthesis. Despite their effectiveness, they face key challenges, notably long inference time and complex architectures that incur high computational costs. While various methods have been proposed to reduce inference steps and accelerate computation, the optimization of diffusion model architectures has received comparatively limited attention. To address this gap, we propose LDMOES (Lightweight Diffusion Models based on Multi-Objective Evolutionary Search), a framework that combines multi-objective evolutionary neural architecture search with knowledge distillation to design efficient UNet-based diffusion models. By adopting a modular search space, LDMOES effectively decouples architecture components for improved search efficiency. We validated our method on multiple datasets, including CIFAR-10, Tiny-ImageNet, CelebA-HQ [Formula: see text], and LSUN-church [Formula: see text]. Experiments show that LDMOES reduces multiply-accumulate operations (MACs) by approximately 40% in pixel space while outperforming the teacher model. When transferred to the larger-scale Tiny-ImageNet dataset, it still generates high-quality images with a competitive FID score of 4.16, demonstrating strong generalization ability. In latent space, MACs are reduced by about 50% with negligible performance loss. After transferring to the more complex LSUN-church dataset, the model surpasses baselines in generation quality while reducing computational cost by nearly 60%, validating the effectiveness and transferability of the multi-objective search strategy. Code and models will be available at https://github.com/GenerativeMind-arch/LDMOES .

Abstract Parkinson's disease (PD) arises from brain cell damage and necessitates early detection for effective treatment and symptom management. While various methods such as voice, speech, and written exams have been explored, utilizing automated tools is crucial to enhance accuracy. Recent advancements in artificial intelligence (AI) and deep learning (DL) provide an opportunity for precise early-stage PD identification. This study introduces a novel approach known as Quantum Mayfly Optimization-based feature subset selection with hybrid convolutional neural network (QMFOFS-HCNN) to improve PD detection and classification. QMFOFS-HCNN is designed to identify optimal feature subsets and overcome the dimensionality challenge. It combines a quantum mayfly optimization approach for feature selection with a convolutional neural network with attention-based long short-term memory for PD detection and classification. Additionally, hyperparameter selection is optimized using the Nadam optimizer. Experimental validation using benchmark datasets yielded compelling results. The QMFOFS-HCNN technique achieved accuracy rates: 96.35% for HandPD Spiral, 96.7% for HandPD Meander, 98.5% for Speech PD, and a perfect 100% for Voice PD datasets. These quantitative findings underscore the potential of AI and DL to enhance early PD detection accuracy significantly. These results offer promising prospects for improving healthcare outcomes in managing PD and related neurological disorders.

Abstract Presently, smart city is designed to enhance the quality of life in city, fulfil the safety of the people, safe travelling, etc. Besides, big data has attracted significant attention among researchers in different fields as a large amount of data is being produced with diverse day‐to‐day applications. Besides, Vehicular adhoc network (VANET) is a kind of mobile adhoc network (MANET) that considers the vehicles as the nodes in a network. Since the VANET generates large amount of data, big data analytics can be used to gain meaningful understanding for improving the traffic management process such as planning, engineering, and operations. This paper designs a Big Data Analytics with Oppositional Moth Flame Optimization based Vehicular Routing Protocol for Future Smart Cities. The presented model maps the features of VANET with the attributes of the big data. In addition, oppositional moth flame optimization based vehicular routing (OMFOVR) technique is developed for VANET over the Hadoop Map Reduce standalone distributed framework. For validating the effectual performance of the proposed OMFOVR technique, a series of experiments were performed and the results are compared with the conventional NetBeans IDE platform. The experimental values showcased the betterment of the OMFOVR technique on the selection of routes over the compared methods.

Abstract Automated examination of biomedical signals plays a vital role to diagnose diseases and offers useful data to several applications in the areas of physiology, sports medicine, and human–computer interface. The latest advancements in Artificial Intelligence (AI) have the ability to manage and analyse enormous biomedical datasets resulting in clinical decision making and real time applications. At the same time, Colorectal cancer (CRC) is the third most deadly disease affecting people over the globe. The utilization of AI techniques for the earlier identification of CRC has gained significant interest among the research communities. Therefore, this paper presents a novel AI based fusion model for CRC disease diagnosis and classification, named AIFM‐CRC. The presented AIFM‐CRC model primarily undergoes Gaussian filtering based noise removal and contrast enhancement as a preprocessing stage. In addition, a fusion based feature extraction process takes place where the SIFT based handcrafted features and Inception v4 based deep features are fused together. Besides, whale optimization algorithm tuned deep support vector machine model is employed as a classification technique to determine the existence of CRC. In order to highlight the proficient results analysis of the AIFM‐CRC model, a comprehensive simulation analysis takes place. The resultant experimental values pointed out the betterment of the AIFM‐CRC model by accomplishing a maximum accuracy of 96.18%.

The exponential upward push in breast cancer cases across the globe has alarmed academia-industries to obtain certain more effect and strong Breast cancer laptop Aided prognosis (BC-CAD) device for breast most cancers detection. Some of techniques have been evolved with focus on case centric segmentation, feature extraction and class of breast cancer Histopathological photos. However, rising complexity and accuracy regularly demands more sturdy answer. Recently, Convolutional Neural community (CNN) has emerged as one of the maximum efferent techniques for medical records evaluation and diverse picture classification issues. On this paper, a notably strong and green BC-CAD solution has been proposed. Our proposed gadget consists of pre-processing, more suitable adaptive learning based totally Gaussian aggregate model (GMM), connected element analysis based vicinity of interest localization, and AlexNet-DNN primarily based characteristic extraction. The precept factor analysis (PCA) and Linear Discriminant analysis (LDA) primarily based on characteristic selection that's used as dimensional discount. One of the blessings of the proposed method is that not one of the current dimensional reduction algorithms hired with SVM to perform breast most cancers detection and class. The overall results acquired signify that the AlexNet-DNN based capabilities at completely connected layer; FC6 together with LDA dimensional discount and SVM-based totally classification outperforms other country-of-artwork techniques for breast cancer detection. The proposed method completed 96.20 for AlexNet-FC6 and 96.70 for AlexNet-FC7 in term of assessment measures.

In this paper, we introduce a framework for knowledge reuse and deduction in mechanical products design and development. The proposed system effectively exploits the capitalized and inferred knowledge. To this end, we settled up an ontology dealing with the design process of mechanical products such as “the car”. The ontology-based framework is supported by a software tool that brings an automatic and personalized assistance to correspondent actors using the deduction process. Indeed, the systems provides the relevant knowledge to the suitable users in order to facilitate their professional tasks considering their roles and collaboration. Experimental results have demonstrated the effectiveness of reusing knowledge during product development lifecycle.

The fingerprint is commonly used biometric method for person identification. It is the most conventional and widely used technique in forensics and criminalities. Identification of the person's age and gender based on his/her fingerprint is an important step in overall person's identification. The aim of this research paper is to propose a gender classification technique based on fingerprint characteristics of individuals using discrete cosine transform (DCT). Gender classification evaluated using dimensionality reduction techniques such as Principal Component Analysis (PCA), along with Support Vector Machine (SVM). A dataset of 2600 persons of different ages and sex was collected as internal database. Of the samples tested, 1250 samples of 1375 exactly identified male samples and 1085 samples of 1225 exactly identified female samples.