Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Moreover, it is a critical determinant in the consumer's decision-making process when considering a smartphone purchase. From the sustainability perspective, it becomes imperative to explore approaches aimed at mitigating the energy consumption of mobile devices, given the significant global consequences arising from the extensive utilisation of billions of smartphones, which imparts a profound environmental impact. Despite the existence of various energy-efficient programming practices within the Android platform, the dominant mobile ecosystem, there remains a need for documented machine learning-based energy prediction algorithms tailored explicitly for mobile app development. Hence, the main objective of this research is to propose a novel neural network-based framework, enhanced by a metaheuristic approach, to achieve robust energy prediction in the context of mobile app development. The metaheuristic approach here plays a crucial role in not only identifying suitable learning algorithms and their corresponding parameters but also determining the optimal number of layers and neurons within each layer. To the best of our knowledge, prior studies have yet to employ any metaheuristic algorithm to address all these hyperparameters simultaneously. Moreover, due to limitations in accessing certain aspects of a mobile phone, there might be missing data in the data set, and the proposed framework can handle this. In addition, we conducted an optimal algorithm selection strategy, employing 13 metaheuristic algorithms, to identify the best algorithm based on accuracy and resistance to missing values. The comprehensive experiments demonstrate that our proposed approach yields significant outcomes for energy consumption prediction.

Energy consumption plays a vital role in mobile App development for developers and end-users, and it is considered one of the most crucial factors for purchasing a smartphone. In addition, in terms of sustainability, it is essential to find methods to reduce the energy consumption of mobile devices since the extensive use of billions of smartphones worldwide significantly impacts the environment. Despite the existence of several energy-efficient programming practices in Android, the leading mobile ecosystem, machine learning-based energy prediction algorithms for mobile App development have yet to be reported. Therefore, this paper proposes a histogram-based gradient boosting classification machine (HGBC), boosted by a metaheuristic approach, for energy prediction in mobile App development. Our metaheuristic approach is responsible for two issues. First, it finds redundant and irrelevant features without any noticeable change in performance. Second, it performs a hyper-parameter tuning for the HGBC algorithm. Since our proposed metaheuristic approach is algorithm-independent, we selected 12 algorithms for the search strategy to find the optimal search algorithm. Our finding shows that a success-history-based parameter adaption for differential evolution with linear population size (L-SHADE) offers the best performance. It can improve performance and decrease the number of features effectively. Our extensive set of experiments clearly shows that our proposed approach can provide significant results for energy consumption prediction.

Data is the new power in today's digital age. As industries and businesses are using mobile applications for various purposes, massive volumes of data are being produced. With such a huge amount of data, businesses need effective and smart ways for driving useful insights and further enhance their business growth. Companies are using data science in mobile app development for achieving higher results. And consequently, it is disrupting the app development industry.

Artificial intelligence and machine learning are two of the most groundbreaking technologies that have altered the present and future of outsourcing app development. With the advent of these two technologies, regular chores are not only intelligently automated but also customized to provide a specialized experience to the clients.

Artificial Intelligence has already changed every aspect of our life and almost every technology niche, leaving each month fewer areas to explore. Mobile development here is never an exception, and vice versa, it's among the top AI-influenced tech fields, as according to a recent report by Brusselstimes, smartphones are used by people more than 2,600 a day. So, how to use ML and AI in mobile app development so as not to disturb people from browsing and scrolling through media and social networks? Today we will reveal the top secrets of AI mobile app development based on AI mobile guide by InData Labs research and development specialists. Of course, there are far more than 5 possible uses of AI in mobile applications, but we recommend you concentrate on these ones, as they can really help you boost your KPIs, automate your workflow, and raise your customer satisfaction rate.

Artificial Intelligence (AI) technology is gradually taking the world in its arms. The capabilities and applications of this intelligent technology are incredible and can completely revolutionize the world. Nowadays, enterprises of all sizes are adopting AI technology. AI can optimize work efficiency, automate repetitive tasks, and also assist companies in providing virtual customer support services. AI-powered mobile applications are also one of the best innovations of technology that help businesses reach targeted audiences globally and generate sustainable revenues.

Artificial intelligence and Machine learning are contributing to building futuristic technologies. Smartphones are becoming smarter with the integration of artificial intelligence and machine learning. Mobile app development companies are exploring endless opportunities to integrate a high level of artificial intelligence and machine learning components to offer the best product to their clients. If you are someone who wants to know how to integrate AI and ML in mobile app development, this article is for you. Let's start by checking out the stats of AI and ML in mobile app development.

Artificial intelligence, along with deep learning systems, is one of the key driving forces in modern software and hardware development. Regardless of the industry or the sector, AI and machine learning applications are becoming virtually endless, as nowadays these technologies can help businesses achieve their micro and macro goals efficiently and effectively. When it comes to mobile app development and the features that modern apps bring to the consumers, AI and machine learning can greatly streamline the process while enhancing the in-app user experience. Let's put that into perspective and take a look at how these technologies are changing mobile app development, just in time for your next app development project in 2023. Here's what you need to know.

In recent years, artificial intelligence (AI) has been taking the tech world by storm, with more and more companies turning to this innovative technology to improve performance. In fact, by 2025, the global artificial intelligence market is set to reach $126 billion. The global mobile application sector is also experiencing great levels of success and is set to generate more than $613 billion in revenues by 2025. Hence, it comes as no surprise that AI and mobile applications have intertwined. Ultimately, the former can certainly enhance the latter.

AI is no longer just for companies like Amazon, Microsoft, Netflix, etc. Take, for example, Siri; the AI-powered virtual assistant is one of Apple's most popular AI applications. Cortana is another famous virtual assistant from Microsoft. These apps are among the best AI apps, with millions of users worldwide. But, AI in business and mobile applications have much more functionality than just virtual assistants and chatbots. Today, AI is available in the products and processes of most industries.