Current Issue

Vol 12, Issue 7 (July 2026) Computer Science

A Survey On Machine Learning And Deep Learning Models For Predicting Traffic Transit Time

The Transition Towards Intelligent Traffic Systems (ITS) Is Inevitable In Future. Accurate Travel Time Estimation Is Vital For The Effectiveness Of Modern Public Transportation Systems. It Plays A Central Role In Applications Such As Real-time Passen... The Transition Towards Intelligent Traffic Systems (ITS) Is Inevitable In Future. Accurate Travel Time Estimation Is Vital For The Effectiveness Of Modern Public Transportation Systems. It Plays A Central Role In Applications Such As Real-time Passenger Information, Transit Planning, And Traffic Management. With Increasing Urbanization And Demand For Efficient Mobility, Transit Agencies Are Turning To Data-driven Models To Improve Service Reliability. One Valuable Data Source Is The General Transit Feed Specification (GTFS), Which Standardizes Public Transportation Schedules And Associated Geographic Information. When Integrated With Statistical Modeling Techniques, GTFS Features Can Significantly Enhance The Precision Of Street-level Travel Time Estimations. This Paper Presents A Comprehensive Review Of Statistical Models For Forecasting Street Level Travel Time Employing GTFS Features, Along With Associated Challenges That The Sector Faces. Read more Read less

Authors: Vaishnavi Sambare | Prof. Pradeep Pal

Vol 12, Issue 7 (July 2026) Computer Science

A Comprehensive Review On Contemporary Machine Learning And Deep Learning Models For Forecasting Stock Market Trends

Machine Learning Is Transforming Stock Market Prediction By Leveraging Vast Datasets, Advanced Algorithms, And Computational Power To Provide More Accurate Forecasts. While Challenges Remain, Continuous Advancements In Artificial Intelligence And Dee... Machine Learning Is Transforming Stock Market Prediction By Leveraging Vast Datasets, Advanced Algorithms, And Computational Power To Provide More Accurate Forecasts. While Challenges Remain, Continuous Advancements In Artificial Intelligence And Deep Learning Are Improving Predictive Models, Making Them An Essential Tool For Traders And Investors. Stock Market Prediction Extremely Challenging Due To The Dependence Of Stock Prices On Several Financial, Socio-economic And Political Parameters Etc. For Real Life Applications Utilizing Stock Market Data, It Is Necessary To Predict Stock Market Data With Low Errors And High Accuracy. This Needs Design Of Appropriate Artificial Intelligence (AI) And Machine Learning (ML) Based Techniques Which Can Analyze Large And Complex Data Sets Pertaining To Stock Markets And Forecast Future Prices And Trends In Stock Prices With Relatively High Accuracy. This Paper Presents A Comprehensive Review On The Various Techniques Used In Recent Contemporary Papers For Stock Market Forecasting. Read more Read less

Authors: Srishti Mongre | Dr. Dayashankar Pandey

Vol 12, Issue 7 (July 2026) Cyber Security, Digital Forensic,deepfake ,Chatbot

Forensic Detection Of Toxic Chatbots, Deepfakes, And Automated Harmful Interactions: A State-of-the-Art Review

The Rapid Expansion Of Generative AI (GenAI) Technologies Has Enabled Unprecedented Synthesised Media And Sophisticated Automated Systems. The Existence Of Advanced Deepfake Technology, Generative Large Language Models (LLMs) That Can Produce Toxic C... The Rapid Expansion Of Generative AI (GenAI) Technologies Has Enabled Unprecedented Synthesised Media And Sophisticated Automated Systems. The Existence Of Advanced Deepfake Technology, Generative Large Language Models (LLMs) That Can Produce Toxic Content, And Automated AI Botnets Has Created Substantial New Obstacles In Digital Forensics. The Present Review Consolidates Recent Work (2024–2025) Concerning The Forensics Of Three Colliding AI Threats: Audio-visual Deepfakes, Toxic Or Weaponised Chatbots, And The Automated Facilitation Of Harm. The Review Reconstructs The Shift From Artefact Detection Systems Toward The Use Of Behavioural, Semantic, And Multimodal Forensics. The Emerging Methodologies, Such As Biological Signal Processing, Audiovisual Correlation, LLM Systematisation Fingerprinting, And Traffic Encryption Biometrics, As Well As The Comprehensive Integration Of These Methodologies, Reveal Vital Yet Unrefined Methodologies That Focus On The Challenges Of Adversarial Attacks, Laundering Via Paraphrasing, And Performance Drops On Real-world Data. The Review Articulates The Necessity Of Well-founded, Transparent, And Standardizable Forensic Systems That Are Meant To Work In Adversarial Conditions. Read more Read less

Authors: Dr.Kiranbhai R Dodiya, Miss Ankita Kumari, Sudha Shetty,Dr. Parvesh Sharma,Dr. Kapil Kumar

Vol 12, Issue 7 (July 2026) Civil Engineering

INFLUENCE OF NATURAL AGGREGATES REPLACEMENT WITH RECYCLED AGGREGATE ON STRENGTH AND SULPHATE RESISTANCE OF M50 GRADE CONCRETE

The Rapid Expansion Of Global Infrastructure Has Led To An Unprecedented Demand For Natural Construction Materials, Necessitating The Exploration Of Sustainable Alternatives. This Research Investigates The Feasibility Of Replacing Natural Coarse Aggr... The Rapid Expansion Of Global Infrastructure Has Led To An Unprecedented Demand For Natural Construction Materials, Necessitating The Exploration Of Sustainable Alternatives. This Research Investigates The Feasibility Of Replacing Natural Coarse Aggregates With Manually Crushed Recycled Concrete Aggregates (RCA) In High-strength M50 Grade Concrete. A Primary Challenge Addressed In This Study Is The Presence Of Adhered Mortar On The Surface Of Recycled Aggregates, Which Significantly Influences Concrete Performance. The Experimental Program Involved Characterizing The Physical Properties Of Recycled Aggregates, Specifically Quantifying The Average Adhered Mortar Content. A Comprehensive Mix Design For M50 Grade Concrete Was Developed, And An Optimum Replacement Ratio Was Determined. The Mechanical Performance Was Evaluated Through Compressive Strength Testing Of Both Natural And Recycled Aggregate Concrete Under Normal Water Curing Conditions. Furthermore, The Study Explores The Effect Of Recycled Aggregate Utilization On Durability By Assessing The Concrete's Resistance To Sulphate Attack. The Findings Provide Critical Insights Into The Viability Of Incorporating Recycled Aggregates Into High-performance Concrete, Offering A Pathway Toward More Sustainable Construction Practices By Mitigating The Reliance On Virgin Natural Resources. Read more Read less

Authors: K SURYAKALA | P D SASIKALA

Vol 12, Issue 7 (July 2026) Civil Engineering

STRENGTHENING OF HIGH-PERFORMANCE LIGHTWEIGHT CONCRETE USING CENOSPHERE AND DOLOMITE HYBRID FILLERS

The Construction Industry’s Rapid Expansion Is Driving An Unsustainable Increase In Global Cement Production, Currently Exceeding 1.6 Billion Tons Annually. This Demand Not Only Depletes Natural Resources But Also Results In Significant Environmental... The Construction Industry’s Rapid Expansion Is Driving An Unsustainable Increase In Global Cement Production, Currently Exceeding 1.6 Billion Tons Annually. This Demand Not Only Depletes Natural Resources But Also Results In Significant Environmental Degradation Due To The Emission Of Greenhouse Gases, Including CO2, SO2, And NOX. To Address These Concerns, This Study Explores The Utilization Of Industrial Byproducts Specifically Cenospheres And Dolomite Powder As Sustainable Additives In Concrete Manufacturing. Cenospheres, Lightweight Hollow Spheres Derived From Coal Combustion, Were Utilized To Reduce The Unit Weight Of The Concrete, While Dolomite Powder Was Introduced As A Mineral-rich Cementitious Material To Enhance Mechanical Performance. This Research Investigates The Mechanical And Physical Properties Of M-30 Grade Concrete Through A Series Of Partial Replacements. The Experimental Program Evaluates Varying Proportions Of Dolomite Powder (0, 10, 20 And 30% By Weight Of Cement) In Combination With Cenospheres (0, 2, 4, And 6% By Weight). The Study Provides A Detailed Experimental Analysis Of Compressive, Flexural, And Split Tensile Strength At 7, 14, And 28 Days Of Curing. Preliminary Results Aim To Determine The Optimal Synergy Between These Materials To Produce A Structural-grade Lightweight Concrete That Minimizes Environmental Impact Without Compromising Mechanical Integrity. This Research Contributes To The Development Of Circular-economy-based Construction Materials, Offering A Viable Pathway For Reducing The Carbon Footprint Of Structural Infrastructure. Read more Read less

Authors: D SRI SIVA KIRAN VARMA | K URMILA DEVI